Uneingeschränkter Zugang

Deep Learning Models for Biometric Recognition based on Face, Finger vein, Fingerprint, and Iris: A Survey

, ,  und   
15. Juni 2024

Zitieren
COVER HERUNTERLADEN

A S. Dargan, and M. Kumar, “A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities, Exp. Sys. with Appl., vol. 143, pp. 113114, Apr. 2020. DarganA S. KumarM. “A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities Exp. Sys. with Appl. 143 113114 Apr. 2020 Search in Google Scholar

AK. Jain, and A. Kumar, “Biometric recognition: an overview”, 2ed, gene. Bio.: The ethical, legal and social context, pp. 49–79, 2012. JainAK. KumarA. “Biometric recognition: an overview” 2ed, gene. Bio.: The ethical, legal and social context 49 79 2012 Search in Google Scholar

T. Sabhanayagam, V.P. Venkatesan, and K. Senthamaraikannan, “A comprehensive survey on various biometric systems”, Int. Jou. of Applied Eng. Res., vol. 13, no.5, pp. 2276–2297, 2018. SabhanayagamT. VenkatesanV.P. SenthamaraikannanK. “A comprehensive survey on various biometric systems” Int. Jou. of Applied Eng. Res. 13 5 2276 2297 2018 Search in Google Scholar

K. Sundararajan and D. L. Woodard, “Deep Learning for Biometrics,” ACM Computing Surveys, vol. 51, no. 3, pp. 1–34, May 2018. SundararajanK. WoodardD. L. “Deep Learning for Biometrics,” ACM Computing Surveys 51 3 1 34 May 2018 Search in Google Scholar

K. W. Bowyer, K. Chang, and P. Flynn, “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Computer Vision and Image Understanding, vol. 101, no. 1, pp. 1–15, Jan. 2006. BowyerK. W. ChangK. FlynnP. “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition,” Computer Vision and Image Understanding 101 1 1 15 Jan. 2006 Search in Google Scholar

A. I. Awad and A. E. Hassanien, “Impact of some biometric modalities on forensic science,” Studies in Computational Intelligence, pp. 47–62, 2014. AwadA. I. HassanienA. E. “Impact of some biometric modalities on forensic science,” Studies in Computational Intelligence 47 62 2014 Search in Google Scholar

A. Ross and A. K. Jain, “Human recognition using biometrics: an overview,” Annales Des Télécommunications, vol. 62, no. 1–2, pp. 11–35, Jan. 2007. RossA. JainA. K. “Human recognition using biometrics: an overview,” Annales Des Télécommunications 62 1–2 11 35 Jan. 2007 Search in Google Scholar

Abd Al-Latief, Shahad Thamear, Salman Yussof, Azhana Ahmad, Saif Mohanad Khadim, and Raed Abdulkareem Abdulhasan. “Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning.” International Journal of Networked and Distributed Computing (2024): 1–18.. Al-LatiefAbd ThamearShahad YussofSalman AhmadAzhana KhadimSaif Mohanad AbdulhasanRaed Abdulkareem “Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning.” International Journal of Networked and Distributed Computing 2024 1 18 Search in Google Scholar

J. Wayman, Anil Jain, D. Maio, and Davide Maltoni, Biometric Systems Technology, Design and Performance Evaluation. London Springer-Verlag London Limited, 2005. WaymanJ. JainAnil MaioD. MaltoniDavide Biometric Systems Technology, Design and Performance Evaluation London Springer-Verlag London Limited 2005 Search in Google Scholar

A. K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4–20, Jan. 2004. JainA. K. RossA. PrabhakarS. “An Introduction to Biometric Recognition,” IEEE Transactions on Circuits and Systems for Video Technology 14 1 4 20 Jan. 2004 Search in Google Scholar

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, Jan. 2015, doi: 10.1016/j.neunet.2014.09.003. SchmidhuberJ. “Deep learning in neural networks: An overview,” Neural Networks 61 85 117 Jan. 2015 10.1016/j.neunet.2014.09.003 Open DOISearch in Google Scholar

S. M. Almabdy and L. A. Elrefaei, “An overview of deep learning techniques for biometric systems,” Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications, pp. 127–170, 2020. AlmabdyS. M. ElrefaeiL. A. “An overview of deep learning techniques for biometric systems,” Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications 127 170 2020 Search in Google Scholar

Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539 LeCunY. BengioY. HintonG. “Deep Learning,” Nature 521 7553 436 444 May 2015 10.1038/nature14539 Open DOISearch in Google Scholar

A. I. Georgevici and M. Terblanche, “Neural networks and deep learning: a brief introduction.” Intensive Care Medicine, vol. 45, no. 5, pp. 712–714, 2019, doi: 10.1007/s00134-019-05537-w. GeorgeviciA. I. TerblancheM. “Neural networks and deep learning: a brief introduction.” Intensive Care Medicine 45 5 712 714 2019 10.1007/s00134-019-05537-w Open DOISearch in Google Scholar

D. Rong, L. Xie, and Y. Ying, “Computer vision detection of foreign objects in walnuts using deep learning,” Computers and Electronics in Agriculture, vol. 162, pp. 1001–1010, Jul. 2019, doi: 10.1016/j.compag.2019.05.019. RongD. XieL. YingY. “Computer vision detection of foreign objects in walnuts using deep learning,” Computers and Electronics in Agriculture 162 1001 1010 Jul. 2019 10.1016/j.compag.2019.05.019 Open DOISearch in Google Scholar

Abdulhasan, Raed Abdulkareem, Shahad Thamear Abd Al-latief, and Saif Mohanad Kadhim. “Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system.” Multimedia Tools and Applications 83, no. 11 (2024): 32099–32122. AbdulhasanRaed Abdulkareem Abd Al-latiefShahad Thamear KadhimSaif Mohanad “Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system.” Multimedia Tools and Applications 83 11 2024 32099 32122 Search in Google Scholar

Minaee, Shervin, A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang, “Biometrics recognition using deep learning: A survey,” arXiv preprint arXiv:1912.00271, 2019. MinaeeShervin AbdolrashidiA. SuH. BennamounM. ZhangD. “Biometrics recognition using deep learning: A survey,” arXiv preprint arXiv:1912.00271, 2019 Search in Google Scholar

Y. Yu, X. Si, C. Hu, and J. Zhang, “A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures,” Neural Computation, vol. 31, no. 7, pp. 1235–1270, Jul. 2019, doi: 10.1162/neco_a_01199. YuY. SiX. HuC. ZhangJ. “A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures,” Neural Computation 31 7 1235 1270 Jul. 2019 10.1162/neco_a_01199 Open DOISearch in Google Scholar

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997. HochreiterS. SchmidhuberJ. “Long Short-Term Memory,” Neural Computation 9 8 1735 1780 Nov. 1997 Search in Google Scholar

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” arXiv.org, 2014. https://arxiv.org/abs/1412.3555 ChungJ. GulcehreC. ChoK. BengioY. “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” arXiv.org, 2014 https://arxiv.org/abs/1412.3555 Search in Google Scholar

R. Dey and F. M. Salem, “Gate-variants of Gated Recurrent Unit (GRU) neural networks,” 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Aug. 2017, doi: https://doi.org/10.1109/mwscas.2017.8053243. DeyR. SalemF. M. “Gate-variants of Gated Recurrent Unit (GRU) neural networks,” 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS) Aug. 2017 doi: https://doi.org/10.1109/mwscas.2017.8053243. Search in Google Scholar

D. P. Kingma and M. Welling, “An Introduction to Variational Autoencoders,” Foundations and Trends® in Machine Learning, vol. 12, no. 4, pp. 307–392, 2019, doi: 10.1561/2200000056. KingmaD. P. WellingM. “An Introduction to Variational Autoencoders,” Foundations and Trends® in Machine Learning 12 4 307 392 2019 10.1561/2200000056 Open DOISearch in Google Scholar

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, Jul. 2006, doi: 10.1162/neco.2006.18.7.1527. HintonG. E. OsinderoS. TehY.-W. “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation 18 7 1527 1554 Jul. 2006 10.1162/neco.2006.18.7.1527 Open DOISearch in Google Scholar

W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11–26, Apr. 2017, doi: 10.1016/j.neucom.2016.12.038. LiuW. WangZ. LiuX. ZengN. LiuY. AlsaadiF. E. “A survey of deep neural network architectures and their applications,” Neurocomputing 234 11 26 Apr. 2017 10.1016/j.neucom.2016.12.038 Open DOISearch in Google Scholar

I. Goodfellow et al., “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, Oct. 2020, doi: 10.1145/3422622. GoodfellowI. “Generative adversarial networks,” Communications of the ACM 63 11 139 144 Oct. 2020 10.1145/3422622 Open DOISearch in Google Scholar

Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–21, 2021, doi: https://doi.org/10.1109/tnnls.2021.3084827. LiZ. LiuF. YangW. PengS. ZhouJ. “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Transactions on Neural Networks and Learning Systems 1 21 2021 https://doi.org/10.1109/tnnls.2021.3084827. Search in Google Scholar

X. Zhang, F. Chen, and R. Huang, “A Combination of RNN and CNN for Attention-based Relation Classification,” Procedia Computer Science, vol. 131, pp. 911–917, 2018, doi: https://doi.org/10.1016/j.procs.2018.04.221. ZhangX. ChenF. HuangR. “A Combination of RNN and CNN for Attention-based Relation Classification,” Procedia Computer Science 131 911 917 2018 doi: https://doi.org/10.1016/j.procs.2018.04.221. Search in Google Scholar

M. Ma and Z. Mao, “Deep Convolution-based LSTM Network for Remaining Useful Life Prediction,” IEEE Transactions on Industrial Informatics, pp. 1–1, 2020, doi: https://doi.org/10.1109/tii.2020.2991796. MaM. MaoZ. “Deep Convolution-based LSTM Network for Remaining Useful Life Prediction,” IEEE Transactions on Industrial Informatics 1 1 2020 https://doi.org/10.1109/tii.2020.2991796. Search in Google Scholar

M. Z. Alom et al., “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” Electronics, vol. 8, no. 3, p. 292, Mar. 2019, doi: https://doi.org/10.3390/electronics8030292. AlomM. Z. “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” Electronics 8 3 292 Mar. 2019 doi: https://doi.org/10.3390/electronics8030292. Search in Google Scholar

A. M. Karim, H. Kaya, M. S. Güzel, M. R. Tolun, F. V. Çelebi, and A. Mishra, “A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification,” Sensors, vol. 20, no. 21, p. 6378, Nov. 2020, doi: https://doi.org/10.3390/s20216378. KarimA. M. KayaH. GüzelM. S. TolunM. R. ÇelebiF. V. MishraA. “A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification,” Sensors 20 21 6378 Nov. 2020 doi: https://doi.org/10.3390/s20216378. Search in Google Scholar

O. I. Abiodun et al., “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition,” IEEE Access, vol. 7, pp. 158820–158846, 2019, doi: https://doi.org/10.1109/access.2019.2945545. AbiodunO. I. “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition,” IEEE Access 7 158820 158846 2019 doi: https://doi.org/10.1109/access.2019.2945545. Search in Google Scholar

G. Kaur, G. Singh, and V. Kumar, “A Review on Biometric Recognition,” International Journal of Bio-Science and Bio-Technology, vol. 6, no. 4, pp. 69–76, Aug. 2014, doi: 10.14257/ijbsbt.2014.6.4.07. KaurG. SinghG. KumarV. “A Review on Biometric Recognition,” International Journal of Bio-Science and Bio-Technology 6 4 69 76 Aug. 2014 10.14257/ijbsbt.2014.6.4.07 Open DOISearch in Google Scholar

H. El Khiyari and H. Wechsler, “Face Recognition across Time Lapse Using Convolutional Neural Networks,” Journal of Information Security, vol. 07, no. 03, pp. 141–151, 2016. El KhiyariH. WechslerH. “Face Recognition across Time Lapse Using Convolutional Neural Networks,” Journal of Information Security 07 03 141 151 2016 Search in Google Scholar

L. Tian, C. Fan, and Y. Ming, “Multiple scales combined principle component analysis deep learning network for face recognition,” Journal of Electronic Imaging, vol. 25, no. 2, p. 023025, Apr. 2016. TianL. FanC. MingY. “Multiple scales combined principle component analysis deep learning network for face recognition,” Journal of Electronic Imaging 25 2 023025 Apr. 2016 Search in Google Scholar

C. Xiong, L. Liu, X. Zhao, S. Yan, and T.-K. Kim, “Convolutional Fusion Network for Face Verification in the Wild,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 3, pp. 517–528, Mar. 2016. XiongC. LiuL. ZhaoX. YanS. KimT.-K. “Convolutional Fusion Network for Face Verification in the Wild,” IEEE Transactions on Circuits and Systems for Video Technology 26 3 517 528 Mar. 2016 Search in Google Scholar

M. O. Simón et al., “Improved RGB-D-T based face recognition,” IET Biometrics, vol. 5, no. 4, pp. 297–303, Jun. 2016. SimónM. O. “Improved RGB-D-T based face recognition,” IET Biometrics 5 4 297 303 Jun. 2016 Search in Google Scholar

H. Li and C. Y. Suen, “Robust face recognition based on dynamic rank representation,” Pattern Recognition, vol. 60, pp. 13–24, Dec. 2016, doi: 10.1016/j.patcog.2016.05.014. LiH. SuenC. Y. “Robust face recognition based on dynamic rank representation,” Pattern Recognition 60 13 24 Dec. 2016 10.1016/j.patcog.2016.05.014 Open DOISearch in Google Scholar

R. Singh and H. Om, “Newborn face recognition using deep convolutional neural network,” Multimedia Tools and Applications, vol. 76, no. 18, pp. 19005–19015, Feb. 2017, doi: 10.1007/s11042-016-4342-x. SinghR. OmH. “Newborn face recognition using deep convolutional neural network,” Multimedia Tools and Applications 76 18 19005 19015 Feb. 2017 10.1007/s11042-016-4342-x Open DOISearch in Google Scholar

Y. Dong, Y. Liu, and S. Lian, “Automatic age estimation based on deep learning algorithm,” Neurocomputing, vol. 187, pp. 4–10, Apr. 2016, doi: 10.1016/j.neucom.2015.09.115. DongY. LiuY. LianS. “Automatic age estimation based on deep learning algorithm,” Neurocomputing 187 4 10 Apr. 2016 10.1016/j.neucom.2015.09.115 Open DOISearch in Google Scholar

M. Peng, C. Wang, T. Chen, and G. Liu, “NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification,” Information, vol. 7, no. 4, p. 61, Oct. 2016, doi: 10.3390/info7040061. PengM. WangC. ChenT. LiuG. “NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification,” Information 7 4 61 Oct. 2016 10.3390/info7040061 Open DOISearch in Google Scholar

X. Luo, R. Shen, J. Hu, J. Deng, L. Hu, and Q. Guan, “A Deep Convolution Neural Network Model for Vehicle Recognition and Face Recognition,” Procedia Computer Science, vol. 107, pp. 715–720, 2017, doi: 10.1016/j.procs.2017.03.153. LuoX. ShenR. HuJ. DengJ. HuL. GuanQ. “A Deep Convolution Neural Network Model for Vehicle Recognition and Face Recognition,” Procedia Computer Science 107 715 720 2017 10.1016/j.procs.2017.03.153 Open DOISearch in Google Scholar

K. Guo, S. Wu, and Y. Xu, “Face recognition using both visible light image and near-infrared image and a deep network,” CAAI Transactions on Intelligence Technology, vol. 2, no. 1, pp. 39–47, Mar. 2017. GuoK. WuS. XuY. “Face recognition using both visible light image and near-infrared image and a deep network,” CAAI Transactions on Intelligence Technology 2 1 39 47 Mar. 2017 Search in Google Scholar

J. Zhao, Y. Lv, Z. Zhou, and F. Cao, “A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network,” Neural Networks, vol. 94, pp. 115–124, Oct. 2017, doi: 10.1016/j.neunet.2017.06.013. ZhaoJ. LvY. ZhouZ. CaoF. “A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network,” Neural Networks 94 115 124 Oct. 2017 10.1016/j.neunet.2017.06.013 Open DOISearch in Google Scholar

S. Al-Waisy, R. Qahwaji, S. Ipson, and S. Al-Fahdawi, “A multimodal deep learning framework using local feature representations for face recognition,” Machine Vision and Applications, vol. 29, no. 1, pp. 35–54, Sep. 2017. Al-WaisyS. QahwajiR. IpsonS. Al-FahdawiS. “A multimodal deep learning framework using local feature representations for face recognition,” Machine Vision and Applications 29 1 35 54 Sep. 2017 Search in Google Scholar

Y. Ding, Y. Cheng, X. Cheng, B. Li, X. You, and X. Yuan, “Noise-resistant network: a deep-learning method for face recognition under noise,” EURASIP Journal on Image and Video Processing, vol. 2017, no. 1, Jun. 2017, doi: 10.1186/s13640-017-0188-z. DingY. ChengY. ChengX. LiB. YouX. YuanX. “Noise-resistant network: a deep-learning method for face recognition under noise,” EURASIP Journal on Image and Video Processing 2017 1 Jun. 2017 10.1186/s13640-017-0188-z Open DOISearch in Google Scholar

B. K. Tripathi, “On the complex domain deep machine learning for face recognition,” Applied Intelligence, vol. 47, no. 2, pp. 382–396, Mar. 2017, doi: 10.1007/s10489-017-0902-7. TripathiB. K. “On the complex domain deep machine learning for face recognition,” Applied Intelligence 47 2 382 396 Mar. 2017 10.1007/s10489-017-0902-7 Open DOISearch in Google Scholar

N. Zhuang, Y. Yan, S. Chen, H. Wang, and C. Shen, “Multi-label learning based deep transfer neural network for facial attribute classification,” Pattern Recognition, vol. 80, pp. 225–240, Aug. 2018, doi: 10.1016/j.patcog.2018.03.018. ZhuangN. YanY. ChenS. WangH. ShenC. “Multi-label learning based deep transfer neural network for facial attribute classification,” Pattern Recognition 80 225 240 Aug. 2018 10.1016/j.patcog.2018.03.018 Open DOISearch in Google Scholar

Y. Li, W. Zheng, Z. Cui, and T. Zhang, “Face recognition based on recurrent regression neural network,” Neurocomputing, vol. 297, pp. 50–58, Jul. 2018, doi: 10.1016/j.neucom.2018.02.037. LiY. ZhengW. CuiZ. ZhangT. “Face recognition based on recurrent regression neural network,” Neurocomputing 297 50 58 Jul. 2018 10.1016/j.neucom.2018.02.037 Open DOISearch in Google Scholar

X. Sun, P. Wu, and S. C. H. Hoi, “Face detection using deep learning: An improved faster RCNN approach,” Neurocomputing, vol. 299, pp. 42–50, Jul. 2018, doi: 10.1016/j.neucom.2018.03.030. SunX. WuP. HoiS. C. H. “Face detection using deep learning: An improved faster RCNN approach,” Neurocomputing 299 42 50 Jul. 2018 10.1016/j.neucom.2018.03.030 Open DOISearch in Google Scholar

A. Vinay, A. Gupta, A. Bharadwaj, A. Srinivasan, K. N. B. Murthy, and S. Natarajan, “Deep Learning on Binary Patterns for Face Recognition,” Procedia Computer Science, vol. 132, pp. 76–83, 2018, doi: 10.1016/j.procs.2018.05.164. VinayA. GuptaA. BharadwajA. SrinivasanA. MurthyK. N. B. NatarajanS. “Deep Learning on Binary Patterns for Face Recognition,” Procedia Computer Science 132 76 83 2018 10.1016/j.procs.2018.05.164 Open DOISearch in Google Scholar

K. Santoso and G. P. Kusuma, “Face Recognition Using Modified OpenFace,” Procedia Computer Science, vol. 135, pp. 510–517, 2018, doi: 10.1016/j.procs.2018.08.203. SantosoK. KusumaG. P. “Face Recognition Using Modified OpenFace,” Procedia Computer Science 135 510 517 2018 10.1016/j.procs.2018.08.203 Open DOISearch in Google Scholar

Y. Li, Z. Lu, J. Li, and Y. Deng, “Improving Deep Learning Feature with Facial Texture Feature for Face Recognition,” Wireless Personal Communications, vol. 103, no. 2, pp. 1195–1206, Feb. 2018, doi: 10.1007/s11277-018-5377-2. LiY. LuZ. LiJ. DengY. “Improving Deep Learning Feature with Facial Texture Feature for Face Recognition,” Wireless Personal Communications 103 2 1195 1206 Feb. 2018 10.1007/s11277-018-5377-2 Open DOISearch in Google Scholar

W. Liu, “Video Face Detection Based on Deep Learning,” Wireless Personal Communications, vol. 102, no. 4, pp. 2853–2868, Jan. 2018. LiuW. “Video Face Detection Based on Deep Learning,” Wireless Personal Communications 102 4 2853 2868 Jan. 2018 Search in Google Scholar

D. Luo, G. Wen, D. Li, Y. Hu, and E. Huan, “Deep-learning-based face detection using iterative bounding-box regression,” Multimedia Tools and Applications, vol. 77, no. 19, pp. 24663–24680, Feb. 2018. LuoD. WenG. LiD. HuY. HuanE. “Deep-learning-based face detection using iterative bounding-box regression,” Multimedia Tools and Applications 77 19 24663 24680 Feb. 2018 Search in Google Scholar

J. Kong, M. Chen, M. Jiang, J. Sun, and J. Hou, “Face Recognition Based on CSGF(2D)2PCANet,” IEEE Access, vol. 6, pp. 45153–45165, 2018, doi: 10.1109/access.2018.2865425. KongJ. ChenM. JiangM. SunJ. HouJ. “Face Recognition Based on CSGF(2D)2PCANet,” IEEE Access 6 45153 45165 2018 10.1109/access.2018.2865425 Open DOISearch in Google Scholar

M. Iqbal, M. S. I. Sameem, N. Naqvi, S. Kanwal, and Z. Ye, “A deep learning approach for face recognition based on angularly discriminative features,” Pattern Recognition Letters, vol. 128, pp. 414–419, Dec. 2019, doi: 10.1016/j.patrec.2019.10.002. IqbalM. SameemM. S. I. NaqviN. KanwalS. YeZ. “A deep learning approach for face recognition based on angularly discriminative features,” Pattern Recognition Letters 128 414 419 Dec. 2019 10.1016/j.patrec.2019.10.002 Open DOISearch in Google Scholar

M. Z. Khan, S. Harous, S. U. Hassan, M. U. Ghani Khan, R. Iqbal, and S. Mumtaz, “Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing,” IEEE Access, vol. 7, pp. 72622–72633, 2019, doi: 10.1109/access.2019.2918275. KhanM. Z. HarousS. HassanS. U. Ghani KhanM. U. IqbalR. MumtazS. “Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing,” IEEE Access 7 72622 72633 2019 10.1109/access.2019.2918275 Open DOISearch in Google Scholar

P. Görgel and A. Simsek, “Face recognition via Deep Stacked Denoising Sparse Autoencoders (DSDSA),” Applied Mathematics and Computation, vol. 355, pp. 325–342, Aug. 2019, doi: 10.1016/j.amc.2019.02.071. GörgelP. SimsekA. “Face recognition via Deep Stacked Denoising Sparse Autoencoders (DSDSA),” Applied Mathematics and Computation 355 325 342 Aug. 2019 10.1016/j.amc.2019.02.071 Open DOISearch in Google Scholar

C. Peng, N. Wang, J. Li, and X. Gao, “DLFace: Deep local descriptor for cross-modality face recognition,” Pattern Recognition, vol. 90, pp. 161–171, Jun. 2019, doi: 10.1016/j.patcog.2019.01.041. PengC. WangN. LiJ. GaoX. “DLFace: Deep local descriptor for cross-modality face recognition,” Pattern Recognition 90 161 171 Jun. 2019 10.1016/j.patcog.2019.01.041 Open DOISearch in Google Scholar

Y.-K. Li, “L1-2D2PCANet: a deep learning network for face recognition,” Journal of Electronic Imaging, vol. 28, no. 02, p. 1, Mar. 2019. LiY.-K. “L1-2D2PCANet: a deep learning network for face recognition,” Journal of Electronic Imaging 28 02 1 Mar. 2019 Search in Google Scholar

A. Elmahmudi and H. Ugail, “Deep face recognition using imperfect facial data,” Future Generation Computer Systems, vol. 99, pp. 213–225, Oct. 2019, doi: 10.1016/j.future.2019.04.025. ElmahmudiA. UgailH. “Deep face recognition using imperfect facial data,” Future Generation Computer Systems 99 213 225 Oct. 2019 10.1016/j.future.2019.04.025 Open DOISearch in Google Scholar

P. Wang, F. Su, Z. Zhao, Y. Guo, Y. Zhao, and B. Zhuang, “Deep Class-Skewed Learning for Face Recognition,” Neurocomputing, vol. 363, pp. 35–45, Jun. 2019, doi: 10.1016/j.neucom.2019.04.085. WangP. SuF. ZhaoZ. GuoY. ZhaoY. ZhuangB. “Deep Class-Skewed Learning for Face Recognition,” Neurocomputing 363 35 45 Jun. 2019 10.1016/j.neucom.2019.04.085 Open DOISearch in Google Scholar

R. Bendjillali, M. Beladgham, K. Merit, and A. Taleb-Ahmed, “Improved Facial Expression Recognition Based on DWT Feature for Deep CNN,” Electronics, vol. 8, no. 3, p. 324, Mar. 2019. BendjillaliR. BeladghamM. MeritK. Taleb-AhmedA. “Improved Facial Expression Recognition Based on DWT Feature for Deep CNN,” Electronics 8 3 324 Mar. 2019 Search in Google Scholar

Z. Pei, H. Xu, Y. Zhang, M. Guo, and Y.-H. Yang, “Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments,” Electronics, vol. 8, no. 10, p. 1088, Sep. 2019. PeiZ. XuH. ZhangY. GuoM. YangY.-H. “Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments,” Electronics 8 10 1088 Sep. 2019 Search in Google Scholar

J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” openaccess.thecvf.com, 2019. https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.html DengJ. GuoJ. XueN. ZafeiriouS. “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” openaccess.thecvf.com, 2019 https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.html Search in Google Scholar

T. Goel and R. Murugan, “Classifier for Face Recognition Based on Deep Convolutional - Optimized Kernel Extreme Learning Machine,” Computers & Electrical Engineering, vol. 85, p. 106640, Jul. 2020, doi: 10.1016/j.compeleceng.2020.106640. GoelT. MuruganR. “Classifier for Face Recognition Based on Deep Convolutional - Optimized Kernel Extreme Learning Machine,” Computers & Electrical Engineering 85 106640 Jul. 2020 10.1016/j.compeleceng.2020.106640 Open DOISearch in Google Scholar

S. Ruan et al., “Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene,” Applied Sciences, vol. 10, no. 13, p. 4669, Jul. 2020, doi: 10.3390/app10134669. RuanS. “Multi-Pose Face Recognition Based on Deep Learning in Unconstrained Scene,” Applied Sciences 10 13 4669 Jul. 2020 10.3390/app10134669 Open DOISearch in Google Scholar

M. Masud et al., “Deep learning-based intelligent face recognition in IoT-cloud environment,” Computer Communications, vol. 152, pp. 215–222, Feb. 2020, doi: 10.1016/j.comcom.2020.01.050. MasudM. “Deep learning-based intelligent face recognition in IoT-cloud environment,” Computer Communications 152 215 222 Feb. 2020 10.1016/j.comcom.2020.01.050 Open DOISearch in Google Scholar

X. Li, Z. Yang, and H. Wu, “Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 174922–174930, 2020, doi: 10.1109/access.2020.3023782. LiX. YangZ. WuH. “Face Detection Based on Receptive Field Enhanced Multi-Task Cascaded Convolutional Neural Networks,” IEEE Access 8 174922 174930 2020 10.1109/access.2020.3023782 Open DOISearch in Google Scholar

B. Ríos Sánchez, D. Costa-da Silva, N. Martin-Yuste, and C. Sanchez-Avila, “Deep Learning for Face Recognition on Mobile Devices,” IET Biometrics, vol. 9, no. 3, Jan. 2020, doi: 10.1049/iet-bmt.2019.0093. Ríos SánchezB. Costa-da SilvaD. Martin-YusteN. Sanchez-AvilaC. “Deep Learning for Face Recognition on Mobile Devices,” IET Biometrics 9 3 Jan. 2020 10.1049/iet-bmt.2019.0093 Open DOISearch in Google Scholar

P. Wang, P. Wang, and E. Fan, “Violence detection and face recognition based on deep learning,” Pattern Recognition Letters, vol. 142, pp. 20–24, Feb. 2021, doi: 10.1016/j.patrec.2020.11.018. WangP. WangP. FanE. “Violence detection and face recognition based on deep learning,” Pattern Recognition Letters 142 20 24 Feb. 2021 10.1016/j.patrec.2020.11.018 Open DOISearch in Google Scholar

Z. Pan et al., “Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome,” Endocrine, vol. 72, no. 3, Nov. 2020, doi: 10.1007/s12020-020-02539-3. PanZ. “Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome,” Endocrine 72 3 Nov. 2020 10.1007/s12020-020-02539-3 Open DOISearch in Google Scholar

N. K. Mishra, M. Dutta, and S. K. Singh, “Multiscale parallel deep CNN (mpdCNN) architecture for the real low-resolution face recognition for surveillance,” Image and Vision Computing, vol. 115, p. 104290, Nov. 2021, doi: 10.1016/j.imavis.2021.104290. MishraN. K. DuttaM. SinghS. K. “Multiscale parallel deep CNN (mpdCNN) architecture for the real low-resolution face recognition for surveillance,” Image and Vision Computing 115 104290 Nov. 2021 10.1016/j.imavis.2021.104290 Open DOISearch in Google Scholar

B. S. M. Teja, C. S. Anita, D. Rajalakshmi, and M. A. Berlin, “A CNN based facial expression recognizer,” Materials Today: Proceedings, vol. 37, pp. 2578–2581, 2021, doi: 10.1016/j.matpr.2020.08.501. TejaB. S. M. AnitaC. S. RajalakshmiD. BerlinM. A. “A CNN based facial expression recognizer,” Materials Today: Proceedings 37 2578 2581 2021 10.1016/j.matpr.2020.08.501 Open DOISearch in Google Scholar

J. Mohammed Sahan, E. I. Abbas, and Z. M. Abood, “A facial recognition using a combination of a novel one dimension deep CNN and LDA,” Materials Today: Proceedings, Jul. 2021, doi: 10.1016/j.matpr.2021.07.325. Mohammed SahanJ. AbbasE. I. AboodZ. M. “A facial recognition using a combination of a novel one dimension deep CNN and LDA,” Materials Today: Proceedings Jul. 2021 10.1016/j.matpr.2021.07.325 Open DOISearch in Google Scholar

X. Lv, M. Su, and Z. Wang, “Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems,” Microprocessors and Microsystems, p. 104034, Jan. 2021, doi: 10.1016/j.micpro.2021.104034. LvX. SuM. WangZ. “Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems,” Microprocessors and Microsystems 104034 Jan. 2021 10.1016/j.micpro.2021.104034 Open DOISearch in Google Scholar

R. Gupta and L. K. Vishwamitra, “Facial expression recognition from videos using CNN and feature aggregation,” Materials Today: Proceedings, Mar. 2021, doi: 10.1016/j.matpr.2020.11.795. GuptaR. VishwamitraL. K. “Facial expression recognition from videos using CNN and feature aggregation,” Materials Today: Proceedings Mar. 2021 10.1016/j.matpr.2020.11.795 Open DOISearch in Google Scholar

H. Ben Fredj, S. Bouguezzi, and C. Souani, “Face recognition in unconstrained environment with CNN,” The Visual Computer, vol. 37, no. 2, Feb. 2021. Ben FredjH. BouguezziS. SouaniC. “Face recognition in unconstrained environment with CNN,” The Visual Computer 37 2 Feb. 2021 Search in Google Scholar

Q. Meng, S. Zhao, Z. Huang, and F. Zhou, “MagFace: A Universal Representation for Face Recognition and Quality Assessment,” openaccess.thecvf.com, 2021. https://openaccess.thecvf.com/content/CVPR2021/html/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.html (accessed Apr. 17, 2023). MengQ. ZhaoS. HuangZ. ZhouF. “MagFace: A Universal Representation for Face Recognition and Quality Assessment,” openaccess.thecvf.com, 2021 https://openaccess.thecvf.com/content/CVPR2021/html/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.html (accessed Apr. 17, 2023). Search in Google Scholar

J. Deng, J. Guo, J. Yang, A. Lattas, and S. Zafeiriou, “Variational Prototype Learning for Deep Face Recognition,” openaccess.thecvf.com, 2021. https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Variational_Prototype_Learning_for_Deep_Face_Recognition_CVPR_2021_paper.html (accessed Apr. 17, 2023). DengJ. GuoJ. YangJ. LattasA. ZafeiriouS. “Variational Prototype Learning for Deep Face Recognition,” openaccess.thecvf.com, 2021 https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Variational_Prototype_Learning_for_Deep_Face_Recognition_CVPR_2021_paper.html (accessed Apr. 17, 2023). Search in Google Scholar

A. J. A. AlBdairi et al., “Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification,” Applied Sciences, vol. 12, no. 5, p. 2605, Mar. 2022. AlBdairiA. J. A. “Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification,” Applied Sciences 12 5 2605 Mar. 2022 Search in Google Scholar

W. Hariri, “Efficient masked face recognition method during the COVID-19 pandemic,” Signal, Image and Video Processing, vol. 16, no. 3, Apr. 2022. HaririW. “Efficient masked face recognition method during the COVID-19 pandemic,” Signal, Image and Video Processing 16 3 Apr. 2022 Search in Google Scholar

H. N. Vu, M. H. Nguyen, and C. Pham, “Masked face recognition with convolutional neural networks and local binary patterns,” Applied Intelligence, vol. 52, no. 5, Mar. 2022. VuH. N. NguyenM. H. PhamC. “Masked face recognition with convolutional neural networks and local binary patterns,” Applied Intelligence 52 5 Mar. 2022 Search in Google Scholar

Y.-H. Huang and H. H. Chen, “Deep face recognition for dim images,” Pattern Recognition, vol. 126, p. 108580, Jun. 2022, doi: 10.1016/j.patcog.2022.108580. HuangY.-H. ChenH. H. “Deep face recognition for dim images,” Pattern Recognition 126 108580 Jun. 2022 10.1016/j.patcog.2022.108580 Open DOISearch in Google Scholar

G. Kaur et al., “Face mask recognition system using CNN model,” Neuroscience Informatics, vol. 2, no. 3, p. 100035, Sep. 2022, doi: 10.1016/j.neuri.2021.100035. KaurG. “Face mask recognition system using CNN model,” Neuroscience Informatics 2 3 100035 Sep. 2022 10.1016/j.neuri.2021.100035 Open DOISearch in Google Scholar

J. Zhang, X. Yan, Z. Cheng, and X. Shen, “A face recognition algorithm based on feature fusion,” Concurrency and Computation: Practice and Experience, vol. 34, no. 14, p. e5748, Jun. 2022. ZhangJ. YanX. ChengZ. ShenX. “A face recognition algorithm based on feature fusion,” Concurrency and Computation: Practice and Experience 34 14 e5748 Jun. 2022 Search in Google Scholar

M. S. Ebrahimi Saadabadi, S. R. Malakshan, S. Soleymani, M. Mostofa, and N. M. Nasrabadi, “Information Maximization for Extreme Pose Face Recognition,” IEEE Xplore, Oct. 01, 2022. https://ieeexplore.ieee.org/abstract/document/10007931 Ebrahimi SaadabadiM. S. MalakshanS. R. SoleymaniS. MostofaM. NasrabadiN. M. “Information Maximization for Extreme Pose Face Recognition,” IEEE Xplore Oct. 01 2022 https://ieeexplore.ieee.org/abstract/document/10007931 Search in Google Scholar

M. S. E. Saadabadi, S. R. Malakshan, A. Zafari, M. Mostofa, and N. M. Nasrabadi, “A Quality Aware Sample-to-Sample Comparison for Face Recognition,” openaccess.thecvf.com, 2023. https://openaccess.thecvf.com/content/WACV2023/html/Saadabadi_A_Quality_Aware_Sample-to-Sample_Comparison_for_Face_Recognition_WACV_2023_paper.html (accessed Apr. 17, 2023). SaadabadiM. S. E. MalakshanS. R. ZafariA. MostofaM. NasrabadiN. M. “A Quality Aware Sample-to-Sample Comparison for Face Recognition,” openaccess.thecvf.com, 2023 https://openaccess.thecvf.com/content/WACV2023/html/Saadabadi_A_Quality_Aware_Sample-to-Sample_Comparison_for_Face_Recognition_WACV_2023_paper.html (accessed Apr. 17, 2023). Search in Google Scholar

F. Liu, M. Kim, A. Jain, and X. Liu, “Controllable and Guided Face Synthesis for Unconstrained Face Recognition,” Lecture Notes in Computer Science, pp. 701–719, 2022, doi: https://doi.org/10.1007/978-3-031-19775-8_41. LiuF. KimM. JainA. LiuX. “Controllable and Guided Face Synthesis for Unconstrained Face Recognition,” Lecture Notes in Computer Science 701 719 2022 https://doi.org/10.1007/978-3-031-19775-8_41. Search in Google Scholar

R. Cappelli, D. Maio, D. Maltoni, J. L. Wayman, and A. K. Jain, “Performance evaluation of fingerprint verification systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 3–18, Jan. 2006. CappelliR. MaioD. MaltoniD. WaymanJ. L. JainA. K. “Performance evaluation of fingerprint verification systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence 28 1 3 18 Jan. 2006 Search in Google Scholar

S. Kim, B. Park, B. S. Song, and S. Yang, “Deep belief network based statistical feature learning for fingerprint liveness detection,” Pattern Recognition Letters, vol. 77, pp. 58–65, Jul. 2016, doi: 10.1016/j.patrec.2016.03.015. KimS. ParkB. SongB. S. YangS. “Deep belief network based statistical feature learning for fingerprint liveness detection,” Pattern Recognition Letters 77 58 65 Jul. 2016 10.1016/j.patrec.2016.03.015 Open DOISearch in Google Scholar

R. F. Nogueira, R. de Alencar Lotufo, and R. Campos Machado, “Fingerprint Liveness Detection Using Convolutional Neural Networks,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1206–1213, Jun. 2016, doi: 10.1109/tifs.2016.2520880. NogueiraR. F. de Alencar LotufoR. Campos MachadoR. “Fingerprint Liveness Detection Using Convolutional Neural Networks,” IEEE Transactions on Information Forensics and Security 11 6 1206 1213 Jun. 2016 10.1109/tifs.2016.2520880 Open DOISearch in Google Scholar

X. Wang, L. Gao, S. Mao, and S. Pandey, “CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach,” IEEE Transactions on Vehicular Technology, vol. 1, no. 66, pp. 1–1, 2016, doi: 10.1109/tvt.2016.2545523. WangX. GaoL. MaoS. PandeyS. “CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach,” IEEE Transactions on Vehicular Technology 1 66 1 1 2016 10.1109/tvt.2016.2545523 Open DOISearch in Google Scholar

H.-U. Jang, D. Kim, S.-M. Mun, S. Choi, and H.-K. Lee, “DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks,” IEEE Signal Processing Letters, vol. 24, no. 12, pp. 1808–1812, Dec. 2017, doi: 10.1109/lsp.2017.2761454. JangH.-U. KimD. MunS.-M. ChoiS. LeeH.-K. “DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks,” IEEE Signal Processing Letters 24 12 1808 1812 Dec. 2017 10.1109/lsp.2017.2761454 Open DOISearch in Google Scholar

W.-S. Jeon and S.-Y. Rhee, “Fingerprint Pattern Classification Using Convolution Neural Network,” INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS, vol. 17, no. 3, pp. 170–176, Sep. 2017, doi: 10.5391/ijfis.2017.17.3.170. JeonW.-S. RheeS.-Y. “Fingerprint Pattern Classification Using Convolution Neural Network,” INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 17 3 170 176 Sep. 2017 10.5391/ijfis.2017.17.3.170 Open DOISearch in Google Scholar

W. Zhicheng, H. Zhang, J. Peng, and L. Yang, “Fingerprint identification based on neural network for large fingerprint database,” in Third International Workshop on Pattern Recognition, 2018, vol. 10828, pp. 18–24. ZhichengW. ZhangH. PengJ. YangL. “Fingerprint identification based on neural network for large fingerprint database,” in Third International Workshop on Pattern Recognition 2018 10828 18 24 Search in Google Scholar

K. Cao and A. K. Jain, “Automated Latent Fingerprint Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 4, pp. 788–800, Mar. 2018. CaoK. JainA. K. “Automated Latent Fingerprint Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence 41 4 788 800 Mar. 2018 Search in Google Scholar

J. Li, J. Feng, and C.-C. . J. Kuo, “Deep convolutional neural network for latent fingerprint enhancement,” Signal Processing: Image Communication, vol. 60, pp. 52–63, Feb. 2018, doi: 10.1016/j.image.2017.08.010. LiJ. FengJ. C.-C. . KuoJ. “Deep convolutional neural network for latent fingerprint enhancement,” Signal Processing: Image Communication 60 52 63 Feb. 2018 10.1016/j.image.2017.08.010 Open DOISearch in Google Scholar

P. Tertychnyi, C. Ozcinar, and G. Anbarjafari, “Low-quality fingerprint classification using deep neural network,” IET Biometrics, vol. 7, no. 6, pp. 550–556, Nov. 2018, doi: 10.1049/iet-bmt.2018.5074. TertychnyiP. OzcinarC. AnbarjafariG. “Low-quality fingerprint classification using deep neural network,” IET Biometrics 7 6 550 556 Nov. 2018 10.1049/iet-bmt.2018.5074 Open DOISearch in Google Scholar

D. Peralta, I. Triguero, S. García, Y. Saeys, J. M. Benitez, and F. Herrera, “On the use of convolutional neural networks for robust classification of multiple fingerprint captures,” International Journal of Intelligent Systems, vol. 33, no. 1, pp. 213–230, Nov. 2017. PeraltaD. TrigueroI. GarcíaS. SaeysY. BenitezJ. M. HerreraF. “On the use of convolutional neural networks for robust classification of multiple fingerprint captures,” International Journal of Intelligent Systems 33 1 213 230 Nov. 2017 Search in Google Scholar

Y. Yu, F. Liu, and S. Mao, “Fingerprint Extraction and Classification of Wireless Channels Based on Deep Convolutional Neural Networks,” Neural Processing Letters, vol. 48, no. 3, pp. 1767–1775, Feb. 2018. YuY. LiuF. MaoS. “Fingerprint Extraction and Classification of Wireless Channels Based on Deep Convolutional Neural Networks,” Neural Processing Letters 48 3 1767 1775 Feb. 2018 Search in Google Scholar

C. Lin and A. Kumar, “Contactless and partial 3D fingerprint recognition using multi-view deep representation,” Pattern Recognition, vol. 83, pp. 314–327, Nov. 2018, doi: 10.1016/j.patcog.2018.05.004. LinC. KumarA. “Contactless and partial 3D fingerprint recognition using multi-view deep representation,” Pattern Recognition 83 314 327 Nov. 2018 10.1016/j.patcog.2018.05.004 Open DOISearch in Google Scholar

H. Y. Jung and Y. S. Heo, “Fingerprint liveness map construction using convolutional neural network,” Electronics Letters, vol. 54, no. 9, pp. 564–566, May 2018, doi: 10.1049/el.2018.0621. JungH. Y. HeoY. S. “Fingerprint liveness map construction using convolutional neural network,” Electronics Letters 54 9 564 566 May 2018 10.1049/el.2018.0621 Open DOISearch in Google Scholar

K. Merchant, S. Revay, G. Stantchev, and B. Nousain, “Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 160–167, Feb. 2018. MerchantK. RevayS. StantchevG. NousainB. “Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks,” IEEE Journal of Selected Topics in Signal Processing 12 1 160 167 Feb. 2018 Search in Google Scholar

R. Donida Labati, A. Genovese, E. Muñoz, V. Piuri, and F. Scotti, “A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks,” Pattern Recognition Letters, vol. 113, pp. 58–66, Oct. 2018, doi: 10.1016/j.patrec.2017.04.001. Donida LabatiR. GenoveseA. MuñozE. PiuriV. ScottiF. “A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks,” Pattern Recognition Letters 113 58 66 Oct. 2018 10.1016/j.patrec.2017.04.001 Open DOISearch in Google Scholar

W. Shao, H. Luo, F. Zhao, Y. Ma, Z. Zhao, and A. Crivello, “Indoor Positioning Based on Fingerprint-Image and Deep Learning,” IEEE Access, vol. 6, pp. 74699–74712, 2018, doi: 10.1109/access.2018.2884193. ShaoW. LuoH. ZhaoF. MaY. ZhaoZ. CrivelloA. “Indoor Positioning Based on Fingerprint-Image and Deep Learning,” IEEE Access 6 74699 74712 2018 10.1109/access.2018.2884193 Open DOISearch in Google Scholar

F. Zeng, S. Hu, and K. Xiao, “Research on partial fingerprint recognition algorithm based on deep learning,” Neural Computing and Applications, vol. 31, no. 9, pp. 4789–4798, Jun. 2018, doi: 10.1007/s00521-018-3609-8. ZengF. HuS. XiaoK. “Research on partial fingerprint recognition algorithm based on deep learning,” Neural Computing and Applications 31 9 4789 4798 Jun. 2018 10.1007/s00521-018-3609-8 Open DOISearch in Google Scholar

N. F. Lori, I. Ramalhosa, P. Marques, and V. Alves, “Deep Learning Based Pipeline for Fingerprinting Using Brain Functional MRI Connectivity Data,” Procedia Computer Science, vol. 141, pp. 539–544, 2018, doi: 10.1016/j.procs.2018.10.129. LoriN. F. RamalhosaI. MarquesP. AlvesV. “Deep Learning Based Pipeline for Fingerprinting Using Brain Functional MRI Connectivity Data,” Procedia Computer Science 141 539 544 2018 10.1016/j.procs.2018.10.129 Open DOISearch in Google Scholar

D. Li and Y. Lei, “Deep Learning for Fingerprint-Based Outdoor Positioning via LTE Networks,” Sensors, vol. 19, no. 23, p. 5180, Nov. 2019. LiD. LeiY. “Deep Learning for Fingerprint-Based Outdoor Positioning via LTE Networks,” Sensors 19 23 5180 Nov. 2019 Search in Google Scholar

hhhhsjL. Peng, J. Zhang, M. Liu, and A. Hu, “Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 1091–1095, Jan. 2020. hhhhsjL. PengJ. Zhang LiuM. HuA. “Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure,” IEEE Transactions on Vehicular Technology 69 1 1091 1095 Jan. 2020 Search in Google Scholar

C. Yuan, Z. Xia, X. Sun, and Q. M. J. Wu, “Deep Residual Network With Adaptive Learning Framework for Fingerprint Liveness Detection,” IEEE Transactions on Cognitive and Developmental Systems, vol. 12, no. 3, pp. 461–473, Sep. 2020. YuanC. XiaZ. SunX. WuQ. M. J. “Deep Residual Network With Adaptive Learning Framework for Fingerprint Liveness Detection,” IEEE Transactions on Cognitive and Developmental Systems 12 3 461 473 Sep. 2020 Search in Google Scholar

F. Zhang, S. Xin, and J. Feng, “Combining global and minutia deep features for partial high-resolution fingerprint matching,” Pattern Recognition Letters, vol. 119, pp. 139–147, Mar. 2019, doi: 10.1016/j.patrec.2017.09.014. ZhangF. XinS. FengJ. “Combining global and minutia deep features for partial high-resolution fingerprint matching,” Pattern Recognition Letters 119 139 147 Mar. 2019 10.1016/j.patrec.2017.09.014 Open DOISearch in Google Scholar

J. B. Kho, W. Lee, H. Choi, and J. Kim, “An incremental learning method for spoof fingerprint detection,” Expert Systems with Applications, vol. 116, pp. 52–64, Feb. 2019, doi: 10.1016/j.eswa.2018.08.055. KhoJ. B. LeeW. ChoiH. KimJ. “An incremental learning method for spoof fingerprint detection,” Expert Systems with Applications 116 52 64 Feb. 2019 10.1016/j.eswa.2018.08.055 Open DOISearch in Google Scholar

Z. Liu, B. Dai, X. Wan, and X. Li, “Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network,” Sensors, vol. 19, no. 20, p. 4597, Oct. 2019. LiuZ. DaiB. WanX. LiX. “Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network,” Sensors 19 20 4597 Oct. 2019 Search in Google Scholar

A. Haider, Y. Wei, S. Liu, and S.-H. Hwang, “Pre- and Post-Processing Algorithms with Deep Learning Classifier for Wi-Fi Fingerprint-Based Indoor Positioning,” Electronics, vol. 8, no. 2, p. 195, Feb. 2019. HaiderA. WeiY. LiuS. HwangS.-H. “Pre- and Post-Processing Algorithms with Deep Learning Classifier for Wi-Fi Fingerprint-Based Indoor Positioning,” Electronics 8 2 195 Feb. 2019 Search in Google Scholar

D. Song, Y. Tang, and J. Feng, “Aggregating minutia-centred deep convolutional features for fingerprint indexing,” Pattern Recognition, vol. 88, pp. 397–408, Apr. 2019, doi: 10.1016/j.patcog.2018.11.018. SongD. TangY. FengJ. “Aggregating minutia-centred deep convolutional features for fingerprint indexing,” Pattern Recognition 88 397 408 Apr. 2019 10.1016/j.patcog.2018.11.018 Open DOISearch in Google Scholar

T. Zia, M. Ghafoor, S. A. Tariq, and I. A. Taj, “Robust fingerprint classification with Bayesian convolutional networks,” IET Image Processing, vol. 13, no. 8, pp. 1280–1288, Jun. 2019, doi: 10.1049/iet-ipr.2018.5466. ZiaT. GhafoorM. TariqS. A. TajI. A. “Robust fingerprint classification with Bayesian convolutional networks,” IET Image Processing 13 8 1280 1288 Jun. 2019 10.1049/iet-ipr.2018.5466 Open DOISearch in Google Scholar

D. M. Uliyan, S. Sadeghi, and H. A. Jalab, “Anti-spoofing method for fingerprint recognition using patch based deep learning machine,” Engineering Science and Technology, an International Journal, vol. 102, Jun. 2020, Accessed: Nov. 21, 2019. UliyanD. M. SadeghiS. JalabH. A. “Anti-spoofing method for fingerprint recognition using patch based deep learning machine,” Engineering Science and Technology, an International Journal 102 Jun. 2020 Accessed: Nov. 21, 2019. Search in Google Scholar

F. Liu, Y. Zhao, G. Liu, and L. Shen, “Fingerprint pore matching using deep features,” Pattern Recognition, vol. 102, p. 107208, Jun. 2020, doi: 10.1016/j.patcog.2020.107208. LiuF. ZhaoY. LiuG. ShenL. “Fingerprint pore matching using deep features,” Pattern Recognition 102 107208 Jun. 2020 10.1016/j.patcog.2020.107208 Open DOISearch in Google Scholar

X. Yang, Q. Hu, and S. Li, “Recognition and classification of damaged fingerprint based on deep learning fuzzy theory,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3529–3537, Apr. 2020, doi: 10.3233/jifs-179575. YangX. HuQ. LiS. “Recognition and classification of damaged fingerprint based on deep learning fuzzy theory,” Journal of Intelligent & Fuzzy Systems 38 4 3529 3537 Apr. 2020 10.3233/jifs-179575 Open DOISearch in Google Scholar

W. J. Wong and S.-H. Lai, “Multi-task CNN for restoring corrupted fingerprint images,” Pattern Recognition, vol. 101, p. 107203, May 2020, doi: 10.1016/j.patcog.2020.107203. WongW. J. LaiS.-H. “Multi-task CNN for restoring corrupted fingerprint images,” Pattern Recognition 101 107203 May 2020 10.1016/j.patcog.2020.107203 Open DOISearch in Google Scholar

S. Arora and M. P. S. Bhatia, “Fingerprint Spoofing Detection to Improve Customer Security in Mobile Financial Applications Using Deep Learning,” Arabian Journal for Science and Engineering, vol. 45, no. 4, pp. 2847–2863, Oct. 2019. AroraS. BhatiaM. P. S. “Fingerprint Spoofing Detection to Improve Customer Security in Mobile Financial Applications Using Deep Learning,” Arabian Journal for Science and Engineering 45 4 2847 2863 Oct. 2019 Search in Google Scholar

F. F. Alkhalid, “The effect of optimizers in fingerprint classification model utilizing deep learning,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 2, p. 1098, Nov. 2020, doi: 10.11591/ijeecs.v20.i2.pp1098-1102. AlkhalidF. F. “The effect of optimizers in fingerprint classification model utilizing deep learning,” Indonesian Journal of Electrical Engineering and Computer Science 20 2 1098 Nov. 2020 10.11591/ijeecs.v20.i2.pp1098-1102 Open DOISearch in Google Scholar

Z. Zhang, S. Liu, and M. Liu, “A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction,” Pattern Recognition, vol. 120, p. 108189, Dec. 2021, doi: 10.1016/j.patcog.2021.108189. ZhangZ. LiuS. LiuM. “A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction,” Pattern Recognition 120 108189 Dec. 2021 10.1016/j.patcog.2021.108189 Open DOISearch in Google Scholar

Nur-A-Alam, M. Ahsan, M. A. Based, J. Haider, and M. Kowalski, “An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning,” Computers & Electrical Engineering, vol. 95, p. 107387, Oct. 2021 Nur-A-AlamM. Ahsan BasedM. A. HaiderJ. KowalskiM. “An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning,” Computers & Electrical Engineering 95 107387 Oct. 2021 Search in Google Scholar

M. Leghari, S. Memon, L. D. Dhomeja, A. H. Jalbani, and A. A. Chandio, “Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics,” Computers, vol. 10, no. 2, p. 21, Feb. 2021, doi: 10.3390/computers10020021. LeghariM. MemonS. DhomejaL. D. JalbaniA. H. ChandioA. A. “Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics,” Computers 10 2 21 Feb. 2021 10.3390/computers10020021 Open DOISearch in Google Scholar

H. Li, “Feature extraction, recognition, and matching of damaged fingerprint: Application of deep learning network,” Concurrency and Computation: Practice and Experience, vol. 33, no. 6, Oct. 2020. LiH. “Feature extraction, recognition, and matching of damaged fingerprint: Application of deep learning network,” Concurrency and Computation: Practice and Experience 33 6 Oct. 2020 Search in Google Scholar

D. Li and Z. Niu, “A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning,” ISPRS International Journal of Geo-Information, vol. 10, no. 7, p. 442, Jun. 2021, doi: 10.3390/ijgi10070442. LiD. NiuZ. “A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning,” ISPRS International Journal of Geo-Information 10 7 442 Jun. 2021 10.3390/ijgi10070442 Open DOISearch in Google Scholar

S. Lee, S.-W. Jang, D. Kim, H. Hahn, and G.-Y. Kim, “A Novel Fingerprint Recovery Scheme using Deep Neural Network-based Learning,” Multimedia Tools and Applications, vol. 80, no. 26, Nov. 2021. LeeS. JangS.-W. KimD. HahnH. KimG.-Y. “A Novel Fingerprint Recovery Scheme using Deep Neural Network-based Learning,” Multimedia Tools and Applications 80 26 Nov. 2021 Search in Google Scholar

K. C. Deepika and G. Shivakumar, “A Robust Deep Features Enabled Touchless 3D-Fingerprint Classification System,” SN Computer Science, vol. 2, no. 4, May 2021, doi: 10.1007/s42979-021-00657-x. DeepikaK. C. ShivakumarG. “A Robust Deep Features Enabled Touchless 3D-Fingerprint Classification System,” SN Computer Science 2 4 May 2021 10.1007/s42979-021-00657-x Open DOISearch in Google Scholar

P. Nahar, N. S. Chaudhari, and S. K. Tanwani, “Fingerprint classification system using CNN,” Multimedia Tools and Applications, vol. 81, no. 17, pp. 24515–24527, Mar. 2022, doi: 10.1007/s11042-022-12294-4. NaharP. ChaudhariN. S. TanwaniS. K. “Fingerprint classification system using CNN,” Multimedia Tools and Applications 81 17 24515 24527 Mar. 2022 10.1007/s11042-022-12294-4 Open DOISearch in Google Scholar

A. M. Ibrahim, A. K. Eesee, and R. R. O. Al-Nima, “Deep fingerprint classification network,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 19, no. 3, p. 893, Jun. 2021, doi: 10.12928/telkomnika.v19i3.18771. IbrahimA. M. EeseeA. K. Al-NimaR. R. O. “Deep fingerprint classification network,” TELKOMNIKA (Telecommunication Computing Electronics and Control) 19 3 893 Jun. 2021 10.12928/telkomnika.v19i3.18771 Open DOISearch in Google Scholar

A. Ilham Gustisyaf and A. Sinaga, “Implementation of Convolutional Neural Network to Classification Gender based on Fingerprint,” International Journal of Modern Education and Computer Science, vol. 13, no. 4, pp. 55–67, Aug. 2021, doi: 10.5815/ijmecs.2021.04.05. Ilham GustisyafA. SinagaA. “Implementation of Convolutional Neural Network to Classification Gender based on Fingerprint,” International Journal of Modern Education and Computer Science 13 4 55 67 Aug. 2021 10.5815/ijmecs.2021.04.05 Open DOISearch in Google Scholar

L. Shi, S. Lan, H. Gui, Y. Yang, and Z. Guo, “A novel 2D contactless fingerprint matching method,” Neurocomputing, vol. 500, pp. 547–555, Aug. 2022, doi: 10.1016/j.neucom.2022.05.092. ShiL. LanS. GuiH. YangY. GuoZ. “A novel 2D contactless fingerprint matching method,” Neurocomputing 500 547 555 Aug. 2022 10.1016/j.neucom.2022.05.092 Open DOISearch in Google Scholar

C. Yuan, P. Yu, Z. Xia, X. Sun, and Q. M. J. Wu, “FLD-SRC: Fingerprint Liveness Detection for AFIS Based on Spatial Ridges Continuity,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 4, pp. 817–827, Jun. 2022, doi: 10.1109/jstsp.2022.3174655. YuanC. YuP. XiaZ. SunX. WuQ. M. J. “FLD-SRC: Fingerprint Liveness Detection for AFIS Based on Spatial Ridges Continuity,” IEEE Journal of Selected Topics in Signal Processing 16 4 817 827 Jun. 2022 10.1109/jstsp.2022.3174655 Open DOISearch in Google Scholar

A.-M. Dincă Lăzărescu, S. Moldovanu, and L. Moraru, “A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks,” Inventions, vol. 7, no. 2, p. 39, May 2022, doi: 10.3390/inventions7020039. Dincă LăzărescuA.-M. MoldovanuS. MoraruL. “A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks,” Inventions 7 2 39 May 2022 10.3390/inventions7020039 Open DOISearch in Google Scholar

F. Saeed, M. Hussain, and H. A. Aboalsamh, “Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet),” Mathematics, vol. 10, no. 8, p. 1285, Apr. 2022, doi: 10.3390/math10081285. SaeedF. HussainM. AboalsamhH. A. “Automatic Fingerprint Classification Using Deep Learning Technology (DeepFKTNet),” Mathematics 10 8 1285 Apr. 2022 10.3390/math10081285 Open DOISearch in Google Scholar

S. Gao, H. Y. K. Chau, K. Wang, H. Ao, R. S. Varghese, and H. W. Ressom, “Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation,” Metabolites, vol. 12, no. 7, p. 605, Jun. 2022, doi: 10.3390/metabo12070605. GaoS. ChauH. Y. K. WangK. AoH. VargheseR. S. RessomH. W. “Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation,” Metabolites 12 7 605 Jun. 2022 10.3390/metabo12070605 Open DOISearch in Google Scholar

J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148–1161, 1993, doi: 10.1109/34.244676. DaugmanJ. G. “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence 15 11 1148 1161 1993 10.1109/34.244676 Open DOISearch in Google Scholar

N. Liu, M. Zhang, H. Li, Z. Sun, and T. Tan, “DeepIris: Learning pairwise filter bank for heterogeneous iris verification,” Pattern Recognition Letters, vol. 82, pp. 154–161, Oct. 2016, doi: 10.1016/j.patrec.2015.09.016. LiuN. ZhangM. LiH. SunZ. TanT. “DeepIris: Learning pairwise filter bank for heterogeneous iris verification,” Pattern Recognition Letters 82 154 161 Oct. 2016 10.1016/j.patrec.2015.09.016 Open DOISearch in Google Scholar

S. Pravinthraja and K. Umamaheswari, “Optimized Features Extraction of IRIS Recognition by Using MADLA to Ensure Secure Authentication,” Circuits and Systems, vol. 07, no. 08, pp. 1927–1933, 2016, doi: 10.4236/cs.2016.78167. PravinthrajaS. UmamaheswariK. “Optimized Features Extraction of IRIS Recognition by Using MADLA to Ensure Secure Authentication,” Circuits and Systems 07 08 1927 1933 2016 10.4236/cs.2016.78167 Open DOISearch in Google Scholar

F. He, Y. Han, H. Wang, J. Ji, Y. Liu, and Z. Ma, “Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network,” Journal of Electronic Imaging, vol. 26, no. 2, p. 023005, Mar. 2017, doi: 10.1117/1.jei.26.2.023005. HeF. HanY. WangH. JiJ. LiuY. MaZ. “Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network,” Journal of Electronic Imaging 26 2 023005 Mar. 2017 10.1117/1.jei.26.2.023005 Open DOISearch in Google Scholar

M. Arsalan et al., “Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment,” Symmetry, vol. 9, no. 11, p. 263, Nov. 2017. ArsalanM. “Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment,” Symmetry 9 11 263 Nov. 2017 Search in Google Scholar

O. OYEDOTUN and A. KHASHMAN, “Iris nevus diagnosis: convolutional neural network and deep belief network,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, vol. 25, no. 2, pp. 1106–1115, 2017, doi: 10.3906/elk-1507-190. OYEDOTUNO. KHASHMANA. “Iris nevus diagnosis: convolutional neural network and deep belief network,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 25 2 1106 1115 2017 10.3906/elk-1507-190 Open DOISearch in Google Scholar

H. Proenca and J. C. Neves, “Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep Learning Frameworks,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 4, pp. 888–896, Apr. 2018. ProencaH. NevesJ. C. “Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep Learning Frameworks,” IEEE Transactions on Information Forensics and Security 13 4 888 896 Apr. 2018 Search in Google Scholar

K. Nguyen, C. Fookes, A. Ross, and S. Sridharan, “Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective,” IEEE Access, vol. 6, pp. 18848–18855, 2018, doi: 10.1109/access.2017.2784352. NguyenK. FookesC. RossA. SridharanS. “Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective,” IEEE Access 6 18848 18855 2018 10.1109/access.2017.2784352 Open DOISearch in Google Scholar

A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A. M. Nagem, “A multi-biometric iris recognition system based on a deep learning approach,” Pattern Analysis and Applications, vol. 21, no. 3, pp. 783–802, Aug. 2018, doi: 10.1007/s10044-017-0656-1. Al-WaisyA. S. QahwajiR. IpsonS. Al-FahdawiS. NagemT. A. M. “A multi-biometric iris recognition system based on a deep learning approach,” Pattern Analysis and Applications 21 3 783 802 Aug. 2018 10.1007/s10044-017-0656-1 Open DOISearch in Google Scholar

F. Marra, G. Poggi, C. Sansone, and L. Verdoliva, “A deep learning approach for iris sensor model identification,” Pattern Recognition Letters, vol. 113, pp. 46–53, Oct. 2018, doi: 10.1016/j.patrec.2017.04.010. MarraF. PoggiG. SansoneC. VerdolivaL. “A deep learning approach for iris sensor model identification,” Pattern Recognition Letters 113 46 53 Oct. 2018 10.1016/j.patrec.2017.04.010 Open DOISearch in Google Scholar

Q. Zhang, H. Li, Z. Sun, and T. Tan, “Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 11, pp. 2897–2912, Nov. 2018, doi: 10.1109/tifs.2018.2833033. ZhangQ. LiH. SunZ. TanT. “Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices,” IEEE Transactions on Information Forensics and Security 13 11 2897 2912 Nov. 2018 10.1109/tifs.2018.2833033 Open DOISearch in Google Scholar

Z. Wang, C. Li, H. Shao, and J. Sun, “Eye Recognition With Mixed Convolutional and Residual Network (MiCoRe-Net),” IEEE Access, vol. 6, pp. 17905–17912, 2018, doi: 10.1109/access.2018.2812208. WangZ. LiC. ShaoH. SunJ. “Eye Recognition With Mixed Convolutional and Residual Network (MiCoRe-Net),” IEEE Access 6 17905 17912 2018 10.1109/access.2018.2812208 Open DOISearch in Google Scholar

L. A. Elrefaei, D. H. Hamid, A. A. Bayazed, S. S. Bushnak, and S. Y. Maasher, “Developing Iris Recognition System for Smartphone Security,” Multimedia Tools and Applications, vol. 77, no. 12, pp. 14579–14603, Aug. 2017, doi: 10.1007/s11042-017-5049-3. ElrefaeiL. A. HamidD. H. BayazedA. A. BushnakS. S. MaasherS. Y. “Developing Iris Recognition System for Smartphone Security,” Multimedia Tools and Applications 77 12 14579 14603 Aug. 2017 10.1007/s11042-017-5049-3 Open DOISearch in Google Scholar

D. Nguyen, N. Baek, T. Pham, and K. Park, “Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor,” Sensors, vol. 18, no. 5, p. 1315, Apr. 2018, doi: 10.3390/s18051315. NguyenD. BaekN. PhamT. ParkK. “Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor,” Sensors 18 5 1315 Apr. 2018 10.3390/s18051315 Open DOISearch in Google Scholar

D. Nguyen, T. Pham, Y. Lee, and K. Park, “Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor,” Sensors, vol. 18, no. 8, p. 2601, Aug. 2018, doi: 10.3390/s18082601. NguyenD. PhamT. LeeY. ParkK. “Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor,” Sensors 18 8 2601 Aug. 2018 10.3390/s18082601 Open DOISearch in Google Scholar

Maram. G Alaslani and L. A. Elrefaei, “Convolutional Neural Network Based Feature Extraction for IRIS Recognition,” International Journal of Computer Science and Information Technology, vol. 10, no. 2, pp. 65–78, Apr. 2018, doi: 10.5121/ijcsit.2018.10206. AlaslaniMaram. G ElrefaeiL. A. “Convolutional Neural Network Based Feature Extraction for IRIS Recognition,” International Journal of Computer Science and Information Technology 10 2 65 78 Apr. 2018 10.5121/ijcsit.2018.10206 Open DOISearch in Google Scholar

Y. Chen, et al, “Adapted deep convnets technology for robust iris recognition”, Journal of Electronic Imaging, vol. 28, no.3, pp. 033008, 2019. ChenY. “Adapted deep convnets technology for robust iris recognition” Journal of Electronic Imaging 28 3 033008 2019 Search in Google Scholar

T. Zhao, Y. Liu, G. Huo, and X. Zhu, “A Deep Learning Iris Recognition Method Based on Capsule Network Architecture,” IEEE Access, vol. 7, pp. 49691–49701, 2019, doi: 10.1109/access.2019.2911056. ZhaoT. LiuY. HuoG. ZhuX. “A Deep Learning Iris Recognition Method Based on Capsule Network Architecture,” IEEE Access 7 49691 49701 2019 10.1109/access.2019.2911056 Open DOISearch in Google Scholar

K. Wang and A. Kumar, “Cross-spectral iris recognition using CNN and supervised discrete hashing,” Pattern Recognition, vol. 86, pp. 85–98, Feb. 2019, doi: 10.1016/j.patcog.2018.08.010. WangK. KumarA. “Cross-spectral iris recognition using CNN and supervised discrete hashing,” Pattern Recognition 86 85 98 Feb. 2019 10.1016/j.patcog.2018.08.010 Open DOISearch in Google Scholar

Z. Zhao and A. Kumar, “A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features,” Pattern Recognition, vol. 93, pp. 546–557, Sep. 2019, doi: 10.1016/j.patcog.2019.04.010. ZhaoZ. KumarA. “A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features,” Pattern Recognition 93 546 557 Sep. 2019 10.1016/j.patcog.2019.04.010 Open DOISearch in Google Scholar

Y. Lee, K. Kim, T. Hoang, M. Arsalan, and K. Park, “Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor,” Sensors, vol. 19, no. 4, p. 842, Feb. 2019. LeeY. KimK. HoangT. ArsalanM. ParkK. “Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor,” Sensors 19 4 842 Feb. 2019 Search in Google Scholar

X. Liu, Y. Bai, Y. Luo, Z. Yang, and Y. Liu, “Iris recognition in visible spectrum based on multi-layer analogous convolution and collaborative representation,” Pattern Recognition Letters, vol. 117, pp. 66–73, Jan. 2019, doi: 10.1016/j.patrec.2018.12.003. LiuX. BaiY. LuoY. YangZ. LiuY. “Iris recognition in visible spectrum based on multi-layer analogous convolution and collaborative representation,” Pattern Recognition Letters 117 66 73 Jan. 2019 10.1016/j.patrec.2018.12.003 Open DOISearch in Google Scholar

W. Zhang, X. Lu, Y. Gu, Y. Liu, X. Meng, and J. Li, “A Robust Iris Segmentation Scheme Based on Improved U-Net,” IEEE Access, vol. 7, pp. 85082–85089, 2019. ZhangW. LuX. GuY. LiuY. MengX. LiJ. “A Robust Iris Segmentation Scheme Based on Improved U-Net,” IEEE Access 7 85082 85089 2019 Search in Google Scholar

M. Liu, Z. Zhou, P. Shang, and D. Xu, “Fuzzified Image Enhancement for Deep Learning in Iris Recognition,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 1, pp. 92–99, Jan. 2020. LiuM. ZhouZ. ShangP. XuD. “Fuzzified Image Enhancement for Deep Learning in Iris Recognition,” IEEE Transactions on Fuzzy Systems 28 1 92 99 Jan. 2020 Search in Google Scholar

M. B. Lee, Y. H. Kim, and K. R. Park, “Conditional Generative Adversarial Network-Based Data Augmentation for Enhancement of Iris Recognition Accuracy,” IEEE Access, vol. 7, pp. 122134–122152, 2019. LeeM. B. KimY. H. ParkK. R. “Conditional Generative Adversarial Network-Based Data Augmentation for Enhancement of Iris Recognition Accuracy,” IEEE Access 7 122134 122152 2019 Search in Google Scholar

K. Wang and A. Kumar, “Toward More Accurate Iris Recognition Using Dilated Residual Features,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 12, pp. 3233–3245, Dec. 2019. WangK. KumarA. “Toward More Accurate Iris Recognition Using Dilated Residual Features,” IEEE Transactions on Information Forensics and Security 14 12 3233 3245 Dec. 2019 Search in Google Scholar

E. Ribeiro, A. Uhl, and F. Alonso-Fernandez, “Iris super-resolution using CNNs: is photo-realism important to iris recognition?,” IET Biometrics, vol. 8, no. 1, pp. 69–78, Aug. 2018. RibeiroE. UhlA. Alonso-FernandezF. “Iris super-resolution using CNNs: is photo-realism important to iris recognition?,” IET Biometrics 8 1 69 78 Aug. 2018 Search in Google Scholar

M. Trokielewicz, A. Czajka, and P. Maciejewicz, “Post-mortem iris recognition with deep-learning-based image segmentation,” Image and Vision Computing, vol. 94, p. 103866, Feb. 2020, doi: 10.1016/j.imavis.2019.103866. TrokielewiczM. CzajkaA. MaciejewiczP. “Post-mortem iris recognition with deep-learning-based image segmentation,” Image and Vision Computing 94 103866 Feb. 2020 10.1016/j.imavis.2019.103866 Open DOISearch in Google Scholar

M. Choudhary, V. Tiwari, and U. Venkanna, “Enhancing human iris recognition performance in unconstrained environment using ensemble of convolutional and residual deep neural network models,” Soft Computing, vol. 24, no. 15, pp. 11477–11491, Aug. 2020. ChoudharyM. TiwariV. VenkannaU. “Enhancing human iris recognition performance in unconstrained environment using ensemble of convolutional and residual deep neural network models,” Soft Computing 24 15 11477 11491 Aug. 2020 Search in Google Scholar

L. Shuai et al., “Multi-Source Feature Fusion and Entropy Feature Lightweight Neural Network for Constrained Multi-State Heterogeneous Iris Recognition,” IEEE Access, vol. 8, pp. 53321–53345, 2020. ShuaiL. “Multi-Source Feature Fusion and Entropy Feature Lightweight Neural Network for Constrained Multi-State Heterogeneous Iris Recognition,” IEEE Access 8 53321 53345 2020 Search in Google Scholar

Q. Hu, S. Yin, H. Ni, and Y. Huang, “An End to End Deep Neural Network for Iris Recognition,” Procedia Computer Science, vol. 174, pp. 505–517, 2020, doi: 10.1016/j.procs.2020.06.118. HuQ. YinS. NiH. HuangY. “An End to End Deep Neural Network for Iris Recognition,” Procedia Computer Science 174 505 517 2020 10.1016/j.procs.2020.06.118 Open DOISearch in Google Scholar

M. Sardar, S. Banerjee, and S. Mitra, “Iris Segmentation Using Interactive Deep Learning,” IEEE Access, vol. 8, pp. 219322–219330, 2020, doi: 10.1109/access.2020.3041519. SardarM. BanerjeeS. MitraS. “Iris Segmentation Using Interactive Deep Learning,” IEEE Access 8 219322 219330 2020 10.1109/access.2020.3041519 Open DOISearch in Google Scholar

Md. S. Azam and H. K. Rana, “Iris Recognition using Convolutional Neural Network,” International Journal of Computer Applications, vol. 175, no. 12, pp. 24–28, Aug. 2020. AzamMd. S. RanaH. K. “Iris Recognition using Convolutional Neural Network,” International Journal of Computer Applications 175 12 24 28 Aug. 2020 Search in Google Scholar

H. Hwang and E. C. Lee, “Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network,” IEEE Access, vol. 8, pp. 158612–158621, 2020, doi: 10.1109/access.2020.3020142. HwangH. LeeE. C. “Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network,” IEEE Access 8 158612 158621 2020 10.1109/access.2020.3020142 Open DOISearch in Google Scholar

Y. Chen, C. Wu, and Y. Wang, “T-Center: A Novel Feature Extraction Approach Towards Large-Scale Iris Recognition,” IEEE Access, vol. 8, pp. 32365–32375, 2020. ChenY. WuC. WangY. “T-Center: A Novel Feature Extraction Approach Towards Large-Scale Iris Recognition,” IEEE Access 8 32365 32375 2020 Search in Google Scholar

J. Jayanthi, E. L. Lydia, N. Krishnaraj, T. Jayasankar, R. L. Babu, and R. A. Suji, “An effective deep learning features based integrated framework for iris detection and recognition,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 3, pp. 3271–3281, Jun. 2020. JayanthiJ. LydiaE. L. KrishnarajN. JayasankarT. BabuR. L. SujiR. A. “An effective deep learning features based integrated framework for iris detection and recognition,” Journal of Ambient Intelligence and Humanized Computing 12 3 3271 3281 Jun. 2020 Search in Google Scholar

Y. Chen, C. Wu, and Y. Wang, “Whether normalized or not? Towards more robust iris recognition using dynamic programming,” Image and Vision Computing, vol. 107, p. 104112, Mar. 2021. ChenY. WuC. WangY. “Whether normalized or not? Towards more robust iris recognition using dynamic programming,” Image and Vision Computing 107 104112 Mar. 2021 Search in Google Scholar

S. Lei, B. Dong, Y. Li, F. Xiao, and F. Tian, “Iris recognition based on few-shot learning,” Computer Animation and Virtual Worlds, vol. 32, no. 3–4, May 2021. LeiS. DongB. LiY. XiaoF. TianF. “Iris recognition based on few-shot learning,” Computer Animation and Virtual Worlds 32 3–4 May 2021 Search in Google Scholar

C.-W. Chuang and C.-P. Fan, “Deep-Learning Based Joint Iris and Sclera Recognition with YOLO Network for Identity Identification,” Journal of Advances in Information Technology, vol. 12, no. 1, pp. 60–65, 2021. ChuangC.-W. FanC.-P. “Deep-Learning Based Joint Iris and Sclera Recognition with YOLO Network for Identity Identification,” Journal of Advances in Information Technology 12 1 60 65 2021 Search in Google Scholar

S. D. Shirke and C. Rajabhushnam, “Local gradient pattern and deep learning-based approach for the iris recognition at-a-distance,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 25, no. 1, pp. 49–64, Apr. 2021. ShirkeS. D. RajabhushnamC. “Local gradient pattern and deep learning-based approach for the iris recognition at-a-distance,” International Journal of Knowledge-based and Intelligent Engineering Systems 25 1 49 64 Apr. 2021 Search in Google Scholar

M. B. Lee, J. K. Kang, H. S. Yoon, and K. R. Park, “Enhanced Iris Recognition Method by Generative Adversarial Network-Based Image Reconstruction,” IEEE Access, vol. 9, pp. 10120–10135, 2021, doi: 10.1109/ACCESS.2021.3050788. LeeM. B. KangJ. K. YoonH. S. ParkK. R. “Enhanced Iris Recognition Method by Generative Adversarial Network-Based Image Reconstruction,” IEEE Access 9 10120 10135 2021 10.1109/ACCESS.2021.3050788 Open DOISearch in Google Scholar

G. Liu, W. Zhou, L. Tian, W. Liu, Y. Liu, and H. Xu, “An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network,” Sensors, vol. 21, no. 11, p. 3721, May 2021. LiuG. ZhouW. TianL. LiuW. LiuY. XuH. “An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network,” Sensors 21 11 3721 May 2021 Search in Google Scholar

Y. Chen, Z. Zeng, Y. Zeng, H. Gan, and H. Chen, “DenseSENet: more accurate and robust cross-domain iris recognition,” Journal of Electronic Imaging, vol. 30, no. 06, Dec. 2021. ChenY. ZengZ. ZengY. GanH. ChenH. “DenseSENet: more accurate and robust cross-domain iris recognition,” Journal of Electronic Imaging 30 06 Dec. 2021 Search in Google Scholar

J. J. Winston, D. J. Hemanth, A. Angelopoulou, and E. Kapetanios, “Hybrid deep convolutional neural models for iris image recognition,” Multimedia Tools and Applications, vol. 81, no. 7, Mar. 2022. WinstonJ. J. HemanthD. J. AngelopoulouA. KapetaniosE. “Hybrid deep convolutional neural models for iris image recognition,” Multimedia Tools and Applications 81 7 Mar. 2022 Search in Google Scholar

J. E. Zambrano, D. P. Benalcazar, C. A. Perez, and K. W. Bowyer, “Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching,” IEEE Access, vol. 10, pp. 41276–41286, 2022. ZambranoJ. E. BenalcazarD. P. PerezC. A. BowyerK. W. “Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching,” IEEE Access 10 41276 41286 2022 Search in Google Scholar

J. Sun, S. Zhao, S. Miao, X. Wang, and Y. Yu, “Open-set iris recognition based on deep learning,” IET Image Processing, Apr. 2022. SunJ. ZhaoS. MiaoS. WangX. YuY. “Open-set iris recognition based on deep learning,” IET Image Processing Apr. 2022 Search in Google Scholar

T. Lu, C. Wang, Y. Wang, and Z. Sun, “Multitask deep active contour-based iris segmentation for off-angle iris images,” Journal of Electronic Imaging, vol. 31, no. 04, Feb. 2022. LuT. WangC. WangY. SunZ. “Multitask deep active contour-based iris segmentation for off-angle iris images,” Journal of Electronic Imaging 31 04 Feb. 2022 Search in Google Scholar

N. Manisha A and G. Sachin R, “Iris Recognition System Based on Convolutional Neural Network,” in ICT with Intelligent Applications, Singapore.: Springer, 2022, pp. 455–463. N.Manisha A G.Sachin R “Iris Recognition System Based on Convolutional Neural Network,” in ICT with Intelligent Applications Singapore Springer 2022 455 463 Search in Google Scholar

W. R. Putri, S.-H. Liu, M. S. Aslam, Y.-H. Li, C.-C. Chang, and J.-C. Wang, “Self-Supervised Learning Framework toward State-of-the-Art Iris Image Segmentation,” Sensors, vol. 22, no. 6, p. 2133, Mar. 2022. PutriW. R. LiuS.-H. AslamM. S. LiY.-H. ChangC.-C. WangJ.-C. “Self-Supervised Learning Framework toward State-of-the-Art Iris Image Segmentation,” Sensors 22 6 2133 Mar. 2022 Search in Google Scholar

R. Alinia Lat, S. Danishvar, H. Heravi, and M. Danishvar, “Boosting Iris Recognition by Margin-Based Loss Functions,” Algorithms, vol. 15, no. 4, p. 118, Mar. 2022. Alinia LatR. DanishvarS. HeraviH. DanishvarM. “Boosting Iris Recognition by Margin-Based Loss Functions,” Algorithms 15 4 118 Mar. 2022 Search in Google Scholar

L. Yang, G. Yang, Y. Yin, and X. Xi, “Finger Vein Recognition With Anatomy Structure Analysis,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 8, pp. 1892–1905, Aug. 2018. YangL. YangG. YinY. XiX. “Finger Vein Recognition With Anatomy Structure Analysis,” IEEE Transactions on Circuits and Systems for Video Technology 28 8 1892 1905 Aug. 2018 Search in Google Scholar

D. Huang, X. Zhu, Y. Wang, and D. Zhang, “Dorsal hand vein recognition via hierarchical combination of texture and shape clues,” Neurocomputing, vol. 214, pp. 815–828, Nov. 2016. HuangD. ZhuX. WangY. ZhangD. “Dorsal hand vein recognition via hierarchical combination of texture and shape clues,” Neurocomputing 214 815 828 Nov. 2016 Search in Google Scholar

G. L. Grinblat, L. C. Uzal, M. G. Larese, and P. M. Granitto, “Deep learning for plant identification using vein morphological patterns,” Computers and Electronics in Agriculture, vol. 127, pp. 418–424, Sep. 2016. GrinblatG. L. UzalL. C. LareseM. G. GranittoP. M. “Deep learning for plant identification using vein morphological patterns,” Computers and Electronics in Agriculture 127 418 424 Sep. 2016 Search in Google Scholar

D. T. Nguyen, H. S. Yoon, T. D. Pham, and K. R. Park, “Spoof Detection for Finger-Vein Recognition System Using NIR Camera,” Sensors, vol. 17, no. 10, p. 2261, Oct. 2017, doi: 10.3390/s17102261. NguyenD. T. YoonH. S. PhamT. D. ParkK. R. “Spoof Detection for Finger-Vein Recognition System Using NIR Camera,” Sensors 17 10 2261 Oct. 2017 10.3390/s17102261 Open DOISearch in Google Scholar

H. Qin and M. A. El-Yacoubi, “Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 8, pp. 1816–1829, Aug. 2017 QinH. El-YacoubiM. A. “Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification,” IEEE Transactions on Information Forensics and Security 12 8 1816 1829 Aug. 2017 Search in Google Scholar

Hong, H. Gil, M. Beom Lee, and K. Ryoung Park, “Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors,” Sensors, vol. 17, no. 6, p. 1297, Jun. 2017. HongH. Gil Beom LeeM. Ryoung ParkK. “Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors,” Sensors 17 6 1297 Jun. 2017 Search in Google Scholar

g Chen, Z. Wu, J. Zhang, P. Li, and F. Azmat, “A finger vein recognition algorithm based on deep learning,” International Journal of Embedded Systems, vol. 9, no. 3, p. 220, 2017, doi: 10.1504/ijes.2017.084690. Cheng WuZ. ZhangJ. LiP. AzmatF. “A finger vein recognition algorithm based on deep learning,” International Journal of Embedded Systems 9 3 220 2017 10.1504/ijes.2017.084690 Open DOISearch in Google Scholar

J. Wang and G. Wang, “Hand-dorsa vein recognition with structure growing guided CNN,” Optik, vol. 149, pp. 469–477, Nov. 2017, doi: 10.1016/j.ijleo.2017.09.064. WangJ. WangG. “Hand-dorsa vein recognition with structure growing guided CNN,” Optik 149 469 477 Nov. 2017 10.1016/j.ijleo.2017.09.064 Open DOISearch in Google Scholar

Y. Liu, J. Ling, Z. Liu, J. Shen, and C. Gao, “Finger vein secure biometric template generation based on deep learning,” Soft Computing, vol. 22, no. 7, pp. 2257–2265, Jan. 2017. LiuY. LingJ. LiuZ. ShenJ. GaoC. “Finger vein secure biometric template generation based on deep learning,” Soft Computing 22 7 2257 2265 Jan. 2017 Search in Google Scholar

Y. Fang, Q. Wu, and W. Kang, “A novel finger vein verification system based on two-stream convolutional network learning,” Neurocomputing, vol. 290, pp. 100–107, May 2018, doi: 10.1016/j.neucom.2018.02.042. FangY. WuQ. KangW. “A novel finger vein verification system based on two-stream convolutional network learning,” Neurocomputing 290 100 107 May 2018 10.1016/j.neucom.2018.02.042 Open DOISearch in Google Scholar

J. Wang, K. Yang, Z. Pan, G. Wang, M. Li, and Y. Li, “Minutiae-Based Weighting Aggregation of Deep Convolutional Features for Vein Recognition,” IEEE Access, vol. 6, pp. 61640–61650, 2018, doi: 10.1109/access.2018.2876396. WangJ. YangK. PanZ. WangG. LiM. LiY. “Minutiae-Based Weighting Aggregation of Deep Convolutional Features for Vein Recognition,” IEEE Access 6 61640 61650 2018 10.1109/access.2018.2876396 Open DOISearch in Google Scholar

J. Wang, Z. Pan, G. Wang, M. Li, and Y. Li, “Spatial Pyramid Pooling of Selective Convolutional Features for Vein Recognition,” IEEE Access, vol. 6, pp. 28563–28572, 2018, doi: 10.1109/access.2018.2839720. WangJ. PanZ. WangG. LiM. LiY. “Spatial Pyramid Pooling of Selective Convolutional Features for Vein Recognition,” IEEE Access 6 28563 28572 2018 10.1109/access.2018.2839720 Open DOISearch in Google Scholar

R. Das, E. Piciucco, E. Maiorana, and P. Campisi, “Convolutional Neural Network for Finger-Vein-Based Biometric Identification,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 2, pp. 360–373, Feb. 2019. DasR. PiciuccoE. MaioranaE. CampisiP. “Convolutional Neural Network for Finger-Vein-Based Biometric Identification,” IEEE Transactions on Information Forensics and Security 14 2 360 373 Feb. 2019 Search in Google Scholar

W. Kim, J. M. Song, and K. R. Park, “Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor,” Sensors, vol. 18, no. 7, p. 2296, Jul. 2018. KimW. SongJ. M. ParkK. R. “Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor,” Sensors 18 7 2296 Jul. 2018 Search in Google Scholar

C. Xie and A. Kumar, “Finger vein identification using Convolutional Neural Network and supervised discrete hashing,” Pattern Recognition Letters, vol. 119, pp. 148–156, Mar. 2019, doi: 10.1016/j.patrec.2017.12.001. XieC. KumarA. “Finger vein identification using Convolutional Neural Network and supervised discrete hashing,” Pattern Recognition Letters 119 148 156 Mar. 2019 10.1016/j.patrec.2017.12.001 Open DOISearch in Google Scholar

L. Jinghui and P. Fang, “FVGNN: A novel GNN to finger vein recognition from limited training data,” in 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), May 2019, pp. 144–148. JinghuiL. FangP. “FVGNN: A novel GNN to finger vein recognition from limited training data,” in 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) May 2019 144 148 Search in Google Scholar

J. Zhang, Z. Lu, M. Li, and H. Wu, “GAN-Based Image Augmentation for Finger-Vein Biometric Recognition,” IEEE Access, vol. 7, pp. 183118–183132, 2019, doi: 10.1109/access.2019.2960411. ZhangJ. LuZ. LiM. WuH. “GAN-Based Image Augmentation for Finger-Vein Biometric Recognition,” IEEE Access 7 183118 183132 2019 10.1109/access.2019.2960411 Open DOISearch in Google Scholar

B. Hou and R. Yan, “Convolutional Autoencoder Model for Finger-Vein Verification,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 5, pp. 2067–2074, May 2020. HouB. YanR. “Convolutional Autoencoder Model for Finger-Vein Verification,” IEEE Transactions on Instrumentation and Measurement 69 5 2067 2074 May 2020 Search in Google Scholar

N. M. Kamaruddin and B. A. Rosdi, “A New Filter Generation Method in PCANet for Finger Vein Recognition,” IEEE Access, vol. 7, pp. 132966–132978, 2019, doi: 10.1109/access.2019.2941555. KamaruddinN. M. RosdiB. A. “A New Filter Generation Method in PCANet for Finger Vein Recognition,” IEEE Access 7 132966 132978 2019 10.1109/access.2019.2941555 Open DOISearch in Google Scholar

J. M. Song, W. Kim, and K. R. Park, “Finger-Vein Recognition Based on Deep DenseNet Using Composite Image,” IEEE Access, vol. 7, pp. 66845–66863, 2019, doi: 10.1109/access.2019.2918503. SongJ. M. KimW. ParkK. R. “Finger-Vein Recognition Based on Deep DenseNet Using Composite Image,” IEEE Access 7 66845 66863 2019 10.1109/access.2019.2918503 Open DOISearch in Google Scholar

W. Yang, S. Wang, J. Hu, G. Zheng, J. Yang, and C. Valli, “Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram,” IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4244–4253, Jul. 2019. YangW. WangS. HuJ. ZhengG. YangJ. ValliC. “Securing Deep Learning Based Edge Finger Vein Biometrics With Binary Decision Diagram,” IEEE Transactions on Industrial Informatics 15 7 4244 4253 Jul. 2019 Search in Google Scholar

Y. Lu, S. Xie, and S. Wu, “Exploring Competitive Features Using Deep Convolutional Neural Network for Finger Vein Recognition,” IEEE Access, vol. 7, pp. 35113–35123, 2019, doi: 10.1109/access.2019.2902429. LuY. XieS. WuS. “Exploring Competitive Features Using Deep Convolutional Neural Network for Finger Vein Recognition,” IEEE Access 7 35113 35123 2019 10.1109/access.2019.2902429 Open DOISearch in Google Scholar

W. Kang, H. Liu, W. Luo, and F. Deng, “Study of a Full-View 3D Finger Vein Verification Technique,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1175–1189, 2020, doi: 10.1109/tifs.2019.2928507. KangW. LiuH. LuoW. DengF. “Study of a Full-View 3D Finger Vein Verification Technique,” IEEE Transactions on Information Forensics and Security 15 1175 1189 2020 10.1109/tifs.2019.2928507 Open DOISearch in Google Scholar

D. Zhao, H. Ma, Z. Yang, J. Li, and W. Tian, “Finger vein recognition based on lightweight CNN combining center loss and dynamic regularization,” Infrared Physics & Technology, vol. 105, p. 103221, Mar. 2020, doi: 10.1016/j.infrared.2020.103221. ZhaoD. MaH. YangZ. LiJ. TianW. “Finger vein recognition based on lightweight CNN combining center loss and dynamic regularization,” Infrared Physics & Technology 105 103221 Mar. 2020 10.1016/j.infrared.2020.103221 Open DOISearch in Google Scholar

W. Yang, W. Luo, W. Kang, Z. Huang, and Q. Wu, “FVRAS-Net: An Embedded Finger-Vein Recognition and AntiSpoofing System Using a Unified CNN,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 11, pp. 8690–8701, Nov. 2020. YangW. LuoW. KangW. HuangZ. WuQ. “FVRAS-Net: An Embedded Finger-Vein Recognition and AntiSpoofing System Using a Unified CNN,” IEEE Transactions on Instrumentation and Measurement 69 11 8690 8701 Nov. 2020 Search in Google Scholar

R. S. Kuzu, E. Maiorana, and P. Campisi, “Vein-Based Biometric Verification Using Densely-Connected Convolutional Autoencoder,” IEEE Signal Processing Letters, vol. 27, pp. 1869–1873, 2020, doi: 10.1109/lsp.2020.3030533. KuzuR. S. MaioranaE. CampisiP. “Vein-Based Biometric Verification Using Densely-Connected Convolutional Autoencoder,” IEEE Signal Processing Letters 27 1869 1873 2020 10.1109/lsp.2020.3030533 Open DOISearch in Google Scholar

R. S. Kuzu, E. Piciucco, E. Maiorana, and P. Campisi, “On-the-Fly Finger-Vein-Based Biometric Recognition Using Deep Neural Networks,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2641–2654, 2020, doi: 10.1109/tifs.2020.2971144. KuzuR. S. PiciuccoE. MaioranaE. CampisiP. “On-the-Fly Finger-Vein-Based Biometric Recognition Using Deep Neural Networks,” IEEE Transactions on Information Forensics and Security 15 2641 2654 2020 10.1109/tifs.2020.2971144 Open DOISearch in Google Scholar

I. Boucherit, M. O. Zmirli, H. Hentabli, and B. A. Rosdi, “Finger vein identification using deeply-fused Convolutional Neural Network,” Journal of King Saud University - Computer and Information Sciences, Apr. 2020, doi: 10.1016/j.jksuci.2020.04.002. BoucheritI. ZmirliM. O. HentabliH. RosdiB. A. “Finger vein identification using deeply-fused Convolutional Neural Network,” Journal of King Saud University - Computer and Information Sciences Apr. 2020 10.1016/j.jksuci.2020.04.002 Open DOISearch in Google Scholar

Z. Jia-Yi, G. Jin Jin, M. Si-Ten, and L. Zhe-Ming, “Curvature gray feature decomposition based finger vein recognition with an improved convolutional neural network,” International Journal of Innovative Computing, Information and Control, vol. 16, no. 1, pp. 77–90, 2020. Jia-YiZ. Jin JinG. Si-TenM. Zhe-MingL. “Curvature gray feature decomposition based finger vein recognition with an improved convolutional neural network,” International Journal of Innovative Computing, Information and Control 16 1 77 90 2020 Search in Google Scholar

J. Choi, K. J. Noh, S. W. Cho, S. H. Nam, M. Owais, and K. R. Park, “Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition,” IEEE Access, vol. 8, pp. 16281–16301, 2020, doi: 10.1109/access.2020.2967771. ChoiJ. NohK. J. ChoS. W. NamS. H. OwaisM. ParkK. R. “Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition,” IEEE Access 8 16281 16301 2020 10.1109/access.2020.2967771 Open DOISearch in Google Scholar

K. J. Noh, J. Choi, J. S. Hong, and K. R. Park, “Finger-Vein Recognition Based on Densely Connected Convolutional Network Using Score-Level Fusion With Shape and Texture Images,” IEEE Access, vol. 8, pp. 96748–96766, 2020, doi: 10.1109/access.2020.2996646. NohK. J. ChoiJ. HongJ. S. ParkK. R. “Finger-Vein Recognition Based on Densely Connected Convolutional Network Using Score-Level Fusion With Shape and Texture Images,” IEEE Access 8 96748 96766 2020 10.1109/access.2020.2996646 Open DOISearch in Google Scholar

J. Zeng et al., “Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field,” IEEE Access, vol. 8, pp. 65402–65419, 2020, doi: 10.1109/access.2020.2984711 ZengJ. “Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field,” IEEE Access 8 65402 65419 2020 10.1109/access.2020.2984711 Open DOISearch in Google Scholar

A. Bilal, G. Sun, and S. Mazhar, “Finger-vein recognition using a novel enhancement method with convolutional neural network,” Journal of the Chinese Institute of Engineers, vol. 44, no. 5, pp. 407–417, May 2021. BilalA. SunG. MazharS. “Finger-vein recognition using a novel enhancement method with convolutional neural network,” Journal of the Chinese Institute of Engineers 44 5 407 417 May 2021 Search in Google Scholar

J. Shen et al., “Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–13, 2022, doi: 10.1109/tim.2021.3132332 ShenJ. “Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network,” IEEE Transactions on Instrumentation and Measurement 71 1 13 2022 10.1109/tim.2021.3132332 Open DOISearch in Google Scholar

K. Wang, G. Chen, and H. Chu, “Finger Vein Recognition Based on Multi-Receptive Field Bilinear Convolutional Neural Network,” IEEE Signal Processing Letters, vol. 28, pp. 1590–1594, 2021, doi: 10.1109/lsp.2021.3094998. WangK. ChenG. ChuH. “Finger Vein Recognition Based on Multi-Receptive Field Bilinear Convolutional Neural Network,” IEEE Signal Processing Letters 28 1590 1594 2021 10.1109/lsp.2021.3094998 Open DOISearch in Google Scholar

B. Hou and R. Yan, “ArcVein-Arccosine Center Loss for Finger Vein Verification,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2021, doi: 10.1109/tim.2021.3062164. HouB. YanR. “ArcVein-Arccosine Center Loss for Finger Vein Verification,” IEEE Transactions on Instrumentation and Measurement 70 1 11 2021 10.1109/tim.2021.3062164 Open DOISearch in Google Scholar

J. Huang, M. Tu, W. Yang, and W. Kang, “Joint Attention Network for Finger Vein Authentication,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–11, 2021, doi: 10.1109/tim.2021.3109978. HuangJ. TuM. YangW. KangW. “Joint Attention Network for Finger Vein Authentication,” IEEE Transactions on Instrumentation and Measurement 70 1 11 2021 10.1109/tim.2021.3109978 Open DOISearch in Google Scholar

H. Ren, L. Sun, J. Guo, C. Han, and F. Wu, “Finger vein recognition system with template protection based on convolutional neural network,” Knowledge-Based Systems, vol. 227, p. 107159, Sep. 2021, doi: 10.1016/j.knosys.2021.107159. RenH. SunL. GuoJ. HanC. WuF. “Finger vein recognition system with template protection based on convolutional neural network,” Knowledge-Based Systems 227 107159 Sep. 2021 10.1016/j.knosys.2021.107159 Open DOISearch in Google Scholar

W.-F. Ou, L.-M. Po, C. Zhou, Y. A. U. Rehman, P.-F. Xian, and Y.-J. Zhang, “Fusion loss and inter-class data augmentation for deep finger vein feature learning,” Expert Systems with Applications, vol. 171, p. 114584, Jun. 2021, doi: 10.1016/j.eswa.2021.114584. OuW.-F. PoL.-M. ZhouC. RehmanY. A. U. XianP.-F. ZhangY.-J. “Fusion loss and inter-class data augmentation for deep finger vein feature learning,” Expert Systems with Applications 171 114584 Jun. 2021 10.1016/j.eswa.2021.114584 Open DOISearch in Google Scholar

J. Choi, J. S. Hong, M. Owais, S. G. Kim, and K. R. Park, “Restoration of Motion Blurred Image by Modified DeblurGAN for Enhancing the Accuracies of Finger-Vein Recognition,” Sensors, vol. 21, no. 14, p. 4635, Jul. 2021. ChoiJ. HongJ. S. OwaisM. KimS. G. ParkK. R. “Restoration of Motion Blurred Image by Modified DeblurGAN for Enhancing the Accuracies of Finger-Vein Recognition,” Sensors 21 14 4635 Jul. 2021 Search in Google Scholar

C. Bhavya, T. Shikhar, J. Rupav, T. Archit, and S. Smriti, “Finger Vein Recognition Using Deep Learning,” in Proceedings of International Conference on Artificial Intelligence and Applications, Singapore: Springer, 2021, pp. 69–78. BhavyaC. ShikharT. RupavJ. ArchitT. SmritiS. “Finger Vein Recognition Using Deep Learning,” in Proceedings of International Conference on Artificial Intelligence and Applications Singapore Springer 2021 69 78 Search in Google Scholar

Z. Huang and C. Guo, “Robust Finger Vein Recognition Based on Deep CNN with Spatial Attention and Bias Field Correction,” International Journal on Artificial Intelligence Tools, vol. 30, no. 01, p. 2140005, Jan. 2021. HuangZ. GuoC. “Robust Finger Vein Recognition Based on Deep CNN with Spatial Attention and Bias Field Correction,” International Journal on Artificial Intelligence Tools 30 01 2140005 Jan. 2021 Search in Google Scholar

K. Shaheed et al., “DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition,” Expert Systems with Applications, vol. 191, p. 116288, Apr. 2022, doi: 10.1016/j.eswa.2021.116288. ShaheedK. “DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition,” Expert Systems with Applications 191 116288 Apr. 2022 10.1016/j.eswa.2021.116288 Open DOISearch in Google Scholar

D. Muthusamy and P. Rakkimuthu, “Trilateral Filterative Hermitian feature transformed deep perceptive fuzzy neural network for finger vein verification,” Expert Systems with Applications, vol. 196, p. 116678, Jun. 2022, doi: 10.1016/j.eswa.2022.116678. MuthusamyD. RakkimuthuP. “Trilateral Filterative Hermitian feature transformed deep perceptive fuzzy neural network for finger vein verification,” Expert Systems with Applications 196 116678 Jun. 2022 10.1016/j.eswa.2022.116678 Open DOISearch in Google Scholar

Y. Zhang, W. Li, L. Zhang, X. Ning, L. Sun, and Y. Lu, “AGCNN: Adaptive Gabor Convolutional Neural Networks with Receptive Fields for Vein Biometric Recognition,” Concurrency and Computation: Practice and Experience, vol. 34, no. 12, May 2022. ZhangY. LiW. ZhangL. NingX. SunL. LuY. “AGCNN: Adaptive Gabor Convolutional Neural Networks with Receptive Fields for Vein Biometric Recognition,” Concurrency and Computation: Practice and Experience 34 12 May 2022 Search in Google Scholar

P. Zhao, S. Zhao, L. Chen, W. Yang, and Q. Liao, “Exploiting Multi-Perspective Driven Hierarchical Content-Aware Network for Finger Vein Verification,” IEEE Transactions on Circuits and Systems for Video Technology, pp. 1–1, 2022, doi: 10.1109/tcsvt.2022.3188561. ZhaoP. ZhaoS. ChenL. YangW. LiaoQ. “Exploiting Multi-Perspective Driven Hierarchical Content-Aware Network for Finger Vein Verification,” IEEE Transactions on Circuits and Systems for Video Technology 1 1 2022 10.1109/tcsvt.2022.3188561 Open DOISearch in Google Scholar

B. Hou and R. Yan, “Triplet-Classifier GAN for Finger-Vein Verification,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–12, 2022, doi: 10.1109/tim.2022.3154834. HouB. YanR. “Triplet-Classifier GAN for Finger-Vein Verification,” IEEE Transactions on Instrumentation and Measurement 71 1 12 2022 10.1109/tim.2022.3154834 Open DOISearch in Google Scholar

J. Huang, W. Luo, W. Yang, A. Zheng, F. Lian, and W. Kang, “FVT: Finger Vein Transformer for Authentication,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–13, 2022, doi: 10.1109/tim.2022.3173276. HuangJ. LuoW. YangW. ZhengA. LianF. KangW. “FVT: Finger Vein Transformer for Authentication,” IEEE Transactions on Instrumentation and Measurement 71 1 13 2022 10.1109/tim.2022.3173276 Open DOISearch in Google Scholar

“LFW Face Database : Main,” vis-www.cs.umass.edu. http://vis-www.cs.umass.edu/lfw/ “LFW Face Database : Main,” vis-www.cs.umass.edu. http://vis-www.cs.umass.edu/lfw/ Search in Google Scholar

“Yale Face Database | vision.ucsd.edu,” vision.ucsd.edu. http://vision.ucsd.edu/content/yale-face-database “Yale Face Database | vision.ucsd.edu,” vision.ucsd.edu. http://vision.ucsd.edu/content/yale-face-database Search in Google Scholar

A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643–660, Jun. 2001. GeorghiadesA. S. BelhumeurP. N. KriegmanD. J. “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence 23 6 643 660 Jun. 2001 Search in Google Scholar

“The CMU Multi-PIE Face Database,” www.cs.cmu.edu. http://www.cs.cmu.edu/afs/cs/project/PIE/Multi4Pie/ “The CMU Multi-PIE Face Database,” www.cs.cmu.edu. http://www.cs.cmu.edu/afs/cs/project/PIE/Multi4Pie/ Search in Google Scholar

R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker, “Multi-PIE,” Image and Vision Computing, vol. 28, no. 5, pp. 807–813, May 2010. GrossR. MatthewsI. CohnJ. KanadeT. BakerS. “Multi-PIE,” Image and Vision Computing 28 5 807 813 May 2010 Search in Google Scholar

W. Lior, H. Tal, and M. Itay, “Face recognition in unconstrained videos with matched background similarity,” in CVPR 2011, IEEE, 2011, pp. 529–534. LiorW. TalH. ItayM. “Face recognition in unconstrained videos with matched background similarity,” in CVPR 2011, IEEE, 2011, 529 534 Search in Google Scholar

“AR Face Database Webpage,” www2.ece.ohio-state.edu. https://www2.ece.ohio-state.edu/~aleix/ARdatabase.html “AR Face Database Webpage,” www2.ece.ohio-state.edu. https://www2.ece.ohio-state.edu/~aleix/ARdatabase.html Search in Google Scholar

“- Biometrics Research Database Catalog,” tsapps.nist.gov. https://tsapps.nist.gov/BDbC/Search/Details/329 “- Biometrics Research Database Catalog,” tsapps.nist.gov. https://tsapps.nist.gov/BDbC/Search/Details/329 Search in Google Scholar

R. Karl and T. Tamirat, “Morph: A longitudinal image database of normal adult age-progression,” in 7th international conference on automatic face and gesture recognition (FGR06), Apr. 2006, pp. 341–345. KarlR. TamiratT. “Morph: A longitudinal image database of normal adult age-progression,” in 7th international conference on automatic face and gesture recognition (FGR06) Apr. 2006 341 345 Search in Google Scholar

C. Qiong, S. Li, X. Weidi, P. Omkar M, and Z. Andrew, “Vggface2: A dataset for recognising faces across pose and age,” in 2018 13th IEEE international conference on automatic face \& gesture recognition (FG 2018), May 2018, pp. 67–74. QiongC. LiS. WeidiX. Omkar MP. AndrewZ. “Vggface2: A dataset for recognising faces across pose and age,” in 2018 13th IEEE international conference on automatic face \& gesture recognition (FG 2018) May 2018 67 74 Search in Google Scholar

K. Brendan F et al., “Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1931–1939. Brendan FK. “Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a,” in Proceedings of the IEEE conference on computer vision and pattern recognition 2015 1931 1939 Search in Google Scholar

B. Sandipan, S. Walter, B. Kevin, and F. Patrick, “On hallucinating context and background pixels from a face mask using multi-scale gans,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2020, pp. 300–309. SandipanB. WalterS. KevinB. PatrickF. “On hallucinating context and background pixels from a face mask using multi-scale gans,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2020 300 309 Search in Google Scholar

E. Eidinger, R. Enbar, and T. Hassner, “Age and Gender Estimation of Unfiltered Faces,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2170–2179, Dec. 2014, doi: 10.1109/tifs.2014.2359646. EidingerE. EnbarR. HassnerT. “Age and Gender Estimation of Unfiltered Faces,” IEEE Transactions on Information Forensics and Security 9 12 2170 2179 Dec. 2014 10.1109/tifs.2014.2359646 Open DOISearch in Google Scholar

G. Yandong, Z. Lei, H. Yuxiao, H. Xiaodong, and G. Jianfeng, “Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,” European conference on computer vision, pp. 87–102, 2016. YandongG. LeiZ. YuxiaoH. XiaodongH. JianfengG. “Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,” European conference on computer vision 87 102 2016 Search in Google Scholar

L. Ziwei, L. Ping, W. Xiaogang, and T. Xiaoou, “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 3730–3738. ZiweiL. PingL. XiaogangW. XiaoouT. “Deep learning face attributes in the wild,” in Proceedings of the IEEE international conference on computer vision 2015 3730 3738 Search in Google Scholar

K. Shlizerman Ira, S. Steven M, M. Daniel, and B. Evan, “The megaface benchmark: 1 million faces for recognition at scale,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4873–4882. Shlizerman IraK. Steven MS. DanielM. EvanB. “The megaface benchmark: 1 million faces for recognition at scale,” in Proceedings of the IEEE conference on computer vision and pattern recognition 2016 4873 4882 Search in Google Scholar

B. Thomee et al., “YFCC100M,” Communications of the ACM, vol. 59, no. 2, pp. 64–73, Jan. 2016, doi: 10.1145/2812802. ThomeeB. “YFCC100M,” Communications of the ACM 59 2 64 73 Jan. 2016 10.1145/2812802 Open DOISearch in Google Scholar

Y. Yilong, L. Lili, and S. Xiwei, “SDUMLA-HMT: a multimodal biometric database,” in Chinese Conference on Biometric Recognition, Dec. 2011, pp. 260–268. YilongY. LiliL. XiweiS. “SDUMLA-HMT: a multimodal biometric database,” in Chinese Conference on Biometric Recognition Dec. 2011 260 268 Search in Google Scholar

M. Ghazi Mostafa and K. Ekenel, Hazim, “A comprehensive analysis of deep learning based representation for face recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2016, pp. 34–41. M. GhaziMostafa K. EkenelHazim “A comprehensive analysis of deep learning based representation for face recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops 2016 34 41 Search in Google Scholar

“ORL - V7 Open Datasets,” www.v7labs.com. https://www.v7labs.com/open-datasets/orl (accessed Oct. 08, 2022). “ORL - V7 Open Datasets,” www.v7labs.com. https://www.v7labs.com/open-datasets/orl (accessed Oct. 08, 2022). Search in Google Scholar

M. Dario, M. Davide, C. Raffaele, W. James L, and J. Anil K, “FVC2002: Second fingerprint verification competition,” in 2002 International Conference on Pattern Recognition, 2002, pp. 811–814. DarioM. DavideM. RaffaeleC. James LW. Anil KJ. “FVC2002: Second fingerprint verification competition,” in 2002 International Conference on Pattern Recognition 2002 811 814 Search in Google Scholar

“Ajay Kumar, The Hong Kong Polytechnic University, Hong Kong,” www4.comp.polyu.edu.hk. http://www4.comp.polyu.edu.hk/~csajaykr/database.php (accessed Oct. 10, 2022). “Ajay Kumar, The Hong Kong Polytechnic University, Hong Kong,” www4.comp.polyu.edu.hk. http://www4.comp.polyu.edu.hk/~csajaykr/database.php (accessed Oct. 10, 2022). Search in Google Scholar

“BIT,” biometrics.idealtest.org. http://biometrics.idealtest.org/dbDetailForUser.do?id=7 “BIT,” biometrics.idealtest.org. http://biometrics.idealtest.org/dbDetailForUser.do?id=7 Search in Google Scholar

G. Michael D, IST special database 27: Fingerprint minutiae from latent and matching tenprint images. US Department of Commerce, National Institute of Standards and Technology, 2000. G. MichaelD IST special database 27: Fingerprint minutiae from latent and matching tenprint images US Department of Commerce, National Institute of Standards and Technology 2000 Search in Google Scholar

“3-D Face Database | Biometrics and Identification Innovation Center | West Virginia University,” biic.wvu.edu. https://biic.wvu.edu/data-sets/3-d-face-databset (accessed Oct. 08, 2022). “3-D Face Database | Biometrics and Identification Innovation Center | West Virginia University,” biic.wvu.edu. https://biic.wvu.edu/data-sets/3-d-face-databset (accessed Oct. 08, 2022). Search in Google Scholar

“Papers with Code - Recognition Oriented Iris Image Quality Assessment in the Feature Space,” paperswithcode.com. https://paperswithcode.com/paper/recognition-oriented-iris-image-quality “Papers with Code - Recognition Oriented Iris Image Quality Assessment in the Feature Space,” paperswithcode.com. https://paperswithcode.com/paper/recognition-oriented-iris-image-quality Search in Google Scholar

K. B. Raja, R. Raghavendra, V. K. Vemuri, and C. Busch, “Smartphone based visible iris recognition using deep sparse filtering,” Pattern Recognition Letters, vol. 57, pp. 33–42, May 2015, doi: 10.1016/j.patrec.2014.09.006. RajaK. B. RaghavendraR. VemuriV. K. BuschC. “Smartphone based visible iris recognition using deep sparse filtering,” Pattern Recognition Letters 57 33 42 May 2015 10.1016/j.patrec.2014.09.006 Open DOISearch in Google Scholar

“UBIRIS,” iris.di.ubi.pt. http://iris.di.ubi.pt/ (accessed Oct. 08, 2022). “UBIRIS,” iris.di.ubi.pt. http://iris.di.ubi.pt/ (accessed Oct. 08, 2022). Search in Google Scholar

M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Mobile Iris Challenge Evaluation (MICHE)-I, biometric iris dataset and protocols,” Pattern Recognition Letters, vol. 57, pp. 17–23, May 2015, doi: 10.1016/j.patrec.2015.02.009. De MarsicoM. NappiM. RiccioD. WechslerH. “Mobile Iris Challenge Evaluation (MICHE)-I, biometric iris dataset and protocols,” Pattern Recognition Letters 57 17 23 May 2015 10.1016/j.patrec.2015.02.009 Open DOISearch in Google Scholar

“IIT Delhi Iris Database,” Polyu.edu.hk, 2021. https://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.hm “IIT Delhi Iris Database,” Polyu.edu.hk, 2021. https://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.hm Search in Google Scholar

J. Peter A, L.-M. Paulo, S. Nadezhda, H. Fang, and S. S, “Quality in face and iris research ensemble (Q-FIRE),” in 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2010, pp. 1–6. Peter AJ. PauloL.-M. NadezhdaS. FangH. S. S, “Quality in face and iris research ensemble (Q-FIRE),” in 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) 2010 1 6 Search in Google Scholar

“Notre Dame CVRL,” cvrl.nd.edu. https://cvrl.nd.edu/projects/data/ (accessed Oct. 08, 2022). “Notre Dame CVRL,” cvrl.nd.edu. https://cvrl.nd.edu/projects/data/ (accessed Oct. 08, 2022). Search in Google Scholar

“Andy's Iris Recognition,” andyzeng.github.io. http://andyzeng.github.io/irisrecognition (accessed Oct. 08, 2022). “Andy's Iris Recognition,” andyzeng.github.io. http://andyzeng.github.io/irisrecognition (accessed Oct. 08, 2022). Search in Google Scholar

W. Yang, X. Huang, F. Zhou, and Q. Liao, “Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion,” Information Sciences, vol. 268, pp. 20–32, Jun. 2014. YangW. HuangX. ZhouF. LiaoQ. “Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion,” Information Sciences 268 20 32 Jun. 2014 Search in Google Scholar

“- Biometrics Research Database Catalog,” tsapps.nist.gov. https://tsapps.nist.gov/BDbC/Search/Details/420 (accessed Oct. 08, 2022). “- Biometrics Research Database Catalog,” tsapps.nist.gov. https://tsapps.nist.gov/BDbC/Search/Details/420 (accessed Oct. 08, 2022). Search in Google Scholar

L. Yu, X. Shan Juan, Y. Sook, W. Zhihui, and P. Dong Sun, “An available database for the research of finger vein recognition,” in 2013 6th International congress on image and signal processing (CISP), 2013, pp. 410–415. YuL. Shan JuanX. SookY. ZhihuiW. Dong SunP. “An available database for the research of finger vein recognition,” in 2013 6th International congress on image and signal processing (CISP) 2013 410 415 Search in Google Scholar

“Multimedia Signal Processing and Security Lab,” www.wavelab.at. https://www.wavelab.at/sources/PLUS-OpenVein/ (accessed Oct. 08, 2022). “Multimedia Signal Processing and Security Lab,” www.wavelab.at. https://www.wavelab.at/sources/PLUS-OpenVein/ (accessed Oct. 08, 2022). Search in Google Scholar

A. Kumar and Yingbo Zhou, “Human Identification Using Finger Images,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2228–2244, Apr. 2012, doi: 10.1109/tip.2011. Information Security, Chaotic Theories, and participated in conference sponsored by IOP and has a published work bout securing medical images within chaotic-based cryptography approach in 2018. KumarA. ZhouYingbo “Human Identification Using Finger Images,” IEEE Transactions on Image Processing 21 4 2228 2244 Apr. 2012 10.1109/tip.2011 Information Security, Chaotic Theories, and participated in conference sponsored by IOP and has a published work bout securing medical images within chaotic-based cryptography approach in 2018. Open DOISearch in Google Scholar

Sprache:
Englisch