This work is licensed under the Creative Commons Attribution 4.0 International License.
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 modalitiesExp. Sys. with Appl.143113114Apr.2020Search 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 context49792012Search 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.135227622972018Search 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 Surveys513134May2018Search 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 Understanding1011115Jan.2006Search 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 Intelligence47622014Search 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 Télécommunications621–21135Jan.2007Search 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-LatiefAbdThamearShahadYussofSalmanAhmadAzhanaKhadimSaif MohanadAbdulhasanRaed Abdulkareem“Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning.”International Journal of Networked and Distributed Computing2024118Search 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.JainAnilMaioD.MaltoniDavideBiometric Systems Technology, Design and Performance EvaluationLondon Springer-Verlag London Limited2005Search 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 Technology141420Jan.2004Search 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 Networks6185117Jan.201510.1016/j.neunet.2014.09.003Open 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 Applications1271702020Search 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/nature14539LeCunY.BengioY.HintonG.“Deep Learning,”Nature5217553436444May201510.1038/nature14539Open 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 Medicine455712714201910.1007/s00134-019-05537-wOpen 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 Agriculture16210011010Jul.201910.1016/j.compag.2019.05.019Open 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 AbdulkareemAbd Al-latiefShahad ThamearKadhimSaif Mohanad“Instant learning based on deep neural network with linear discriminant analysis features extraction for accurate iris recognition system.”Multimedia Tools and Applications831120243209932122Search 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.MinaeeShervinAbdolrashidiA.SuH.BennamounM.ZhangD.“Biometrics recognition using deep learning: A survey,”arXiv preprint arXiv:1912.00271,2019Search 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 Computation31712351270Jul.201910.1162/neco_a_01199Open 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 Computation9817351780Nov.1997Search 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/1412.3555ChungJ.GulcehreC.ChoK.BengioY.“Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,”arXiv.org,2014https://arxiv.org/abs/1412.3555Search 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.2017doi: 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 Learning124307392201910.1561/2200000056Open 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 Computation18715271554Jul.200610.1162/neco.2006.18.7.1527Open 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,”Neurocomputing2341126Apr.201710.1016/j.neucom.2016.12.038Open 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 ACM6311139144Oct.202010.1145/3422622Open 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 Systems1212021https://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 Science1319119172018doi: 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 Informatics112020https://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,”Electronics83292Mar.2019doi: 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,”Sensors20216378Nov.2020doi: 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 Access71588201588462019doi: 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-Technology646976Aug.201410.14257/ijbsbt.2014.6.4.07Open 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 Security07031411512016Search 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 Imaging252023025Apr.2016Search 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 Technology263517528Mar.2016Search 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 Biometrics54297303Jun.2016Search 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 Recognition601324Dec.201610.1016/j.patcog.2016.05.014Open 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 Applications76181900519015Feb.201710.1007/s11042-016-4342-xOpen 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,”Neurocomputing187410Apr.201610.1016/j.neucom.2015.09.115Open 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,”Information7461Oct.201610.3390/info7040061Open 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 Science107715720201710.1016/j.procs.2017.03.153Open 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 Technology213947Mar.2017Search 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 Networks94115124Oct.201710.1016/j.neunet.2017.06.013Open 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 Applications2913554Sep.2017Search 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 Processing20171Jun.201710.1186/s13640-017-0188-zOpen 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 Intelligence472382396Mar.201710.1007/s10489-017-0902-7Open 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 Recognition80225240Aug.201810.1016/j.patcog.2018.03.018Open 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,”Neurocomputing2975058Jul.201810.1016/j.neucom.2018.02.037Open 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,”Neurocomputing2994250Jul.201810.1016/j.neucom.2018.03.030Open 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 Science1327683201810.1016/j.procs.2018.05.164Open 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 Science135510517201810.1016/j.procs.2018.08.203Open 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 Communications103211951206Feb.201810.1007/s11277-018-5377-2Open 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 Communications102428532868Jan.2018Search 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 Applications77192466324680Feb.2018Search 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 Access64515345165201810.1109/access.2018.2865425Open 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 Letters128414419Dec.201910.1016/j.patrec.2019.10.002Open 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 Access77262272633201910.1109/access.2019.2918275Open 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 Computation355325342Aug.201910.1016/j.amc.2019.02.071Open 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 Recognition90161171Jun.201910.1016/j.patcog.2019.01.041Open 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 Imaging28021Mar.2019Search 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 Systems99213225Oct.201910.1016/j.future.2019.04.025Open 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,”Neurocomputing3633545Jun.201910.1016/j.neucom.2019.04.085Open 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,”Electronics83324Mar.2019Search 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,”Electronics8101088Sep.2019Search 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.htmlDengJ.GuoJ.XueN.ZafeiriouS.“ArcFace: Additive Angular Margin Loss for Deep Face Recognition,”openaccess.thecvf.com,2019https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.htmlSearch 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 Engineering85106640Jul.202010.1016/j.compeleceng.2020.106640Open 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 Sciences10134669Jul.202010.3390/app10134669Open 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 Communications152215222Feb.202010.1016/j.comcom.2020.01.050Open 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 Access8174922174930202010.1109/access.2020.3023782Open 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 Biometrics93Jan.202010.1049/iet-bmt.2019.0093Open 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 Letters1422024Feb.202110.1016/j.patrec.2020.11.018Open 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,”Endocrine723Nov.202010.1007/s12020-020-02539-3Open 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 Computing115104290Nov.202110.1016/j.imavis.2021.104290Open 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: Proceedings3725782581202110.1016/j.matpr.2020.08.501Open 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: ProceedingsJul.202110.1016/j.matpr.2021.07.325Open 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 Microsystems104034Jan.202110.1016/j.micpro.2021.104034Open 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: ProceedingsMar.202110.1016/j.matpr.2020.11.795Open 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 Computer372Feb.2021Search 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,2021https://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,2021https://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 Sciences1252605Mar.2022Search 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 Processing163Apr.2022Search 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 Intelligence525Mar.2022Search 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 Recognition126108580Jun.202210.1016/j.patcog.2022.108580Open 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 Informatics23100035Sep.202210.1016/j.neuri.2021.100035Open 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 Experience3414e5748Jun.2022Search 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/10007931Ebrahimi SaadabadiM. S.MalakshanS. R.SoleymaniS.MostofaM.NasrabadiN. M.“Information Maximization for Extreme Pose Face Recognition,”IEEE XploreOct.012022https://ieeexplore.ieee.org/abstract/document/10007931Search 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,2023https://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 Science7017192022https://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 Intelligence281318Jan.2006Search 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 Letters775865Jul.201610.1016/j.patrec.2016.03.015Open 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 Security11612061213Jun.201610.1109/tifs.2016.2520880Open 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 Technology16611201610.1109/tvt.2016.2545523Open 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 Letters241218081812Dec.201710.1109/lsp.2017.2761454Open 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 SYSTEMS173170176Sep.201710.5391/ijfis.2017.17.3.170Open 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,”inThird International Workshop on Pattern Recognition2018108281824Search 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 Intelligence414788800Mar.2018Search 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 Communication605263Feb.201810.1016/j.image.2017.08.010Open 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 Biometrics76550556Nov.201810.1049/iet-bmt.2018.5074Open 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 Systems331213230Nov.2017Search 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 Letters48317671775Feb.2018Search 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 Recognition83314327Nov.201810.1016/j.patcog.2018.05.004Open 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 Letters549564566May201810.1049/el.2018.0621Open 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 Processing121160167Feb.2018Search 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 Letters1135866Oct.201810.1016/j.patrec.2017.04.001Open 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 Access67469974712201810.1109/access.2018.2884193Open 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 Applications31947894798Jun.201810.1007/s00521-018-3609-8Open 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 Science141539544201810.1016/j.procs.2018.10.129Open 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,”Sensors19235180Nov.2019Search 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. ZhangLiuM.HuA.“Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure,”IEEE Transactions on Vehicular Technology69110911095Jan.2020Search 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 Systems123461473Sep.2020Search 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 Letters119139147Mar.201910.1016/j.patrec.2017.09.014Open 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 Applications1165264Feb.201910.1016/j.eswa.2018.08.055Open 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,”Sensors19204597Oct.2019Search 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,”Electronics82195Feb.2019Search 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 Recognition88397408Apr.201910.1016/j.patcog.2018.11.018Open 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 Processing13812801288Jun.201910.1049/iet-ipr.2018.5466Open 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 Journal102Jun.2020Accessed: 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 Recognition102107208Jun.202010.1016/j.patcog.2020.107208Open 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 Systems38435293537Apr.202010.3233/jifs-179575Open 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 Recognition101107203May202010.1016/j.patcog.2020.107203Open 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 Engineering45428472863Oct.2019Search 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 Science2021098Nov.202010.11591/ijeecs.v20.i2.pp1098-1102Open 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 Recognition120108189Dec.202110.1016/j.patcog.2021.108189Open 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. 2021Nur-A-AlamM. AhsanBasedM. A.HaiderJ.KowalskiM.“An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning,”Computers & Electrical Engineering95107387Oct.2021Search 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,”Computers10221Feb.202110.3390/computers10020021Open 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 Experience336Oct.2020Search 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-Information107442Jun.202110.3390/ijgi10070442Open 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 Applications8026Nov.2021Search 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 Science24May202110.1007/s42979-021-00657-xOpen 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 Applications81172451524527Mar.202210.1007/s11042-022-12294-4Open 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)193893Jun.202110.12928/telkomnika.v19i3.18771Open 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 Science1345567Aug.202110.5815/ijmecs.2021.04.05Open 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,”Neurocomputing500547555Aug.202210.1016/j.neucom.2022.05.092Open 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 Processing164817827Jun.202210.1109/jstsp.2022.3174655Open 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,”Inventions7239May202210.3390/inventions7020039Open 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),”Mathematics1081285Apr.202210.3390/math10081285Open 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,”Metabolites127605Jun.202210.3390/metabo12070605Open 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 Intelligence151111481161199310.1109/34.244676Open 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 Letters82154161Oct.201610.1016/j.patrec.2015.09.016Open 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 Systems070819271933201610.4236/cs.2016.78167Open 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 Imaging262023005Mar.201710.1117/1.jei.26.2.023005Open 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,”Symmetry911263Nov.2017Search 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 SCIENCES25211061115201710.3906/elk-1507-190Open 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 Security134888896Apr.2018Search 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 Access61884818855201810.1109/access.2017.2784352Open 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 Applications213783802Aug.201810.1007/s10044-017-0656-1Open 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 Letters1134653Oct.201810.1016/j.patrec.2017.04.010Open 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 Security131128972912Nov.201810.1109/tifs.2018.2833033Open 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 Access61790517912201810.1109/access.2018.2812208Open 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 Applications77121457914603Aug.201710.1007/s11042-017-5049-3Open 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,”Sensors1851315Apr.201810.3390/s18051315Open 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,”Sensors1882601Aug.201810.3390/s18082601Open 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. GElrefaeiL. A.“Convolutional Neural Network Based Feature Extraction for IRIS Recognition,”International Journal of Computer Science and Information Technology1026578Apr.201810.5121/ijcsit.2018.10206Open 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 Imaging2830330082019Search 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 Access74969149701201910.1109/access.2019.2911056Open 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 Recognition868598Feb.201910.1016/j.patcog.2018.08.010Open 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 Recognition93546557Sep.201910.1016/j.patcog.2019.04.010Open 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,”Sensors194842Feb.2019Search 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 Letters1176673Jan.201910.1016/j.patrec.2018.12.003Open 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 Access785082850892019Search 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 Systems2819299Jan.2020Search 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 Access71221341221522019Search 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 Security141232333245Dec.2019Search 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 Biometrics816978Aug.2018Search 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 Computing94103866Feb.202010.1016/j.imavis.2019.103866Open 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 Computing24151147711491Aug.2020Search 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 Access853321533452020Search 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 Science174505517202010.1016/j.procs.2020.06.118Open 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 Access8219322219330202010.1109/access.2020.3041519Open 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 Applications175122428Aug.2020Search 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 Access8158612158621202010.1109/access.2020.3020142Open 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 Access832365323752020Search 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 Computing12332713281Jun.2020Search 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 Computing107104112Mar.2021Search 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 Worlds323–4May2021Search 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 Technology12160652021Search 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 Systems2514964Apr.2021Search 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 Access91012010135202110.1109/ACCESS.2021.3050788Open 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,”Sensors21113721May2021Search 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 Imaging3006Dec.2021Search 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 Applications817Mar.2022Search 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 Access1041276412862022Search 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 ProcessingApr.2022Search 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 Imaging3104Feb.2022Search 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 AG.Sachin R“Iris Recognition System Based on Convolutional Neural Network,”inICT with Intelligent ApplicationsSingaporeSpringer2022455463Search 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,”Sensors2262133Mar.2022Search 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,”Algorithms154118Mar.2022Search 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 Technology28818921905Aug.2018Search 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,”Neurocomputing214815828Nov.2016Search 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 Agriculture127418424Sep.2016Search 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,”Sensors17102261Oct.201710.3390/s17102261Open 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. 2017QinH.El-YacoubiM. A.“Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification,”IEEE Transactions on Information Forensics and Security12818161829Aug.2017Search 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. GilBeom LeeM.Ryoung ParkK.“Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors,”Sensors1761297Jun.2017Search 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.ChengWuZ.ZhangJ.LiP.AzmatF.“A finger vein recognition algorithm based on deep learning,”International Journal of Embedded Systems93220201710.1504/ijes.2017.084690Open 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,”Optik149469477Nov.201710.1016/j.ijleo.2017.09.064Open 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 Computing22722572265Jan.2017Search 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,”Neurocomputing290100107May201810.1016/j.neucom.2018.02.042Open 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 Access66164061650201810.1109/access.2018.2876396Open 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 Access62856328572201810.1109/access.2018.2839720Open 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 Security142360373Feb.2019Search 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,”Sensors1872296Jul.2018Search 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 Letters119148156Mar.201910.1016/j.patrec.2017.12.001Open 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,”in2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)May 2019144148Search 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 Access7183118183132201910.1109/access.2019.2960411Open 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 Measurement69520672074May2020Search 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 Access7132966132978201910.1109/access.2019.2941555Open 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 Access76684566863201910.1109/access.2019.2918503Open 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 Informatics15742444253Jul.2019Search 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 Access73511335123201910.1109/access.2019.2902429Open 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 Security1511751189202010.1109/tifs.2019.2928507Open 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 & Technology105103221Mar.202010.1016/j.infrared.2020.103221Open 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 Measurement691186908701Nov.2020Search 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 Letters2718691873202010.1109/lsp.2020.3030533Open 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 Security1526412654202010.1109/tifs.2020.2971144Open 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 SciencesApr.202010.1016/j.jksuci.2020.04.002Open 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 Control16177902020Search 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 Access81628116301202010.1109/access.2020.2967771Open 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 Access89674896766202010.1109/access.2020.2996646Open 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.2020.2984711ZengJ.“Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field,”IEEE Access86540265419202010.1109/access.2020.2984711Open 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 Engineers445407417May2021Search 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.2021.3132332ShenJ.“Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network,”IEEE Transactions on Instrumentation and Measurement71113202210.1109/tim.2021.3132332Open 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 Letters2815901594202110.1109/lsp.2021.3094998Open 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 Measurement70111202110.1109/tim.2021.3062164Open 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 Measurement70111202110.1109/tim.2021.3109978Open 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 Systems227107159Sep.202110.1016/j.knosys.2021.107159Open 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 Applications171114584Jun.202110.1016/j.eswa.2021.114584Open 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,”Sensors21144635Jul.2021Search 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,”inProceedings of International Conference on Artificial Intelligence and ApplicationsSingaporeSpringer20216978Search 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 Tools30012140005Jan.2021Search 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 Applications191116288Apr.202210.1016/j.eswa.2021.116288Open 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 Applications196116678Jun.202210.1016/j.eswa.2022.116678Open 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 Experience3412May2022Search 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 Technology11202210.1109/tcsvt.2022.3188561Open 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 Measurement71112202210.1109/tim.2022.3154834Open 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 Measurement71113202210.1109/tim.2022.3173276Open 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-databaseSearch 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 Intelligence236643660Jun.2001Search 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 Computing285807813May2010Search 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, 2011,529534Search 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.htmlSearch 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/329Search 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,”in7th international conference on automatic face and gesture recognition (FGR06)Apr. 2006341345Search 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,”in2018 13th IEEE international conference on automatic face \& gesture recognition (FG 2018)May 20186774Search 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,”inProceedings of the IEEE conference on computer vision and pattern recognition201519311939Search 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,”inProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision2020300309Search 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 Security91221702179Dec.201410.1109/tifs.2014.2359646Open 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 vision871022016Search 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,”inProceedings of the IEEE international conference on computer vision201537303738Search 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,”inProceedings of the IEEE conference on computer vision and pattern recognition201648734882Search 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 ACM5926473Jan.201610.1145/2812802Open 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,”inChinese Conference on Biometric RecognitionDec.2011260268Search 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. GhaziMostafaK. EkenelHazim“A comprehensive analysis of deep learning based representation for face recognition,”inProceedings of the IEEE conference on computer vision and pattern recognition workshops20163441Search 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,”in2002 International Conference on Pattern Recognition2002811814Search 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=7Search 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. MichaelDIST special database 27: Fingerprint minutiae from latent and matching tenprint imagesUS Department of Commerce, National Institute of Standards and Technology2000Search 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-qualitySearch 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 Letters573342May201510.1016/j.patrec.2014.09.006Open 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 Letters571723May201510.1016/j.patrec.2015.02.009Open 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.hmSearch 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),”in2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)201016Search 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 Sciences2682032Jun.2014Search 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,”in2013 6th International congress on image and signal processing (CISP)2013410415Search 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 Processing21422282244Apr.201210.1109/tip.2011Information 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