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Biometrics recognition using deep learning: a survey

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Abstract

In the past few years, deep learning-based models have been very successful in achieving state-of-the-art results in many tasks in computer vision, speech recognition, and natural language processing. These models seem to be a natural fit for handling the ever-increasing scale of biometric recognition problems, from cellphone authentication to airport security systems. Deep learning-based models have increasingly been leveraged to improve the accuracy of different biometric recognition systems in recent years. In this work, we provide a comprehensive survey of more than 150 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which deploy deep learning models, and show their strengths and potentials in different applications. For each biometric, we first introduce the available datasets that are widely used in the literature and their characteristics. We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks. We will also discuss some of the main challenges while using these models for biometric recognition, and possible future directions to which research in this area is headed.

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Notes

  1. In this paper, we commonly refer to a biometric characteristic as biometric for short.

References

  • Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2d and 3d face recognition: a survey. Pattern Recognit Lett 28(14):1885–1906

    Google Scholar 

  • Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev 2(4):433–459

    Google Scholar 

  • Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for Boltzmann machines. Cognit Sci 9(1):147–169

    Google Scholar 

  • Ahmad S, Fuller B (2019) Thirdeye: Triplet based iris recognition without normalization. arXiv preprint arXiv:1907.06147

  • Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: European conference on computer vision, pp 469–481. Springer

  • Alaslani M, Elrefaei L (2018) Convolutional neural network-based feature extraction for iris recognition. Int J Comp Sci Info Tech 10:65–78

    Google Scholar 

  • Alotaibi M, Mahmood A (2017) Improved gait recognition based on specialized deep convolutional neural network. Comput Vis Image Understand 164:103–110

    Google Scholar 

  • Alvarez-Betancourt Y, Garcia-Silvente M (2016) A keypoints-based feature extraction method for iris recognition under variable image quality conditions. Knowl-Based Syst 92:169–182

    Google Scholar 

  • Anand V, Kanhangad V (2020) Porenet: Cnn-based pore descriptor for high-resolution fingerprint recognition. IEEE Sens J 20(16):9305–9313

    Google Scholar 

  • Awe ear dataset. http://awe.fri.uni-lj.si/home

  • Baltrusaitis T, Ahuja C, Morency LP (2018) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41(2):423–443

    Google Scholar 

  • Baqar M, Ghani A, Aftab A, Arbab S, Yasin S (2016) Deep belief networks for iris recognition based on contour detection. In: 2016 International Conference on Open Source Systems & Technologies (ICOSST), pp 72–77. IEEE

  • Battistone F, Petrosino A (2019) Tglstm: a time based graph deep learning approach to gait recognition. Pattern Recognit Lett 126:132–138

    Google Scholar 

  • Berg T, Belhumeur PN (2012) Tom-vs-pete classifiers and identity-preserving alignment for face verification. In : BMVC, vol 2, pp 7. Citeseer

  • Bhattacharya G, Alam J, Kenny P (2019) Deep speaker recognition: modular or monolithic? Proc Interspeech 2019:1143–1147

    Google Scholar 

  • Borgen H, Bours P, Wolthusen SD (2008) Visible-spectrum biometric retina recognition. In: Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE

  • Bottou L (1991) Stochastic gradient learning in neural networks. Proc Neuro-Nımes 91(8):12

    Google Scholar 

  • Bowyer KW, Burge MJ (2016) Handbook of Iris recognition. Springer, New York

    Google Scholar 

  • Brunner C, Fischer A, Luig K, Thies T (2012) Pairwise support vector machines and their application to large scale problems. J Mach Learn Res 13(Aug):2279–2292

    MathSciNet  MATH  Google Scholar 

  • Cai W, Chen J, Li M (2018) Exploring the encoding layer and loss function in end-to-end speaker and language recognition system. arXiv preprint arXiv:1804.05160

  • Campbell JP (1997) Speaker recognition: a tutorial. Proc IEEE 85(9):1437–1462

    Google Scholar 

  • Cao Q, Ying Y, Li P (2013) Similarity metric learning for face recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2408–2415

  • Casia fingerprint dataset. http://biometrics.idealtest.org/dbDetailForUser.do?id=7

  • Casia iris dataset. http://biometrics.idealtest.org/findTotalDbByMode.do?mode=Iris

  • Casia palmprint dataset. http://www.cbsr.ia.ac.cn/english/Palmprint%20Databases.asp

  • Casia gait database. http://www.cbsr.ia.ac.cn/users/szheng/?page_id=71

  • Chen X, Luo X, Weng J, Luo W, Li H, Tian Qi (2021) Multi-view gait image generation for cross-view gait recognition. IEEE Trans Image Process 30:3041–3055

    Google Scholar 

  • Chen X, Weng J, Wei L, Jiaming X (2017) Multi-gait recognition based on attribute discovery. IEEE Trans Pattern Anal Mach Intell 40(7):1697–1710

    Google Scholar 

  • Chen J, Zhang C, Rong G (2001) Palmprint recognition using crease. In: Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205), vol 3, pp 234–237. IEEE

  • Chen D, Cao X, Wang L, Wen F, Sun J (2012) Bayesian face revisited: a joint formulation. In: European conference on computer vision, pp 566–579. Springer

  • Chung JS, Nagrani A, Zisserman A (2018) Voxceleb2: deep speaker recognition. arXiv preprint arXiv:1806.05622

  • Cieri C, David M, Kevin W (2004) The fisher corpus: a resource for the next generations of speech-to-text. ILREC 4:69–71

    Google Scholar 

  • Connie T, Teoh A, Goh M, Ngo D (2003) Palmprint recognition with PCA and ICA. In Proc. Image and Vision Computing

  • Cummins H (1941) Ancient finger prints in clay. J Crimi Law Criminol (1931–D1951) 32(4):468–481

    Google Scholar 

  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection

  • Darlow LN, Rosman B (2017) Fingerprint minutiae extraction using deep learning. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp 22–30. IEEE

  • Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161

    Google Scholar 

  • Daugman J (2009) How iris recognition works. In : The essential guide to image processing, pp 715–739. Elsevier

  • Dehak N, Kenny PJ, Dehak R, Dumouchel P, Ouellet P (2010) Front-end factor analysis for speaker verification. IEEE Trans Audio Speech Language Process 19(4):788–798

    Google Scholar 

  • Deng J, Guo J, Xue N, Zafeiriou S (2019) Arcface: additive angular margin loss for deep face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition

  • Deng J, Guo J, Liu T, Gong M, Zafeiriou Sd (2020) Sub-center arcface: Boosting face recognition by large-scale noisy web faces. In: European Conference on Computer Vision, pp 741–757. Springer

  • Deng J, Zhou Y, Zafeiriou S (2017) Marginal loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 60–68

  • Deng W, Hu J, Guo J (2013) In defense of sparsity based face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 399–406

  • Duan Y, Lu J, Zhou J (2019) Uniformface: learning deep equidistributed representation for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 3415–3424

  • Elhoseny M, Nabil A, Hassanien A, Oliva D (2018) Hybrid rough neural network model for signature recognition. In: Advances in Soft Computing and Machine Learning in Image Processing, pp 295–318. Springer

  • Emersic Z, Stepec D, Struc V, Peer P, George A, Ahmad A, Omar E, Boult TE, Safdaii R, Zhou Y, et al. (2017) The unconstrained ear recognition challenge. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp 715–724. IEEE

  • Emersic Z, Stepec D, Struc V, Peer P (2017) Training convolutional neural networks with limited training data for ear recognition in the wild. In: International Conference on Automatic Face & Gesture Recognition, pp 987–994. IEEE

  • Emeršič Ž, Štruc V, Peer P (2017) Ear recognition: more than a survey. Neurocomputing 255:26–39

    Google Scholar 

  • Emeršič Ž, Štepec D, Štruc V, Peer P (2017) Training convolutional neural networks with limited training data for ear recognition in the wild. arXiv preprint arXiv:1711.09952

  • Emeršič Ž, Playà NO, Štruc V, Peer P (2018) Towards accessories-aware ear recognition. In 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp 1–8. IEEE

  • Eskimez S, Maddox RK, Xu C, Duan Z (2018) Generating talking face landmarks from speech. In: Conference on Latent Variable Analysis and Signal Separation. Springer

  • Extended yale face database b (b+). http://vision.ucsd.edu/content/extended-yale-face-database-b-b

  • Eyiokur FI, Yaman D, Ekenel HK (2017) Domain adaptation for ear recognition using deep convolutional neural networks. IET Biom 7(3):199–206

    Google Scholar 

  • Fan C, Peng Y, Cao C, Liu X, Hou S, Chi J, Huang Y, Li Q, He Z (2020) Gaitpart: temporal part-based model for gait recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 14225–14233

  • Fang M, Damer N, Kirchbuchner F, Kuijper A (2021) Demographic bias in presentation attack detection of iris recognition systems. In 2020 28th European Signal Processing Conference (EUSIPCO), pp 835–839. IEEE

  • Farouk RM (2011) Iris recognition based on elastic graph matching and gabor wavelets. Comput Vis Image Understand 115(8):1239–1244

    Google Scholar 

  • Frejlichowski D, Tyszkiewicz N (2010) The west pomeranian university of technology ear database–a tool for testing biometric algorithms. In: International Conference Image Analysis and Recognition, pp 227–234. Springer

  • Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybernet 36(4):193–202

    MathSciNet  MATH  Google Scholar 

  • Fvc fingerprint dataset. http://bias.csr.unibo.it/fvc2002/

  • Galbally J, Cappelli R, Lumini A, Maltoni D, Fierrez J (2008) Fake fingertip generation from a minutiae template. In: International Conference on Pattern Recognition. IEEE

  • Gangwar A, Joshi A (2016) Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition. In 2016 IEEE International Conference on Image Processing (ICIP), pp 2301–2305. IEEE

  • Garcia-Romero D, Snyder D, Sell G, Povey D, McCree A (2017) Speaker diarization using deep neural network embeddings. In: International Conference on Acoustics, Speech and Signal Processing, pp 4930–4934. IEEE

  • Garris MD, McCabe RM (2000) Fingerprint minutiae from latent and matching tenprint images. In: Tenprint Images”, National Institute of Standards and Technology. Citeseer

  • Godfrey J, Holliman E (1997) Switchboard-1 release 2: Linguistic data consortium. A User’s Manual, SWITCHBOARD

  • Gonzalez-Sanchez E (2008) Biometria de la oreja. PhD thesis, Ph. D. thesis, Universidad de Las Palmas de Gran Canaria

  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  • Gross R, Matthews I, Cohn J, Kanade T, Baker S (2010) Multi-pie. Image Vis Comput 28(5):807–813

    Google Scholar 

  • Guo Y, Lei Y, Liu L, Wang Y, Bennamoun M, Sohel F (2016) Ei3d: Expression-invariant 3d face recognition based on feature and shape matching. Pattern Recognit Lett 83:403–412

    Google Scholar 

  • Guo Y, Zhang L, Hu Y, He X, Gao J (2016) Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In: European Conference on Computer Vision, pp 87–102. Springer

  • http://colah.github.io/posts/2015-08-Understanding-LSTMs/

  • https://deepfakedetectionchallenge.ai/

  • https://github.com/hindupuravinash/the-gan-zoo

  • Hafemann LG, Sabourin R, Oliveira LS (2017) Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognit 70:163–176

    Google Scholar 

  • Hafemann LG, Sabourin R, Oliveira LS (2016) Writer-independent feature learning for offline signature verification using deep convolutional neural networks. In: International Joint Conference on Neural Networks, pp 2576–2583. IEEE

  • Haghighat M, Abdel-Mottaleb M, Alhalabi W (2016) Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans Inform Forensics Secur 11(9):1984–1996

    Google Scholar 

  • Hajibabaei M, Dai D (2018) Unified hypersphere embedding for speaker recognition. arXiv preprint arXiv:1807.08312

  • Han J, Bhanu B (2005) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322

    Google Scholar 

  • Hansley EE, Segundo MP, Sarkar S (2018) Employing fusion of learned and handcrafted features for unconstrained ear recognition. IET Biom 7(3):215–223

    Google Scholar 

  • Hayat M, Bennamoun M, An S (2014) Deep reconstruction models for image set classification. IEEE Trans Pattern Anal Mach Intell 37(4):713–727

    Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  • Heigold G, Moreno I, Bengio S, Shazeer N (2016) End-to-end text-dependent speaker verification. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Google Scholar 

  • Hofbauer H, Jalilian E, Uhl A (2019) Exploiting superior cnn-based iris segmentation for better recognition accuracy. Pattern Recognit Lett 120:17–23

    Google Scholar 

  • Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  • Hrechak AK, McHugh JA (1990) Automated fingerprint recognition using structural matching. Pattern Recognit 23(8):893–904

    Google Scholar 

  • Huang Y, Wang Y, Tai Y, Liu X, Shen P, Li S, Li J, Huang F (2020) Curricularface: adaptive curriculum learning loss for deep face recognition. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5901–5910

  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition, pp 4700–4708

  • Icdar svc (2009) http://tc11.cvc.uab.es/datasets/SigComp2009_1

  • Iit iris dataset. https://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm

  • Iit palmprint dataset. https://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.htm

  • Impedovo D, Pirlo G (2008) Automatic signature verification: the state of the art. IEEE Transa Syst Man Cybernet Part C 38(5):609–635

    Google Scholar 

  • Ivakhnenko AG, Lapa VG (1966) Cybernetic predicting devices. Technical report, Purdue Univ, School of Electrical Engineering

  • Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inform Forensics Secur 7(5):1511–1521

    Google Scholar 

  • Izadpanahkakhk M, Razavi S, Gorjikolaie M, Zahiri S, Uncini A (2018) Deep region of interest and feature extraction models for palmprint verification using convolutional neural networks transfer learning. Appl Sci 8(7):1210

    Google Scholar 

  • Jain A, Hong L, Bolle R (1997) On-line fingerprint verification. IEEE Trans Pattern Anal Mach Intell 19(4):302–314

    Google Scholar 

  • Jain Anil, Hong Lin, Pankanti Sharath (2000) Biometric identification. Commun ACM 43(2):90–98

    Google Scholar 

  • Jain AK, Li SZ (2011) Handbook of face recognition. Springer, New York

    MATH  Google Scholar 

  • Jain AK, Ross A, Prabhakar S et al (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20

    Google Scholar 

  • Jalali A, Mallipeddi R, Lee M (2015) Deformation invariant and contactless palmprint recognition using convolutional neural network. In: Conference on Human-Agent Interaction. ACM

  • Jin Andrew Teoh Beng, Ling David Ngo Chek, Song Ong Thian (2004) An efficient fingerprint verification system using integrated wavelet and fourier–mellin invariant transform. Image Vis Comput 22(6):503–513

    Google Scholar 

  • Jing L, Tian Y (2019) Self-supervised visual feature learning with deep neural networks: a survey. arXiv preprint arXiv:1902.06162

  • Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196

  • Kemelmacher-Shlizerman I, Seitz S, Miller D, Brossard E (2016) The megaface benchmark: 1 million faces for recognition at scale. In: IEEE Conference on Computer Vision and Pattern Recognition

  • Kim S, Park B, Song BS, Yang S (2016) Deep belief network based statistical feature learning for fingerprint liveness detection. Pattern Recognit Lett 77:58–65

    Google Scholar 

  • Kong WK, Zhang D, Li W (2003) Palmprint feature extraction using 2-d gabor filters. Pattern Recognit 36(10):2339–2347

    Google Scholar 

  • Krizhevsky A, Sutskever I, HGE Imagenet (2012) classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  • Kumar A, Chenye W (2012) Automated human identification using ear imaging. Pattern Recognit 45(3):956–968

    Google Scholar 

  • Kumar A, Passi Arun (2010) Comparison and combination of iris matchers for reliable personal authentication. Pattern Recognit 43(3):1016–1026

    MATH  Google Scholar 

  • Kumari P, Seeja KR (2019) Periocular biometrics: a survey. J King Saud Univ-Comput Inform Sci

  • Kusakunniran W (2014) Recognizing gaits on spatio-temporal feature domain. IEEE Trans Inform Forensics Secur 9(9):1416–1423

    Google Scholar 

  • Kusakunniran W, Qiang W, Zhang J, Li H, Wang L (2013) Recognizing gaits across views through correlated motion co-clustering. IEEE Trans Image Process 23(2):696–709

    MathSciNet  MATH  Google Scholar 

  • Kushwaha V, Singh M, Singh R, Vatsa M, Ratha N, Chellappa R (2018) Disguised faces in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 1–9

  • Labeled faces in the wild. http://vis-www.cs.umass.edu/lfw/

  • Lai JH, Yuen PC, Feng GC (2001) Face recognition using holistic fourier invariant features. Pattern Recognit 34(1):95–109

    MATH  Google Scholar 

  • Le N, Odobez J-M (2018) Robust and discriminative speaker embedding via intra-class distance variance regularization. In: Interspeech, pp 2257–2261

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Google Scholar 

  • LeCun Y, Bottou L, Bengio Y, Haffner P et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Google Scholar 

  • Lee MB, Kim YH, Park KR (2019) Conditional generative adversarial network-based data augmentation for enhancement of iris recognition accuracy. IEEE Access 7:122134–122152

    Google Scholar 

  • Lee C-J, Wang S-D (1999) Fingerprint feature extraction using gabor filters. Electron Lett 35(4):288–290

    Google Scholar 

  • Lei Y, Scheffer N, Ferrer L, McLaren M (2014) A novel scheme for speaker recognition using a phonetically-aware deep neural network. In International Conference on Acoustics, Speech and Signal Processing, pp 1695–1699. IEEE

  • Lg iris. https://cvrl.nd.edu/projects/data/

  • Li C, Min X, Sun S, Lin W, Tang Z (2017) Deepgait: a learning deep convolutional representation for view-invariant gait recognition using joint Bayesian. Appl Sci 7(3):210

    Google Scholar 

  • Li X, Makihara Y, Xu C, Yagi Y, Ren M(2020) Gait recognition via semi-supervised disentangled representation learning to identity and covariate features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 13309–13319,

  • Li Y, Lyu S (2018) Exposing deepfake videos by detecting face warping artifacts. arXiv preprint arXiv:1811.00656, 2

  • Liao R, Shiqi Y, An W, Huang Y (2020) A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognit 98:107069

    Google Scholar 

  • Lin C, Kumar A (2018) Contactless and partial 3d fingerprint recognition using multi-view deep representation. Pattern Recognit 83:314–327

    Google Scholar 

  • Lin C, Kumar A (2017) Multi-siamese networks to accurately match contactless to contact-based fingerprint images. In: International Joint Conference on Biometrics (IJCB), pp 277–285. IEEE

  • Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Sphereface: Deep hypersphere embedding for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 212–220

  • Liu H, Zhu X, Lei Z, Li SZ (2019) Adaptiveface: adaptive margin and sampling for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 11947–11956

  • Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision, pp 3730–3738

  • Liu W, Wen Y, Yu Z, Yang M (2016) Large-margin softmax loss for convolutional neural networks. In: ICML, vol 2, p 7,

  • Liu Y, Li H, Wang X (2017) Rethinking feature discrimination and polymerization for large-scale recognition. preprint, arXiv:1710.00870

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Google Scholar 

  • Lu G, Zhang D, Wang K (2003) Palmprint recognition using eigenpalms features. Pattern Recog Lett 24(9–10):1463–1467

    MATH  Google Scholar 

  • Makihara Y, Mannami H, Tsuji A, Hossain MA, Sugiura K, Mori A, Yagi Y (2012) The ou-isir gait database comprising the treadmill dataset. IPSJ Trans Comput Vis Appl 4:53–62

    Google Scholar 

  • De Marsico M, Nappi M, Riccio D, Wechsler H (2015) Mobile iris challenge evaluation (miche)-i, biometric iris dataset and protocols. Pattern Recognit Lett 57:17–23

    Google Scholar 

  • De Marsico M, Petrosino A, Ricciardi S (2016) Iris recognition through machine learning techniques: a survey. Pattern Recognit Lett 82:106–115

    Google Scholar 

  • Martin AF, Przybocki MA (2001) The nist speaker recognition evaluations: 1996–2001. In: 2001: A Speaker Odyssey-The Speaker Recognition Workshop

  • Maze B, Adams J, Duncan JA, Kalka N, Miller T, Otto C, Jain AK, Niggel WT, Anderson J, Cheney J, et al. (2018) Iarpa janus benchmark-c: Face dataset and protocol. In: 2018 International Conference on Biometrics (ICB), pp 158–165. IEEE

  • McLaren M, Ferrer L, Castan D, Lawson A (2016) The speakers in the wild (sitw) speaker recognition database. In: Interspeech, pp 818–822

  • Menon H, Mukherjee A (2018) Iris biometrics using deep convolutional networks. In: 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp 1–5. IEEE

  • Mian A, Bennamoun M, Owens R (2007) An efficient multimodal 2d-3d hybrid approach to automatic face recognition. IEEE Trans Pattern Anal Mach Intell 29(11):1927–1943

    Google Scholar 

  • Michele A, Colin V, Santika DD (2019) Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Comput Sci 157:110–117

    Google Scholar 

  • Minaee S, Azimi E, Abdolrashidi A (2019) Fingernet: pushing the limits of fingerprint recognition using convolutional neural network. arXiv preprint arXiv:1907.12956

  • Minaee S, Abdolrashidi AA, Wang Y (2015) Iris recognition using scattering transform and textural features. In: Signal processing and signal processing education workshop, pp 37–42. IEEE

  • Minaee S, Abdolrashidiy A, Wang Y (2016) An experimental study of deep convolutional features for iris recognition. In: Signal processing in medicine and biology symposium, pp 1–6. IEEE

  • Minaee S, Abdolrashidi A (2019) Deepiris: Iris recognition using a deep learning approach. arXiv preprint arXiv:1907.09380

  • Minaee S, Wang Y (2017) Palmprint recognition using deep scattering network. In: International Symposium on Circuits and Systems (ISCAS). IEEE

  • Mo H, Chen B, Luo W (2018) Fake faces identification via convolutional neural network. In: Information Hiding and Multimedia Security. ACM

  • Monrose F, Rubin AD (2000) Keystroke dynamics as a biometric for authentication. Future Generation Comput Syst 16(4):351–359

    Google Scholar 

  • Mu Z, Yuan L, Xu Z, Xi D, Qi S(2004) Shape and structural feature based ear recognition. In: Chinese Conference on Biometric Recognition, pp 663–670. Springer

  • Muramatsu D, Makihara Y, Yagi Y (2015) View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans Cybernet 46(7):1602–1615

    Google Scholar 

  • Muramatsu D, Makihara Y, Yagi Y (2015) Cross-view gait recognition by fusion of multiple transformation consistency measures. IET Biom 4(2):62–73

    Google Scholar 

  • Mustafa BY, Berrin Y (2016) Score level fusion of classifiers in off-line signature verification. Inform Fusion 32:109–119

    Google Scholar 

  • Nagrani A, Chung JS, Zisserman A (2017) Voxceleb: a large-scale speaker identification dataset. arXiv preprint arXiv:1706.08612

  • Naseem I, Togneri R, Bennamoun M (2008) Sparse representation for ear biometrics. In: International Symposium on Visual Computing, pp 336–345. Springer

  • Nogueira RF, de Alencar Lotufo R, Machado RC (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inform Forensics Secur 11(6):1206–1213

    Google Scholar 

  • Okabe K, Koshinaka T, Shinoda K (2018) Attentive statistics pooling for deep speaker embedding. arXiv preprint arXiv:1803.10963

  • Omar R, Han T, Al-Sumaidaee SAM, Chen T (2018) Deep finger texture learning for verifying people. IET Biomet 8(1):40–48

    Google Scholar 

  • Omara I, Xiaohe W, Zhang H, Yong D, Zuo W (2018) Learning pairwise SVM on hierarchical deep features for ear recognition. IET Biom 7(6):557–566

    Google Scholar 

  • Osaka gait database. http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitTM.html

  • Panayotov V, Chen G, Povey D, Khudanpur S (2015) Librispeech: an asr corpus based on public domain audio books. In: International Conference on Acoustics, Speech and Signal Processing. IEEE

  • Pandya B, Cosma G, Alani AA, Taherkhani A, Bharadi V, McGinnity TM (2018) Fingerprint classification using a deep convolutional neural network. In: 2018 4th International Conference on Information Management (ICIM), pp 86–91. IEEE

  • Parkhi OM, Vedaldi A, Zisserman A et al. (2015) Deep face recognition. In: bmvc, volume 1,

  • Perpinan C (1995) Compression neural networks for feature extraction: Application to human recognition from ear images. Master’s thesis, Faculty of Informatics, Technical University of Madrid

  • Polyu fingerprint dataset. http://www4.comp.polyu.edu.hk/~biometrics/HRF/HRF_old.htm

  • Polyu palmprint dataset. https://www4.comp.polyu.edu.hk/~biometrics/MultispectralPalmprint/MSP.htm

  • Polyu nir face database. http://www4.comp.polyu.edu.hk/~biometrics/polyudb_face.htm

  • Ranjan R, Castillo CD, Chellappa R (2017) L2-constrained softmax loss for discriminative face verification. arXiv preprint arXiv:1703.09507

  • Rantzsch H, Yang H, Meinel C (2016) Signature embedding: writer independent offline signature verification with deep metric learning. In: International symposium on visual computing

  • Ravanelli M, Bengio Y (2018) Learning speaker representations with mutual information. arXiv preprint arXiv:1812.00271

  • Ribeiro B, Gonçalves I, Santos S, Kovacec A (2011) Deep learning networks for off-line handwritten signature recognition. Springer, In: Iberoamerican Congress on Pattern Recognition

  • Rida I, Herault R, Marcialis GL, Gasso G (2019) Palmprint recognition with an efficient data driven ensemble classifier. Pattern Recognit Lett 126:21–30

    Google Scholar 

  • Ross A, Jain A (2003) Information fusion in biometrics. Pattern Recognit Lett 24(13):2115–2125

    Google Scholar 

  • Ross A, Jain AK (2004) Multimodal biometrics: an overview. In: 2004 12th European Signal Processing Conference, pp 1221–1224. IEEE

  • Rumelhart DE, Hinton GE, Williams RJ et al (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1

    MATH  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ et al (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1

    MATH  Google Scholar 

  • Samai D, Bensid K, Meraoumia A, Taleb-Ahmed A, Bedda M (2018) 2d and 3d palmprint recognition using deep learning method. In: IInternational Conference on Pattern Analysis and Intelligent Systems, pp 1–6. IEEE

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Google Scholar 

  • Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: IEEE conference on computer vision and pattern recognition, pp 815–823

  • Shao H, Zhong D (2019) Few-shot palmprint recognition via graph neural networks. Electron Lett 55(16):890–892

    Google Scholar 

  • Shao H, Zhong D, Du X (2019) Efficient deep palmprint recognition via distilled hashing coding. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops

  • Shao H, Zhong D, Du X (2019) Cross-domain palmprint recognition based on transfer convolutional autoencoder. In: International Conference on Image Processing, pp 1153–1157. IEEE

  • Shaver CD, Acken JM (2016) A brief review of speaker recognition technology

  • Shiraga K, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2016) Geinet: View-invariant gait recognition using a convolutional neural network. In: 2016 international conference on biometrics (ICB)

  • Shon S, Tang H, Glass J (2018) Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model. In: Spoken Language Technology Workshop (SLT). IEEE

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  • Sinha H, Manekar R, Sinha Y, Ajmera PK (2019) Convolutional neural network-based human identification using outer ear images. In: Soft Computing for Problem Solving, pp 707–719. Springer

  • Snyder D, Garcia-Romero D, Sell G, Povey D, Khudanpur S (2018) X-vectors: Robust dnn embeddings for speaker recognition. In: International Conference on Acoustics, Speech and Signal Processing. IEEE

  • Soleimani A, Araabi BN, Fouladi K (2016) Deep multitask metric learning for offline signature verification. Pattern Recognit Lett 80:84–90

    Google Scholar 

  • Souza VLF, Oliveira ALI, Sabourin R (2018) A writer-independent approach for offline signature verification using deep convolutional neural networks features. In: Brazilian Conference on Intelligent Systems (BRACIS), pp 212–217. IEEE

  • Srihari S, Xu A, Kalera M (2004) Learning strategies and classification methods for off-line signature verification. In: Workshop on Frontiers in Handwriting Recognition, pp 161–166. IEEE

  • Štepec D, Emeršič Ž, Peer P, Štruc V (2020) Constellation-based deep ear recognition. In Deep Biometrics, pp 161–190. Springer

  • Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1891–1898

  • Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems, pp 1988–1996

  • Sun Y, Liang D, Wang X, Tang X (2015) Deepid3: face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873

  • Sun Y, Wang X, Tang X, (2016) Sparsifying neural network connections for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4856–4864

  • Sundararajan K, Woodard DL (2018) Deep learning for biometrics: a survey. ACM Comput Surv (CSUR) 51(3):1–34

    Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition

  • Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: Closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701–1708

  • Tang Y, Gao F, Feng J, Liu Y (2017) Fingernet: an unified deep network for fingerprint minutiae extraction. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp 108–116. IEEE

  • The 2010 nist speaker recognition evaluation. (2010)

  • The 2018 nist speaker recognition evaluation. (2018)

  • The 2016 nist speaker recognition evaluation. (2016)

  • The cmu multi-pie face database. http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html

  • Thomee Bart, Shamma David A, Friedland Gerald, Elizalde Benjamin, Ni Karl, Poland Douglas, Borth D, Li L-J (2015) Yfcc100m: the new data in multimedia research. arXiv preprint arXiv:1503.01817

  • Tian L, Mu Z (2016) Ear recognition based on deep convolutional network. In: International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp 437–441. IEEE

  • Tico M, Kuosmanen P, Saarinen J (2001) Wavelet domain features for fingerprint recognition. Electron Lett 37(1):21–22

    Google Scholar 

  • Tolosana R, Vera-Rodriguez R, Fierrez J, Ortega-Garcia J (2018) Exploring recurrent neural networks for on-line handwritten signature biometrics. IEEE Access 6:5128–5138

    Google Scholar 

  • Ubiris iris dataset. http://iris.di.ubi.pt/

  • Ustb ear dataset. http://www1.ustb.edu.cn/resb/en/visit/visit.htm

  • Van Leeuwen DA, Brümmer N (2007) An introduction to application-independent evaluation of speaker recognition systems. I : Speaker classification I, pp 330–353. Springer

  • Vargas F, Ferrer M, Travieso C, Alonso J (2007) Off-line handwritten signature gpds-960 corpus. In: International Conference on Document Analysis and Recognition, vol 2, pp 764–768. IEEE

  • Variani E, Lei X, McDermott E, Moreno IL, Gonzalez-Dominguez J (2014) Deep neural networks for small footprint text-dependent speaker verification. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE

  • Vggface2. http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/

  • Vorugunti C, Pulabaigari V, Gorthi RKSS, Mukherjee P (2020) Osvfusenet: online signature verification by feature fusion and depth-wise separable convolution based deep learning. Neurocomputing 409:157–172

    Google Scholar 

  • Wan L, Wang Q, Papir A, Moreno IL (2018) Generalized end-to-end loss for speaker verification. In: International Conference on Acoustics, Speech and Signal Processing. IEEE

  • Wang F, Cheng J, Liu W, Liu H (2018) Additive margin softmax for face verification. IEEE Signal Process Lett 25(7):926–930

    Google Scholar 

  • Wang M, Deng W (2021) Deep face recognition: a survey. Neurocomputing 429:215–244

    Google Scholar 

  • Wang C, Muhammad J, Wang Y, He Z, Sun Z (2020) Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition. IEEE Trans Inform Forensics Secur 15:2944–2959

    Google Scholar 

  • Wang L, Ning H, Tan T, Hu W (2004) Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans Circuits Syst Video Technol 14(2):149–158

    Google Scholar 

  • Wang L, Tan T, Ning H, Hu W (2003) Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25(12):1505–1518

    Google Scholar 

  • Wang X, Zhang S, Wang S, Tianyu F, Shi H, Mei T (2020) Mis-classified vector guided softmax loss for face recognition. Proc AAAI Conf Artif Intell 34:12241–12248

    Google Scholar 

  • Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W (2018) Cosface: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5265–5274

  • Wang X, Wang S, Chi C, Zhang S, Mei T (2020) Loss function search for face recognition. In: International Conference on Machine Learning, pp 10029–10038. PMLR

  • Wang J, She M, Nahavandi S, Kouzani A (2010) A review of vision-based gait recognition methods for human identification. In: 2010 international conference on digital image computing: techniques and applications, pp 320–327. IEEE

  • Wang S, Jia S (2019) Signature handwriting identification based on generative adversarial networks. In: Journal of Physics: Conference Series, number 4

  • Wang M, Deng W (2018) Deep face recognition: a survey. arXiv preprint arXiv:1804.06655

  • Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision, pp 499–515. Springer

  • Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, McBride S (1994) A system for automated iris recognition. In: Workshop on Applications of Computer Vision, pp 121–128. IEEE

  • Winston J, Hemanth DJ (2019) A comprehensive review on iris image-based biometric system. Soft Comput 23(19):9361–9384

    Google Scholar 

  • Wolf T, Babaee M, Rigoll G (2016) Multi-view gait recognition using 3d convolutional neural networks. In: International Conference on Image Processing, pp 4165–4169. IEEE

  • Wright J, Yang AY, Ganesh A, Shankar S, Ma Y (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–277

    Google Scholar 

  • Wu Z, Huang Y, Wang L, Wang X, Tan T (2016) A comprehensive study on cross-view gait based human identification with deep CNNS. IEEE Trans Pattern Anal Mach Intell 39(2):209–226

    Google Scholar 

  • Wu Y, Wu Y, Gong R, Lv Y, Chen K, Liang D, Hu X, Liu X, Yan J (2020) Rotation consistent margin loss for efficient low-bit face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6866–6876

  • Wu X-Q, Wang K-Q, Zhang D(2002) Wavelet based palm print recognition. In: Proceedings. International Conference on Machine Learning and Cybernetics, vol 3, pp 1253–1257. IEEE

  • Xie Z, Guo Z, Qian C (2018) Palmprint gender classification by convolutional neural network. IET Comput Vis 12(4):476–483

    Google Scholar 

  • Xie W, Nagrani A, Chung JS, Zisserman A (2019) Utterance-level aggregation for speaker recognition in the wild. In: International Conference on Acoustics, Speech and Signal Processing. IEEE

  • Xin Z, Dandan P, Xin P, Xiaoling L, Xiaojing G (2015) Palmprint recognition based on deep learning

  • Xu C, Makihara Y, Li X, Yagi Y, Lu J (2020) Cross-view gait recognition using pairwise spatial transformer networks. IEEE Transactions on Circuits and Systems for Video Technology

  • Xu X, Xu N, Li H, Zhu Q (2019) Multi-spectral palmprint recognition with deep multi-view representation learning. In: International Conference on Machine Learning and Intelligent Communications, pp 748–758. Springer

  • Yale face database. http://vision.ucsd.edu/content/yale-face-database

  • Yan C, Zhang B, Coenen F (2015) Multi-attributes gait identification by convolutional neural networks. In: International Congress on Image and Signal Processing (CISP), pp 642–647. IEEE

  • Yang J, Zhang D, Frangi AF, Yang J (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137

    Google Scholar 

  • Yang M, Zhang L, Yang J, Zhang D (2012) Regularized robust coding for face recognition. IEEE Trans Image Process 22(5):1753–1766

    MathSciNet  MATH  Google Scholar 

  • Yeung D-Y, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) Svc2004: first international signature verification competition. In: International conference on biometric authentication, pp 16–22. Springer

  • Yi D, Lei Z, Li SZ (2013) Towards pose robust face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3539–3545

  • Yi D, Lei Z, Liao S, Li SZ (2014) Learning face representation from scratch. arXiv preprint arXiv:1411.7923

  • Yoffie DB, Wu L, Sweitzer J, Eden D, Ahuja K (2018) Voice war: Hey google vs. alexa vs. siri

  • Youtube faces db. http://www.cs.tau.ac.il/~wolf/ytfaces/

  • Yu S, Chen H, Reyes G, Edel B, Poh N (2017) Gaitgan: invariant gait feature extraction using generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 30–37

  • Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, pp 818–833. Springer

  • Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer

  • Zhang D (2000) Automated biometrics: technologies and systems, vol 7. Springer, New York

    Google Scholar 

  • Zhang D, Guo Z, Gong Y (2015) Multispectral biometrics systems and applications. Springer, New York

    Google Scholar 

  • Zhang D, Lu G, Zhang L (2018) Advanced biometrics. Springer, New York

    Google Scholar 

  • Zhang D, Shu W (1999) Two novel characteristics in palmprint verification: datum point invariance and line feature matching. Pattern Recognit 32(4):691–702

    Google Scholar 

  • Zhang D, Song F, Yong X, Liang Z (2009) Advanced pattern recognition technologies with applications to biometrics. IGI Global Hershey, Hershey

    Google Scholar 

  • Zhang Y, Zhichun M, Yuan L, Chen Y (2018) Ear verification under uncontrolled conditions with convolutional neural networks. IET Biom 7(3):185–198

    Google Scholar 

  • Zhang D, Zuo W, Yue F (2012) A comparative study of palmprint recognition algorithms. ACM Comput Surv (CSUR) 44(1):2

    Google Scholar 

  • Zhang X, Zhao R, Qiao Y, Wang X, Li H (2019) Adacos: Adaptively scaling cosine logits for effectively learning deep face representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 10823–10832

  • Zhang X, Zhao R, Yan J, Gao M, Qiao Y, Wang X, Li H (2019) P2sgrad: Refined gradients for optimizing deep face models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 9906–9914

  • Zhang J, Yu W, Yang X, Deng F (2019) Few-shot learning for ear recognition. In: Proceedings of the 2019 International Conference on Image, Video and Signal Processing, pp 50–54. ACM

  • Zhang S-X, Chen Z, Zhao Y, Li J, Gong Y (2016) End-to-end attention based text-dependent speaker verification. In: Spoken Language Technology Workshop (SLT), pp 171–178. IEEE

  • Zhang C, Koishida K (2017) End-to-end text-independent speaker verification with triplet loss on short utterances. In: Interspeech

  • Zhang Z, Liu X, Cui Y (2016) Multi-phase offline signature verification system using deep convolutional generative adversarial networks. In: 2016 9th international Symposium on Computational Intelligence and Design, vol 2, pp 103–107. IEEE

  • Zhang C, Liu W, Ma H, Fu H (2016) Siamese neural network based gait recognition for human identification. In: International Conference on Acoustics, Speech and Signal Processing. IEEE

  • Zhang Z, Tran L, Yin X, Atoum Y, Liu X, Wan J, Wang N (2019) Gait recognition via disentangled representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4710–4719

  • Zhang X, Fang Z, Wen Y, Li Z, Qiao Y (2017) Range loss for deep face recognition with long-tailed training data. In: IEEE International Conference on Computer Vision, pp 5409–5418

  • Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) ACM computing surveys (CSUR). Face Recog 35(4):399–458

    Google Scholar 

  • Zhao Z, Kumar A (2016) Accurate periocular recognition under less constrained environment using semantics-assisted convolutional neural network. IEEE Trans Inform Forensic Secur 12(5):1017–1030

    Google Scholar 

  • Zhao S, Zhang B, Chen CLP (2019) Joint deep convolutional feature representation for hyperspectral palmprint recognition. Inform Sci 489:167–181

    MathSciNet  Google Scholar 

  • Zhao Z, Kumar A (2017) Towards more accurate iris recognition using deeply learned spatially corresponding features. In: IEEE International Conference on Computer Vision, pp 3809–3818

  • Zhao S, Zhang B (2020) Joint constrained least-square regression with deep convolutional feature for palmprint recognition. IEEE Transactions on Systems, Man, and Cybernetics: Systems

  • Zheng Y, Pal DK, Savvides M (2018) Ring loss: convex feature normalization for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5089–5097

  • Zheng S, Zhang J, Huang K, He R, Tan T (2011) Robust view transformation model for gait recognition. In: International Conference on Image Processing. IEEE

  • Zhong D, Yang Y, Du X (2018) Palmprint recognition using siamese network. In: Chinese Conference on Biometric Recognition, pp 48–55. Springer

  • Zhu Y, Tan T, Wang Y (2000) Biometric personal identification based on iris patterns. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000

  • Zou Q, Wang Y, Zhao Y, Wang Q, Shen C, Li Q (2018) Deep learning based gait recognition using smartphones in the wild. arXiv preprint arXiv:1811.00338

  • Zue V, Seneff S, Glass J (1990) Speech database development at mit: timit and beyond. Speech Commun 9(4):351–356

    Google Scholar 

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Acknowledgements

We would like to thank Prof. Rama Chellappa, and Dr. Nalini Ratha for reviewing this work, and providing very helpful comments and suggestions.

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  1. Snapchat, Santa Monica, USA

    Shervin Minaee

  2. University of California, Riverside, USA

    Amirali Abdolrashidi

  3. Facebook Research, Seattle, USA

    Hang Su

  4. The University of Western Australia, Crawley, Australia

    Mohammed Bennamoun

  5. Chinese University of Hong Kong, Shenzhen, China

    David Zhang

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Minaee, S., Abdolrashidi, A., Su, H. et al. Biometrics recognition using deep learning: a survey. Artif Intell Rev 56, 8647–8695 (2023). https://doi.org/10.1007/s10462-022-10237-x

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