Face Recognition Technologies: A Comprehensive Review
Keywords:
Face recognition, Deep learning, Biometrics, Computer vision, Neural networks, Privacy, Bias mitigationAbstract
It is hard to state how much face recognition has developed in the last decade. As a once-specialized type of biometric system, we see face recognition wherever we look: at the airport, through Instagram etc. This review will discuss that journey from its traditional systems to the deep learning systems that we see at the forefront of the field. From a detailed analysis of 127 academic publications from 2015 to 2024, what is evidently clear is that face recognition systems have undergone rapid algorithm development during this time period. From that literature, there are several emergent trends. Convolutional neural networks are still dominant, transformer-based architectures are a new development, and there is an uptick in work on the pressing issues of bias and privacy. Our analysis reflects that face recognition systems are more accurate but adept at modeling real-world situation variation in the terms of illumination, pose and the aging process. Additionally, the issue of demographic fairness is far from solved. Overall, the review provides an informative snapshot of the current state of face recognition and identifies what we observe as the most promising and important avenues towards future research.
References
[1] W. Bledsoe, "The model method in facial recognition," Rep. PR1, Panoramic Research Inc, Palo Alto, CA, 1966.
[2] M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[3] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
[4] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, "DeepFace: Closing the gap to human-level performance in face verification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1701-1708.
[5] F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: A unified embedding for face recognition and clustering," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 815-823.
[6] T. Kanade, Computer recognition of human faces. Basel, Switzerland: Birkhauser, 1977.
[7] T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: Application to face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.
[8] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[9] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-up robust features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008.
[10] Y. Sun, X. Wang, and X. Tang, "Deep learning face representation from predicting 10,000 classes," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1891-1898.
[11] Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep learning face representation by joint identification-verification," in Advances in Neural Information Processing Systems, 2014, pp. 1988-1996.
[12] Y. Sun, D. Liang, X. Wang, and X. Tang, "Deepid3: Face recognition with very deep neural networks," arXiv preprint arXiv:1502.00873, 2015.
[13] W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, "SphereFace: Deep hypersphere embedding for face recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 212-220.
[14] H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu, "CosFace: Large margin cosine loss for deep face recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5265-5274.
[15] J. Deng, J. Guo, N. Xue, and S. Zafeiriou, "ArcFace: Additive angular margin loss for deep face recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 4690-4699.
[16] Y. Zhong and W. Deng, "Face transformer for recognition," arXiv preprint arXiv:2103.14803, 2021.
[17] A. Dosovitskiy et al., "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.
[18] D. Yi, Z. Lei, S. Liao, and S. Z. Li, "Learning face representation from scratch," arXiv preprint arXiv:1411.7923, 2014.
[19] Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, "MS-Celeb-1M: A dataset and benchmark for large-scale face recognition," in European Conference on Computer Vision, 2016, pp. 87-102.
[20] O. M. Parkhi, A. Vedaldi, and A. Zisserman, "Deep face recognition," in British Machine Vision Conference, 2015.
[21] Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "VGGFace2: A dataset for recognising faces across pose and age," in International Conference on Automatic Face & Gesture Recognition, 2018, pp. 67-74.
[22] Z. Zhu et al., "WebFace260M: A benchmark unveiling the power of million-scale deep face recognition," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10492-10502.
[23] G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, "Labeled faces in the wild: A database for studying face recognition in unconstrained environments," in Workshop on Faces in Real-Life Images: Detection, Alignment, and Recognition, 2008.
[24] B. F. Klare et al., "Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1931-1939.
[25] C. Whitelam et al., "IARPA Janus Benchmark-B face dataset," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 90-98.
[26] B. Maze et al., "IARPA Janus Benchmark-C: Face dataset and protocol," in International Conference on Biometrics, 2018, pp. 158-165.
[27] I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, "The MegaFace benchmark: 1 million faces for recognition at scale," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4873-4882.
[28] K. Ricanek and T. Tesafaye, "MORPH: A longitudinal image database of normal adult age-progression," in International Conference on Automatic Face and Gesture Recognition, 2006, pp. 341-345.
[29] G. Panis, A. Lanitis, N. Tsapatsoulis, and T. F. Cootes, "Overview of research on facial ageing using the FG-NET ageing database," IET Biometrics, vol. 5, no. 2, pp. 37-46, 2016.
[30] R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker, "Multi-PIE," Image and Vision Computing, vol. 28, no. 5, pp. 807-813, 2010.
[31] S. Sengupta, J. C. Chen, C. Castillo, V. M. Patel, R. Chellappa, and D. W. Jacobs, "Frontal to profile face verification in the wild," in IEEE Winter Conference on Applications of Computer Vision, 2016, pp. 1-9.
[32] I. Masi, S. Rawls, G. Medioni, and P. Natarajan, "Pose-aware face recognition in the wild," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4838-4846.
[33] X. Zhu, Z. Lei, X. Liu, H. Shi, and S. Z. Li, "Face alignment across large poses: A 3D solution," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 146-155.
[34] T. Hassner, S. Harel, E. Paz, and R. Enbar, "Effective face frontalization in unconstrained images," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 4295-4304.
[35] Y. Wen, Z. Li, and Y. Qiao, "Latent factor guided convolutional neural networks for age-invariant face recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 4893-4901.
[36] H. Qiu, D. Gong, Z. Li, W. Liu, and D. Tao, "End2End occluded face recognition by masking corrupted features," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, pp. 6939-6952, 2021.
[37] K. Zhang, Z. Zhang, C. W. Cheng, W. H. Hsu, Y. Qiao, W. Liu, and T. Zhang, "Super-identity convolutional neural network for face hallucination," in Proceedings of the European Conference on Computer Vision, 2018, pp. 183-198.
[38] L. Best-Rowden and A. K. Jain, "Learning face image quality from human assessments," IEEE Transactions on Information Forensics and Security, vol. 13, no. 12, pp. 3064-3077, 2018.
[39] J. Buolamwini and T. Gebru, "Gender shades: Intersectional accuracy disparities in commercial gender classification," in Conference on Fairness, Accountability and Transparency, 2018, pp. 77-91.
[40] P. Grother, M. Ngan, and K. Hanaoka, "Face recognition vendor test (FRVT) part 3: Demographic effects," National Institute of Standards and Technology, 2019.
[41] M. Wang and W. Deng, "Mitigating bias in face recognition using skewness-aware reinforcement learning," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9322-9331.
[42] Z. Erkin et al., "Privacy-preserving face recognition," in International Symposium on Privacy Enhancing Technologies Symposium, 2009, pp. 235-253.
[43] I. D. Raji, T. Gebru, M. Mitchell, J. Buolamwini, J. Lee, and E. Denton, "Saving face: Investigating the ethical concerns of facial recognition auditing," in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020, pp. 145-151.
[44] FBI, "Next Generation Identification (NGI) System," 2023. [Online]. Available: https://www.fbi.gov/services/cjis/fingerprints-and-other-biometrics/ngi
[45] Apple Inc., "About Face ID advanced technology," 2023. [Online]. Available: https://support.apple.com/en-us/HT208108
[46] Y. Gurovich et al., "Identifying facial phenotypes of genetic disorders using deep learning," Nature Medicine, vol. 25, no. 1, pp. 60-64, 2019.
[47] B. Yin, L. Tran, H. Li, X. Shen, and X. Liu, "Towards interpretable face recognition," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 9348-9357.
[48] X. Zhao, X. Ding, Y. Chen, Z. Du, and Y. Liu, "Continual learning for face recognition," Pattern Recognition, vol. 132, 2022.
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