Past, Present, and Future of Face Recognition: A Review

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Squeeze-and-Excitation Networks

Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667

Advances in neural information processing systems 7

Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839

FaceNet: A unified embedding for face recognition and clustering

Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341010 · Cited by 14,265

Eigenfaces for Recognition

Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733

Squeeze-and-Excitation Networks

Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157

Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781

Deep Learning Face Attributes in the Wild

Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542

Face Description with Local Binary Patterns: Application to Face Recognition

Ahonen, T., Hadid, A., & Pietikainen, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.244 · Cited by 5,584

Deep Face Recognition

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479

Principal warps: thin-plate splines and the decomposition of deformations

Bookstein, F. L. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.24792 · Cited by 4,913

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636

A Discriminative Feature Learning Approach for Deep Face Recognition

Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051

Learning a Similarity Metric Discriminatively, with Application to Face Verification

Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036

IEEE Transactions on Pattern Analysis and Machine Intelligence

(2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1307293 · Cited by 3,721

Deep Learning: Methods and Applications

Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480

CosFace: Large Margin Cosine Loss for Deep Face Recognition

Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00552 · Cited by 3,034

VGGFace2: A Dataset for Recognising Faces across Pose and Age

Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00020 · Cited by 3,032

Face recognition by elastic bunch graph matching

Wiskott, L., Fellous, J.-M., Kuiger, N., & von der Malsburg, C. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598235 · Cited by 2,883

Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

Tan, X., & Triggs, B. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2042645 · Cited by 2,842

Face Recognition with Local Binary Patterns

Ahonen, T., Hadid, A., & Pietikäinen, M. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_36 · Cited by 2,462

Application of the Karhunen-Loeve procedure for the characterization of human faces

Kirby, M., & Sirovich, L. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.41390 · Cited by 2,448

The FERET database and evaluation procedure for face-recognition algorithms

Phillips, P. J., Wechsler, H., Huang, J., & Rauss, P. J. (1998) · Image and Vision Computing · doi:10.1016/s0262-8856(97)00070-x · Cited by 2,431

International Journal of Computer Vision

(2013) · International Journal of Computer Vision · doi:10.1007/11263.1573-1405 · Cited by 2,340

Low-dimensional procedure for the characterization of human faces

Sirovich, L., & Kirby, M. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.000519 · Cited by 2,311

SphereFace: Deep Hypersphere Embedding for Face Recognition

Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.713 · Cited by 2,280

MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

Guo, Y., Zhang, L., Hu, Y., He, X., & Gao, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_6 · Cited by 2,122

Deep Learning Face Representation from Predicting 10,000 Classes

Sun, Y., Wang, X., & Tang, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.244 · Cited by 1,997

Face recognition by independent component analysis

Bartlett, M. S., Movellan, J. R., & Sejnowski, T. J. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.804287 · Cited by 1,930

Deep Learning Face Representation by Joint Identification-Verification

Sun, Y., Wang, X., & Tang, X. (2014) · arXiv · doi:10.48550/arxiv.1406.4773 · Cited by 1,906

Handbook of Face Recognition

(2011) · Springer eBooks · doi:10.1007/978-0-85729-932-1 · Cited by 1,819

Distortion invariant object recognition in the dynamic link architecture

Lades, M., Vorbruggen, J. C., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R. P., & Konen, W. (1993) · IEEE Transactions on Computers · doi:10.1109/12.210173 · Cited by 1,805

View-based and modular eigenspaces for face recognition

Pentland, Moghaddam, & Starner (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323814 · Cited by 1,800

Multi-PIE

Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.08.002 · Cited by 1,621

IEEE Transactions on Image Processing

del Conocimiento, D. D. G. (2021) · Universidad Peruana de Ciencias Aplicadas (UPC) · Cited by 1,524

Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition

Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,503

A Light CNN for Deep Face Representation With Noisy Labels

Wu, X., He, R., Sun, Z., & Tan, T. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2833032 · Cited by 1,186

Additive Margin Softmax for Face Verification

Wang, F., Cheng, J., Liu, W., & Liu, H. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2822810 · Cited by 1,106

Blur Insensitive Texture Classification Using Local Phase Quantization

Ojansivu, V., & Heikkilä, J. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-69905-7_27 · Cited by 1,027

Deeply learned face representations are sparse, selective, and robust

Sun, Y., Wang, X., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298907 · Cited by 939

The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

Kemelmacher-Shlizerman, I., Seitz, S. M., Miller, D., & Brossard, E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.527 · Cited by 912

Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class

Martinez, A. M. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1008382 · Cited by 813

Frontal to profile face verification in the wild

Sengupta, S., Chen, J.-C., Castillo, C., Patel, V. M., Chellappa, R., & Jacobs, D. W. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477558 · Cited by 805

Computer Vision and Image Understanding

Saideni, W., Courreges, F., Helbert, D., & Cances, J. P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4129906 · Cited by 766

IEEE Transactions on Information Forensics and Security

(2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.10206 · Cited by 729

A tutorial survey of architectures, algorithms, and applications for deep learning

Deng, L. (2014) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2013.9 · Cited by 725

Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A

Klare, B. F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P., Mah, A., Burge, M., & Jain, A. K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298803 · Cited by 701

IEEE Transactions on Instrumentation and Measurement

(2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2931486 · Cited by 691

Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About

Sinha, P., Balas, B., Ostrovsky, Y., & Russell, R. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.884093 · Cited by 674

A data-driven approach to cleaning large face datasets

Ng, H.-W., & Winkler, S. (2014) · 2014 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2014.7025068 · Cited by 643

IARPA Janus Benchmark - C: Face Dataset and Protocol

Maze, B., Adams, J., Duncan, J. A., Kalka, N., Miller, T., Otto, C., Jain, A. K., Niggel, W. T., Anderson, J., Cheney, J., et al. (2018) · 2018 International Conference on Biometrics (ICB) · doi:10.1109/icb2018.2018.00033 · Cited by 639

Face Recognition Systems: A Survey

Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020) · Sensors · doi:10.3390/s20020342 · Cited by 623

NormFace: L 2 Hypersphere Embedding for Face Verification

Wang, F., Xiang, X., Cheng, J., & Yuille, A. L. (2017) · Proceedings of the 25th ACM international conference on Multimedia · doi:10.1145/3123266.3123359 · Cited by 581

Recognizing moving faces: a psychological and neural synthesis

O'Toole, A. J., Roark, D. A., & Abdi, H. (2002) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)01908-3 · Cited by 487

A survey on deep learning based face recognition

Guo, G., & Zhang, N. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102805 · Cited by 485

A survey on face detection in the wild: Past, present and future

Zafeiriou, S., Zhang, C., & Zhang, Z. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.03.015 · Cited by 458

IEEE transactions on systems, man, and cybernetics. Systems

Systems, M. I. (2013) · Institute of Electrical and Electronics Engineers eBooks · Cited by 451

IARPA Janus Benchmark-B Face Dataset

Whitelam, C., Taborsky, E., Blanton, A., Maze, B., Adams, J., Miller, T., Kalka, N., Jain, A. K., Duncan, J. A., Allen, K., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.87 · Cited by 445

Bayesian Face Revisited: A Joint Formulation

Chen, D., Cao, X., Wang, L., Wen, F., & Sun, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33712-3_41 · Cited by 427

Independent component analysis: an introduction

Stone, J. V. (2002) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01813-1 · Cited by 425

The BANCA Database and Evaluation Protocol

Bailly-Bailliére, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Mariéthoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., et al. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-44887-x_74 · Cited by 423

Large-Margin Softmax Loss for Convolutional Neural Networks

Liu, W., Wen, Y., Yu, Z., & Yang, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02295 · Cited by 408

Range Loss for Deep Face Recognition with Long-Tailed Training Data

Zhang, X., Fang, Z., Wen, Y., Li, Z., & Qiao, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.578 · Cited by 405

Deep Imbalanced Learning for Face Recognition and Attribute Prediction

Huang, C., Li, Y., Loy, C. C., & Tang, X. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2914680 · Cited by 354

Feature Transfer Learning for Face Recognition With Under-Represented Data

Yin, X., Yu, X., Sohn, K., Liu, X., & Chandraker, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00585 · Cited by 346

Unsupervised Joint Alignment of Complex Images

Huang, G. B., Jain, V., & Learned-Miller, E. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408858 · Cited by 339

Do We Really Need to Collect Millions of Faces for Effective Face Recognition?

Masi, I., Trần, A. T., Hassner, T., Leksut, J. T., & Medioni, G. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_35 · Cited by 339

Sum-product networks: A new deep architecture

Poon, H., & Domingos, P. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130310 · Cited by 315

3D Face Recognition under Expressions, Occlusions, and Pose Variations

Drira, H., Ben Amor, B., Srivastava, A., Daoudi, M., & Slama, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.48 · Cited by 312

Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset

Chen, B.-C., Chen, C.-S., & Hsu, W. H. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2420374 · Cited by 305

Recognition of blurred faces using Local Phase Quantization

Ahonen, T., Rahtu, E., Ojansivu, V., & Heikkila, J. (2008) · 2008 19th International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761847 · Cited by 301

Face recognition based on convolutional neural network

Coskun, M., Ucar, A., Yildirim, O., & Demir, Y. (2017) · 2017 International Conference on Modern Electrical and Energy Systems (MEES) · doi:10.1109/mees.2017.8248937 · Cited by 295

Scale-space properties of the multiscale morphological dilation-erosion

Jackway, P. T., & Deriche, M. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.476009 · Cited by 292

KinectFaceDB: A Kinect Database for Face Recognition

Min, R., Kose, N., & Dugelay, J.-L. (2014) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2014.2331215 · Cited by 274

Learning to Align from Scratch

Huang, G. B., Mattar, M., Lee, H., & Miller, E. G. (2012) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 269

Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network

Song, L., Gong, D., Li, Z., Liu, C., & Liu, W. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00086 · Cited by 267

International Journal of Imaging Systems and Technology

(2006) · International Journal of Imaging Systems and Technology · doi:10.1002/(issn)1098-1098 · Cited by 264

Illumination-Aware Age Progression

Kemelmacher-Shlizerman, I., Suwajanakorn, S., & Seitz, S. M. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.426 · Cited by 236

Face authentication with Gabor information on deformable graphs

Duc, B., Fischer, S., & Bigun, J. (1999) · IEEE Transactions on Image Processing · doi:10.1109/83.753738 · Cited by 228

Web-scale training for face identification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298891 · Cited by 227

Marginal Loss for Deep Face Recognition

Deng, J., Zhou, Y., & Zafeiriou, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.251 · Cited by 218

Ring Loss: Convex Feature Normalization for Face Recognition

Zheng, Y., Pal, D. K., & Savvides, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00534 · Cited by 211

Human-centric Computing and Information Sciences

(2013) · Human-centric Computing and Information Sciences · doi:10.1007/13673.2192-1962 · Cited by 198

Can facial cosmetics affect the matching accuracy of face recognition systems?

Dantcheva, A., Chen, C., & Ross, A. (2012) · 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2012.6374605 · Cited by 186

Level Playing Field for Million Scale Face Recognition

Nech, A., & Kemelmacher-Shlizerman, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.363 · Cited by 180

A Bayesian similarity measure for direct image matching

Moghaddam, B., Nastar, C., & Pentland, A. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546848 · Cited by 154

Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation

Chen, B., Deng, W., & Du, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.428 · Cited by 153

A survey of local feature methods for 3D face recognition

Soltanpour, S., Boufama, B., & Wu, Q. M. J. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.08.003 · Cited by 147

Face-based Active Authentication on mobile devices

Fathy, M. E., Patel, V. M., & Chellappa, R. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178258 · Cited by 134

The CSU Face Identification Evaluation System

Beveridge, J. R., Bolme, D. S., Draper, B. A., & Teixeira, M. A. (2004) · Machine Vision and Applications · doi:10.1007/s00138-004-0144-7 · Cited by 127

Face recognition in unconstrained environment with CNN

Fredj, H. B., Bouguezzi, S., & Souani, C. (2020) · The Visual Computer · doi:10.1007/s00371-020-01794-9 · Cited by 125

Automatic extraction of head and face boundaries and facial features

Shih, F. Y. (2003) · Information Sciences · doi:10.1016/j.ins.2003.03.002 · Cited by 124

Deep 3D face identification

Kim, D., Hernandez, M., Choi, J., & Medioni, G. (2017) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272691 · Cited by 124

Rethinking Feature Discrimination and Polymerization for Large-scale RecognitionPreprint

Liu, Y., Li, H., & Wang, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.00870 · Cited by 120

3D face recognition: a survey

Zhou, S., & Xiao, S. (2018) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-018-0157-2 · Cited by 119

A Survey of 2D Face Recognition Techniques

Chihaoui, M., Elkefi, A., Bellil, W., & Amar, C. B. (2016) · Computers · doi:10.3390/computers5040021 · Cited by 112

Gaussian Affinity for Max-Margin Class Imbalanced Learning

Hayat, M., Khan, S. H., Zamir, S. W., Shen, J., & Shao, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00657 · Cited by 106

A review of wavelet denoising in medical imaging

Ouahabi, A. (2013) · International Workshop on Systems, Signal Processing and their Applications · doi:10.1109/wosspa.2013.6602330 · Cited by 103

Signal and Image Multiresolution Analysis

(2012) · Wiley eBooks · doi:10.1002/9781118568767 · Cited by 93

An anatomical and photographic technique for forensic facial identification

Porter, G., & Doran, G. (2000) · Forensic Science International · doi:10.1016/s0379-0738(00)00290-5 · Cited by 92

Dense 3D Face Correspondence

Gilani, S. Z., Mian, A. S., Faisal Shafait, B., & Reid, I. D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2725279 · Cited by 89

Face Authentication Using Adapted Local Binary Pattern Histograms

Rodriguez, Y., & Marcel, S. (2006) · Lecture notes in computer science · doi:10.1007/11744085_25 · Cited by 88

Facial soft biometric features for forensic face recognition

Pedro Tome, A., Vera-Rodríguez, R., Fiérrez, J., & Ortega-García, J. (2015) · Forensic Science International · doi:10.1016/j.forsciint.2015.09.002 · Cited by 87

Disguised Faces in the Wild

Kushwaha, V., Singh, M., Singh, R., Mayank Vatsa, A., Ratha, N., & Chellappa, R. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00008 · Cited by 82

Attention-based convolutional neural network for deep face recognition

Ling, H., Wu, J., Huang, J., Chen, J., & Li, P. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08422-2 · Cited by 81

Face Recognition Using Gabor Filter Based Feature Extraction with Anisotropic Diffusion as a Pre-processing Technique

Abhishree, T. M., Latha, J., Manikantan, K., & Ramachandran, S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.03.149 · Cited by 75

Deep learning in intrusion detection perspective: Overview and further challenges

Kim, K., & Aminanto, M. E. (2017) · International Workshop on Big Data and Information Security (IWBIS) · doi:10.1109/iwbis.2017.8275095 · Cited by 72

Future of Face Recognition: A Review

Arya, S., Pratap, N., & Bhatia, K. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.08.076 · Cited by 69

Jensen-Shannon Boosting Learning for Object Recognition

Huang, X., Siyuan, L. I., & Wang, Y. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.197 · Cited by 68

Deep, dense and accurate 3D face correspondence for generating population specific deformable models

Gilani, S. Z., Mian, A. S., & Eastwood, P. R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.04.013 · Cited by 68

Deep Face Recognition with Center Invariant Loss

Wu, Y., Liu, H., Li, J., & Fu, Y. (2017) · Thematic Workshops of ACM Multimedia · doi:10.1145/3126686.3126693 · Cited by 62

Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation

Ferroukhi, M., Ouahabi, A., Attari, M., et al. (2019) · Electronics · doi:10.3390/electronics8010088 · Cited by 57

Systematic review of 3D facial expression recognition methods

Alexandre, G. R., Soares, J. M., & Thé, G. A. P. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107108 · Cited by 51

Minimum margin loss for deep face recognition

Wei, X., Wang, H., Scotney, B. W., & Wan, H. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107012 · Cited by 47

Bayesian shape model for facial feature extraction and recognition

Xue, Z., Li, S. Z., & Teoh, E. K. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00181-x · Cited by 43

Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition

Wu, Y., Wu, Y., Gong, R., Lv, Y., Chen, K., Liang, D., Hu, X., Liu, X., & Yan, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00690 · Cited by 42

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Mimouna, A., Alouani, I., Khalifa, A. B., Hillali, Y. E., Ahmed, A. T., Rivenq, A., Ouahabi, A., & Amara, N. E. B. (2020) · Electronics · doi:10.3390/electronics9040560 · Cited by 36

Robust facial feature localization by coupled features

Zobel, M., Gebhard, A., Paulus, D. W. R., Denzler, J., & Niemann, H. J. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2000.840604 · Cited by 31

Active/space-variant object recognition

Massimo Tistarelli, A. (1995) · Image and Vision Computing · doi:10.1016/0262-8856(95)90841-u · Cited by 28

Unconstrained and constrained face recognition using dense local descriptor with ensemble framework

Kumar, D., Garain, J., Kisku, D. R., et al. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.117 · Cited by 26

LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild

Elharrouss, O., Almaadeed, N., & Al-Maadeed, S. A. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089530 · Cited by 24

Multimodal depth and intensity face verification approach using LBP, SLF, BSIF, and LPQ local features fusion

Ouamane, A., Benakcha, A., Belahcene, M., & Ahmed, A. T. (2015) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661815040215 · Cited by 23

Robust multimodal 2D and 3D face authentication using local feature fusion

Ouamane, A., Belahcene, M., Benakcha, A., Bourennane, S., & Ahmed, A. T. (2014) · Signal Image and Video Processing · doi:10.1007/s11760-014-0712-x · Cited by 22

Advances in Heuristic Signal Processing and Applications

Chatterjee, A., Nobahari, H., & Siarry, P. (2013) · Springer eBooks · doi:10.1007/978-3-642-37880-5 · Cited by 19

Face recognition using 1DLBP, DWT and SVM

Benzaoui, A., Boukrouche, A., Doghmane, H., & Bourouba, H. (2015) · International Conference on Control, Engineering & Information Technology · doi:10.1109/ceit.2015.7233002 · Cited by 17

Methods based on wavelets for time delay estimation of ultrasound signals

Guetbi, C., Kouamé, D., Ouahabi, A., & Chemla, J.-P. (2002) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs.1998.813947 · Cited by 17

Deep Learning for Facial Recognition on Single Sample per Person Scenarios with Varied Capturing Conditions

Ríos‐Sánchez, B., Costa-da-Silva, D., Martín‐Yuste, N., et al. (2019) · Applied Sciences · doi:10.3390/app9245474 · Cited by 15

1DLBP and PCA for face recognition

Benzaoui, A., & Boukrouche, A. (2013) · International Symposium on Programming and Systems (ISPS) · doi:10.1109/isps.2013.6581486 · Cited by 10

Angular Discriminative Deep Feature Learning for Face Verification

Wu, B., & Wu, H. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053675 · Cited by 10

Staistical features extraction in wavelet domain for texture classification

Zehani, S., Ouahabi, A., Mimi, M., & Ahmed, A. T. (2019) · International Conference on Image and Signal Processing and their Applications (ISPA) · doi:10.1109/ispa48434.2019.8966865 · Cited by 3

Delphi study for the development and preliminary validation of an item set for the assessment of non-experts' AI literacy

Laupichler, M. C., Aster, A., & Raupach, T. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100126 · Cited by 149

Artificial intelligence empowers gamification: Optimizing student engagement and learning outcomes in e-learning and moocs

Bachiri, Y.-A., Mouncif, H., & Bouikhalene, B. (2023) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v13i8.40853 · Cited by 59

High-efficiency and visual-usability image encryption based on thumbnail preserving and chaotic system

Zhang, Y., Zhao, R., Zhang, Y., Lan, R., & Chai, X. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.04.001 · Cited by 57

Forensic Facial Comparison: Current Status, Limitations, and Future Directions

Bacci, N., Davimes, J. G., Steyn, M., & Briers, N. (2021) · Biology · doi:10.3390/biology10121269 · Cited by 26

A Study on the Effect of Ageing in Facial Authentication and the Utility of Data Augmentation to Reduce Performance Bias Across Age Groups

Yao, W., Farooq, M. A., Lemley, J., & Corcoran, P. (2023) · IEEE Access · doi:10.1109/access.2023.3312612 · Cited by 8