This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
E. Zhou, Z. Cao, and Q. Yin, “Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?,” Jan. 2015, Accessed: Nov. 11, 2022. [Online]. Available: http://arxiv.org/abs/1501.04690.ZhouE.CaoZ.YinQ.“Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?,”Jan.2015Accessed: Nov. 11, 2022. [Online]. Available: http://arxiv.org/abs/1501.04690.Search 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, 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 Letters128414419201910.1016/j.patrec.2019.10.002Open DOISearch in Google Scholar
S. Balaban, “Deep learning and face recognition: the state of the art,” in Biometric and Surveillance Technology for Human and Activity Identification XII, 2015, vol. 9457, p. 94570B, doi: 10.1117/12.2181526.BalabanS.“Deep learning and face recognition: the state of the art,”inBiometric and Surveillance Technology for Human and Activity Identification XII2015945794570B10.1117/12.2181526Open DOISearch in Google Scholar
Y. Sun, Y. Chen, X. Wang, and X. Tang, “Deep learning face representation by joint identification-verification,” in Advances in Neural Information Processing Systems, 2014, vol. 3, no. January, pp. 1988–1996, Accessed: Nov. 11, 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2014/hash/e5e63da79fcd2bebbd7cb8bf1c1d0274-Abstract.html.SunY.ChenY.WangX.TangX.“Deep learning face representation by joint identification-verification,”inAdvances in Neural Information Processing Systems20143January19881996Accessed: Nov. 11, 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2014/hash/e5e63da79fcd2bebbd7cb8bf1c1d0274-Abstract.html.Search in Google Scholar
P. R. Chowdhury, A. S. Wadhwa, and N. Tyagi, “Brain Inspired Face Recognition : A Computational Framework,” pp. 1–26, May 2021, Accessed: Nov. 11, 2022. [Online]. Available: http://arxiv.org/abs/2105.07237.ChowdhuryP. R.WadhwaA. S.TyagiN.“Brain Inspired Face Recognition : A Computational Framework,”126May2021Accessed: Nov. 11, 2022. [Online]. Available: http://arxiv.org/abs/2105.07237.Search in Google Scholar
S. Mao, D. Rajan, and L. T. Chia, “Deep residual pooling network for texture recognition,” Pattern Recognition, vol. 112, 2021, doi: 10.1016/j.patcog.2021.107817.MaoS.RajanD.ChiaL. T.“Deep residual pooling network for texture recognition,”Pattern Recognition112202110.1016/j.patcog.2021.107817Open DOISearch in Google Scholar
D. Franco, N. Navarin, M. Donini, D. Anguita, and L. Oneto, “Deep fair models for complex data: Graphs labeling and explainable face recognition,” Neurocomputing, vol. 470, pp. 318–334, 2022, doi: 10.1016/j.neucom.2021.05.109.FrancoD.NavarinN.DoniniM.AnguitaD.OnetoL.“Deep fair models for complex data: Graphs labeling and explainable face recognition,”Neurocomputing470318334202210.1016/j.neucom.2021.05.109Open DOISearch in Google Scholar
Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: A survey,” Sensors (Switzerland), vol. 20, no. 2. 2020, doi: 10.3390/s20020342.KortliY.JridiM.Al FalouA.AtriM.“Face recognition systems: A survey,”Sensors (Switzerland)202202010.3390/s20020342Open DOISearch in Google Scholar
N. Liu et al., “Super Wide Regression Network for Unsupervised Cross-Database Facial Expression Recognition,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2018, vol. 2018-April, pp. 1897–1901, doi: 10.1109/ICASSP.2018.8461322.LiuN.“Super Wide Regression Network for Unsupervised Cross-Database Facial Expression Recognition,”inICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings20182018-April1897190110.1109/ICASSP.2018.8461322Open DOISearch in Google Scholar
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,” hal.inria.fr. 2007, Accessed: Nov. 11, 2022. [Online]. Available: https://hal.inria.fr/inria-00321923/.HuangG. B.RameshM.BergT.Learned-MillerE.“Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,”hal.inria.fr. 2007, Accessed: Nov. 11, 2022. [Online]. Available: https://hal.inria.fr/inria-00321923/.Search in Google Scholar
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499–1503, 2016, doi: 10.1109/LSP.2016.2603342.ZhangK.ZhangZ.LiZ.QiaoY.“Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,”IEEE Signal Processing Letters231014991503201610.1109/LSP.2016.2603342Open DOISearch in Google Scholar
Q. Wang, P. Zhang, H. Xiong, and J. Zhao, “Face.evoLVe: A cross-platform library for high-performance face analytics,” Neurocomputing, vol. 494, pp. 443–445, Jul. 2022, doi: 10.1016/j.neucom.2022.04.118.WangQ.ZhangP.XiongH.ZhaoJ.“Face.evoLVe: A cross-platform library for high-performance face analytics,”Neurocomputing494443445Jul.202210.1016/j.neucom.2022.04.118Open DOISearch in Google Scholar
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol. 07-12-June, pp. 815–823, doi: 10.1109/CVPR.2015.7298682.SchroffF.KalenichenkoD.PhilbinJ.“FaceNet: A unified embedding for face recognition and clustering,”inProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition2015, vol. 07-12-June81582310.1109/CVPR.2015.7298682Open DOISearch in Google Scholar
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” in 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 4278–4284, doi: 10.1609/aaai.v31i1.11231.SzegedyC.IoffeS.VanhouckeV.AlemiA. A.“Inception-v4, inception-ResNet and the impact of residual connections on learning,”in31st AAAI Conference on Artificial Intelligence, AAAI 201720174278428410.1609/aaai.v31i1.11231Open DOISearch in Google Scholar