{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T21:38:46Z","timestamp":1767908326357,"version":"3.49.0"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030325824","type":"print"},{"value":"9783030325831","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-32583-1_6","type":"book-chapter","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T07:04:16Z","timestamp":1580195056000},"page":"99-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep Learning Models for Face Recognition: A Comparative Analysis"],"prefix":"10.1007","author":[{"given":"Arindam","family":"Chaudhuri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,29]]},"reference":[{"key":"6_CR1","unstructured":"M. Wang, W. Deng, Deep face recognition: a survey, arXiv:1804.06655v7 (2018)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"K. Grm, V. \u0160truc, A. Artiges, M. Caron, H.K. Ekenel, Strengths and weaknesses of deep learning models for face recognition against image degradations, arXiv:1710.01494v1 (2017)","DOI":"10.1049\/iet-bmt.2017.0083"},{"key":"6_CR3","volume-title":"Some Investigations on Deep Face Recognition Using Artificially Created Datasets, Technical Report TH-1696","author":"A Chaudhuri","year":"2016","unstructured":"A. Chaudhuri, Some Investigations on Deep Face Recognition Using Artificially Created Datasets, Technical Report TH-1696 (Samsung R & D Institute Delhi, Noida, 2016)"},{"key":"6_CR4","first-page":"41.1","volume-title":"Proceedings of British Machine Vision Conference","author":"OM Parkhi","year":"2015","unstructured":"O.M. Parkhi, A. Vedaldi, A. Zisserman, Deep face recognition, in Proceedings of British Machine Vision Conference, ed. by X. Xie, M. W. Jones, G. K. L. Tam, (BMVA Press, Surrey, 2015), pp. 41.1\u201341.12"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"K. Chatfield, K. Simonyan, A. Vedaldi, A. Zisserman, Return of the devil in the details: delving deep into convolutional nets. arXiv:1405.3531 (2014)","DOI":"10.5244\/C.28.6"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"G. Hu, Y. Yang, D. Yi, J. Kittler, W. Christmas, S.Z. Li, T. Hospedales, When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition, arXiv:1504.02351v1 (2015)","DOI":"10.1109\/ICCVW.2015.58"},{"issue":"35","key":"6_CR7","first-page":"19","volume":"8","author":"S Zhou","year":"2018","unstructured":"S. Zhou, S. Xiao, 3D face recognition: a survey. Hum. Centric Comput. Inf. Sci. 8(35), 19\u201345 (2018)","journal-title":"Hum. Centric Comput. Inf. Sci."},{"issue":"4","key":"6_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/computers5040021","volume":"5","author":"M Chihaoui","year":"2016","unstructured":"M. Chihaoui, A. Elkefi, W. Bellil, C.B. Amar, A survey of 2D face recognition techniques. Computers 5(4), 21 (2016)","journal-title":"Computers"},{"key":"6_CR9","volume-title":"2D and 3D Face Recognition Revisited Again, Technical Report TH-1486","author":"A Chaudhuri","year":"2014","unstructured":"A. Chaudhuri, 2D and 3D Face Recognition Revisited Again, Technical Report TH-1486 (Samsung R & D Institute Delhi, Noida, 2014)"},{"key":"6_CR10","volume-title":"Studying Face Recognition Using Convolutional Neural Networks, Technical Report TH-1669","author":"A Chaudhuri","year":"2016","unstructured":"A. Chaudhuri, Studying Face Recognition Using Convolutional Neural Networks, Technical Report TH-1669 (Samsung R & D Institute Delhi, Noida, 2016)"},{"key":"6_CR11","first-page":"1097","volume-title":"Proceedings of Neural Information Processing Systems","author":"A Krizhevsky","year":"2012","unstructured":"A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Proceedings of Neural Information Processing Systems, (MIT Press, Cambridge, 2012), pp. 1097\u20131105"},{"key":"6_CR12","first-page":"1701","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"Y Taigman","year":"2014","unstructured":"Y. Taigman, M. Yang, M.A. Ranzato, L. Wolf, DeepFace: closing the gap to human level performance in face verification, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Computer Society Press, Los Alamitos, 2014), pp. 1701\u20131708"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"key":"6_CR14","volume-title":"Proceedings of 9th International Conference on Digital Image Processing","author":"S Chaib","year":"2017","unstructured":"S. Chaib, H. Yao, Y. Gu, M. Amrani, Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models, in Proceedings of 9th International Conference on Digital Image Processing, (IEEE, Piscataway, 2017)"},{"issue":"4","key":"6_CR15","doi-asserted-by":"publisher","first-page":"712","DOI":"10.3390\/s17040712","volume":"17","author":"Y Liu","year":"2017","unstructured":"Y. Liu, Y. Li, X. Ma, R. Song, Facial expression recognition with fusion features extracted from salient facial areas. Sensors (Basel) 17(4), 712 (2017)","journal-title":"Sensors (Basel)"},{"issue":"8","key":"6_CR16","doi-asserted-by":"publisher","first-page":"4042","DOI":"10.1109\/TIP.2017.2713940","volume":"26","author":"J Lu","year":"2017","unstructured":"J. Lu, G. Wang, J. Zhou, Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans. Image Process. 26(8), 4042\u20134054 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"6_CR17","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TIP.2017.2756450","volume":"27","author":"G Hu","year":"2018","unstructured":"G. Hu, X. Peng, Y. Yang, T.M. Hospedales, J. Verbeek, Frankenstein: learning deep face representations using small data. IEEE Trans. Image Process. 27(1), 293\u2013303 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR18","first-page":"1717","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"M Oquab","year":"2014","unstructured":"M. Oquab, L. Bottou, I. Laptev, Learning and transferring mid-level image representations using convolutional neural networks, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Service Center, Piscataway, 2014), pp. 1717\u20131724"},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.imavis.2017.01.011","volume":"65","author":"V Campos","year":"2017","unstructured":"V. Campos, B. Jou, X. Giro-i-Nieto, From pixels to sentiment: fine-tuning CNNs for visual sentiment prediction. Image Vis. Comput. 65, 15\u201322 (2017)","journal-title":"Image Vis. Comput."},{"key":"6_CR20","first-page":"7","volume-title":"Proceedings of 2nd International Conference on Contemporary Computing and Informatics","author":"V Nagori","year":"2016","unstructured":"V. Nagori, Fine tuning the parameters of back propagation algorithm for optimum learning performance, in Proceedings of 2nd International Conference on Contemporary Computing and Informatics, (IEEE, Piscataway, 2016), pp. 7\u201312"},{"key":"6_CR21","first-page":"2918","volume-title":"Proceedings of 23rd International Conference on Pattern Recognition","author":"M Tzelepi","year":"2016","unstructured":"M. Tzelepi, A. Tefas, Exploiting supervised learning for finetuning deep CNNs in content-based image retrieval, in Proceedings of 23rd International Conference on Pattern Recognition, (IEEE, Piscataway, 2016), pp. 2918\u20132923"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.patcog.2016.10.019","volume":"63","author":"Y Li","year":"2016","unstructured":"Y. Li, W. Xie, H. Li, Hyperspectral image reconstruction by deep convolutional neural network for classification. Pattern Recogn. 63, 371\u2013383 (2016)","journal-title":"Pattern Recogn."},{"issue":"9","key":"6_CR23","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"W. Rawat, Z. Wang, Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29(9), 2352\u20132449 (2017)","journal-title":"Neural Comput."},{"key":"6_CR24","first-page":"249","volume":"9","author":"X Glorot","year":"2010","unstructured":"X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249\u2013256 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR25","first-page":"2217","volume-title":"Proceedings of International Conference on Machine Learning","author":"W Shang","year":"2016","unstructured":"W. Shang, K. Sohn, D. Almeida, H. Lee, Understanding and improving convolutional neural networks via concatenated rectified linear units, in Proceedings of International Conference on Machine Learning, (IEEE, Piscataway, 2016), pp. 2217\u20132225"},{"issue":"11","key":"6_CR26","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"6_CR27","first-page":"448","volume-title":"Proceedings of International Conference on Machine Learning","author":"S Ioffe","year":"2015","unstructured":"S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, in Proceedings of International Conference on Machine Learning, (IEEE Service Center, Piscataway, 2015), pp. 448\u2013456"},{"issue":"7","key":"6_CR28","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.3390\/s18072080","volume":"18","author":"J Li","year":"2018","unstructured":"J. Li, T. Qiu, C. Wen, K. Xie, F.Q. Wen, Robust face recognition using the deep C2D-CNN model based on decision level fusion. Sensors (Basel) 18(7), 2080 (2018)","journal-title":"Sensors (Basel)"},{"issue":"3","key":"6_CR29","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1109\/TIP.2011.2168413","volume":"21","author":"JY Choi","year":"2012","unstructured":"J.Y. Choi, Y.M. Ro, K.N. Plataniotis, Color local texture features for color face recognition. IEEE Trans. Image Process. 21(3), 1366\u20131380 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR30","first-page":"1857","volume-title":"Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing","author":"Z Lu","year":"2017","unstructured":"Z. Lu, X. Jiang, A. Kot, A novel LBP-based color descriptor for face recognition, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, (IEEE Service Center, Piscataway, 2017), pp. 1857\u20131861"},{"key":"6_CR31","first-page":"849","volume-title":"Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing","author":"Z Lu","year":"2016","unstructured":"Z. Lu, X. Jiang, A. Kot, An effective color space for face recognition, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, (IEEE Service Center, Piscataway, 2016), pp. 849\u2013856"},{"issue":"11","key":"6_CR32","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1109\/LSP.2015.2438024","volume":"22","author":"Z Lu","year":"2015","unstructured":"Z. Lu, X. Jiang, A. Kot, A color channel fusion approach for face recognition. IEEE Signal Process. Lett. 22(11), 1839\u20131843 (2015)","journal-title":"IEEE Signal Process. Lett."},{"key":"6_CR33","first-page":"770","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"K He","year":"2016","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Service Center, Piscataway, 2016), pp. 770\u2013778"},{"key":"6_CR34","first-page":"1891","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"Y Sun","year":"2014","unstructured":"Y. Sun, X. Wang, X. Tang, Deep learning face representation from predicting 10,000 classes, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Service Center, Piscataway, 2014), pp. 1891\u20131898"},{"key":"6_CR35","first-page":"1388","volume-title":"Proceedings of Neural Information Processing Systems","author":"O Tadmor","year":"2016","unstructured":"O. Tadmor, Y. Wexler, T. Rosenwein, S. Shalevshwartz, A. Shashua, Learning a metric embedding for face recognition using the multi batch method, in Proceedings of Neural Information Processing Systems, (MIT Press, Cambridge, 2016), pp. 1388\u20131389"},{"key":"6_CR36","first-page":"499","volume-title":"Proceedings of European Conference on Computer Vision","author":"Y Wen","year":"2016","unstructured":"Y. Wen, K. Zhang, Z. Li, Y. Qiao, A discriminative feature learning approach for deep face recognition, in Proceedings of European Conference on Computer Vision, (Springer Nature, Cham, 2016), pp. 499\u2013515"},{"issue":"5","key":"6_CR37","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1049\/iet-ipr.2017.1085","volume":"12","author":"Y Zhang","year":"2018","unstructured":"Y. Zhang, K. Shang, J. Wang, N. Lia, M.M.Y. Zhang, Patch strategy for deep face recognition. IET Image Process. 12(5), 819\u2013825 (2018)","journal-title":"IET Image Process."},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"F. Schroff, D. Kalenichenko, J. Philbin, FaceNet: a unified embedding for face recognition and clustering, arXiv:1503.03832v3 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"6_CR39","first-page":"1","volume-title":"Proceedings of 8th IEEE International Conference on Quality of Multimedia Experience","author":"S Dodge","year":"2016","unstructured":"S. Dodge, L. Karam, Understanding how image quality affects deep neural networks, in Proceedings of 8th IEEE International Conference on Quality of Multimedia Experience, (IEEE Service Center, Piscataway, 2016), pp. 1\u20136"},{"key":"6_CR40","unstructured":"B.R. Webster, S.E. Anthony, W.J. Scheirer, PsyPhy: a psychophysics driven evaluation framework for visual recognition, arXiv:1611.06448 (2016)"},{"key":"6_CR41","first-page":"766","volume-title":"Proceedings of Neural Information Processing Systems","author":"A Dosovitskiy","year":"2014","unstructured":"A. Dosovitskiy, J.T. Springenberg, M. Riedmiller, T. Brox, Discriminative unsupervised feature learning with convolutional neural networks, in Proceedings of Neural Information Processing Systems, (MIT Press, Cambridge, 2014), pp. 766\u2013774"},{"key":"6_CR42","first-page":"818","volume-title":"Proceedings of European Conference on Computer Vision","author":"MD Zeiler","year":"2014","unstructured":"M.D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in Proceedings of European Conference on Computer Vision, (Springer International Publishing, Cham, 2014), pp. 818\u2013833"},{"key":"6_CR43","first-page":"991","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"K Lenc","year":"2015","unstructured":"K. Lenc, A. Vedaldi, Understanding image representations by measuring their equivariance and equivalence, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Computer Society, Los Alamitos, 2015), pp. 991\u2013999"},{"issue":"1","key":"6_CR44","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/MSP.2017.2764116","volume":"35","author":"R Ranjan","year":"2018","unstructured":"R. Ranjan, S. Sankaranarayanan, A. Bansal, N. Bodla, J.C. Chen, V.M. Patel, C.D. Castillo, R. Chellappa, Deep learning for understanding faces: machines may be just as good, or better than humans. IEEE Signal Process. Mag. 35(1), 66\u201383 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"6_CR45","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, arXiv:1709.01507 (2017)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"6_CR46","volume-title":"Face Detection Using Deformable Parts Models, Technical Report, TH-1679","author":"A Chaudhuri","year":"2016","unstructured":"A. Chaudhuri, Face Detection Using Deformable Parts Models, Technical Report, TH-1679 (Samsung R & D Institute Delhi, Noida, 2016)"},{"key":"6_CR47","first-page":"5325","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"H Li","year":"2015","unstructured":"H. Li, Z. Lin, X. Shen, J. Brandt, G. Hua, A convolutional neural network cascade for face detection, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Computer Society, Los Alamitos, 2015), pp. 5325\u20135334"},{"key":"6_CR48","first-page":"3476","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"Y Sun","year":"2013","unstructured":"Y. Sun, X. Wang, X. Tang, Deep convolutional network cascade for facial point detection, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Computer Society, Los Alamitos, 2013), pp. 3476\u20133483"},{"key":"6_CR49","first-page":"532","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"X Xiong","year":"2013","unstructured":"X. Xiong, F.D.L. Torre, Supervised descent method and its applications to face alignment, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (IEEE Computer Society, Los Alamitos, 2013), pp. 532\u2013539"},{"key":"6_CR50","first-page":"1988","volume-title":"Proceedings of Neural Information Processing Systems","author":"Y Sun","year":"2014","unstructured":"Y. Sun, Y. Chen, X. Wang, X. Tang, Deep learning face representation by joint identification-verification, in Proceedings of Neural Information Processing Systems, (MIT Press, London, 2014), pp. 1988\u20131996"},{"key":"6_CR51","unstructured":"Y. Sun, D. Liang, X. Wang, X. Tang, DeepID3: face recognition with very deep neural networks. arXiv:1502.00873v1 (2015)"},{"key":"6_CR52","unstructured":"E. Zhou, Z. Cao, Q. Yin, Naive-deep face recognition: touching the limit of LFW benchmark or not? arXiv:1501.04690v1 (2015)"}],"container-title":["Unsupervised and Semi-Supervised Learning","Deep Biometrics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32583-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T07:16:34Z","timestamp":1580195794000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-32583-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030325824","9783030325831"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32583-1_6","relation":{},"ISSN":["2522-848X","2522-8498"],"issn-type":[{"value":"2522-848X","type":"print"},{"value":"2522-8498","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}