Abstract
Recently, the problem of racial bias in facial biometric systems has generated considerable attention from the media and biometric community. Many investigative studies have been published on estimating the bias between Caucasians and Asians, Caucasians and Africans, and other racial comparisons. These studies have reported inferior performances of both Asians and Africans when compared to other races. However, very few studies have highlighted the comparative differences in performance as a function of race between Africans and Asians. More so, those previous studies were mainly concentrated on a single aspect of facial biometrics and were usually conducted with images potentially captured with multiple camera sensors, thereby compounding their findings. This paper presents a comparative racial bias study of Asians with Africans on various facial biometric tasks. The images used were captured with the same camera sensor and under controlled conditions. We examine the performances of many DCNN-based models on face detection, facial landmark detection, quality assessment, verification, and identification. The results suggested higher performance on the Asians compared to the Africans by most algorithms under the same imaging and testing conditions.
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References
Gong, S., Liu, X., Jain, A.K.: Mitigating face recognition bias via group adaptive classifier. arXiv preprint arXiv:2006.07576 (2020)
Wang, M., Deng, M.: Mitigating bias in face recognition using skewness-aware reinforcement learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9322–9331 (2020)
Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., Busch, C.: Demographic bias in biometrics: a survey on an emerging challenge. IEEE Trans. Technol, Soc (2020)
Singer, N.: Amazon’s facial recognition wrongly identifies 28 lawmakers, a.c.l.u. says, 2020–02-23 2018. http://www.nytimes.com/2018/07/26/technology/amazon-aclu-facial-recognition-congress.html
Cavazos, J.G., Phillips, P.J., Castillo, C.D., O’Toole, A.J.: Accuracy comparison across face recognition algorithms: Where are we on measuring race bias? Behavior, and Identity Science, IEEE Transactions on Biometrics (2020)
Patrick, G., Mei, N., Kayee, H.: Face recognition vendor test (frvt) part 3: Demographic effects. National Institute of Standards and Technology, Report NISTIR 8280 (2019)
Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on fairness, accountability and transparency, Conference Proceedings, pp. 77–91 (2018)
Du, M., Yang, F., Zou, N., Hu, X.: Fairness in deep learning: a computational perspective, arXiv preprint arXiv:1908.08843 (2019)
Wang, M., Deng, M., Hu, J., Tao, X., Huang, Y.: Racial faces in the wild: Reducing racial bias by information maximization adaptation network. In: Proceedings of the IEEE International Conference on Computer Vision, Conference Proceedings, pp. 692–702 (2019)
Wang, M., Deng, W.: Mitigate bias in face recognition using skewness-aware reinforcement learning, arXiv preprint arXiv:1911.10692 (2019)
Nagpal, S., Singh, M., Singh, R., Vatsa, M., Ratha, N.: Deep learning for face recognition: pride or prejudiced? arXiv preprint arXiv:1904.01219 (2019)
Klare, B.F., Burge, M.J., Klontz, J.C., Bruegge, R.W.V., Jain, A.K.: Face recognition performance: role of demographic information. IEEE Trans. Inf. Forens. Secur. 7(6), 1789–1801 (2012)
Cavazos, J.G., Phillips, P.J., Castillo, C.D., O’Toole, A.J.: Accuracy comparison across face recognition algorithms: where are we on measuring race bias?” arXiv preprint arXiv:1912.07398 (2019)
Furl, N., Phillips, P.J., O’Toole, A.J.: Face recognition algorithms and the other-race effect: computational mechanisms for a developmental contact hypothesis. Cognitive Science 26(6), 797–815 (2002)
Phillips, P.J., Jiang, F., Narvekar, A., Ayyad, J., O’Toole, A.J.: An other-race effect for face recognition algorithms. ACM Trans. Appl. Perception (TAP) 8(2), 1–11 (2011)
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Conference Proceedings, pp. 212–220 (2017)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Conference Proceedings, pp. 815–823 (2015)
Wu, X., He, R., Sun, Z., Tan, T.: A light cnn for deep face representation with noisy labels. IEEE Trans. Inf. Forens. Secur. 13(11), 2884–2896 (2018)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Conference Proceedings, pp. 4690–4699
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483 (2013)
Yang, S., Luo, P., Loy, C.-C., Tang, X.: Wider face: a face detection benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533 (2016)
Terhorst, P., Kolf, J.N., Damer, N., Kirchbuchner, F., Kuijper, A.: Ser-fiq: unsupervised estimation of face image quality based on stochastic embedding robustness. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5651–5660 (2020)
Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.C., Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: CVPR: WORKSHOPS. IEEE 2011, pp. 74–81 (2011)
Visualizing data using t-sne: v. d. Maaten, L., Hinton, G. J. Mach. Learn. Res. 9, 2579–2605 (2008)
O’Toole, A.J., Phillips, P.J., An, X., Dunlop, J.: Demographic effects on estimates of automatic face recognition performance. Image Vision Comput. 30(3), 169–176 (2012)
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Muhammad, J., Wang, Y., Wang, L., Zhang, K., Sun, Z. (2022). An Empirical Comparative Analysis of Africans with Asians Using DCNN Facial Biometric Models. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_14
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