Abstract
Face recognition (FR) has witnessed remarkable progress with the surge of deep learning. Current FR evaluation protocols usually adopt different thresholds to calculate the True Accept Rate (TAR) under a pre-defined False Accept Rate (FAR) for different datasets. However, in practice, when the FR model is deployed on industry systems (e.g., hardware devices), only one fixed threshold is adopted for all scenarios to distinguish whether a face image pair belongs to the same identity. Therefore, current evaluation protocols using different thresholds for different datasets are not fully compatible with the practical evaluation scenarios with one fixed threshold, and it is critical to measure the performance of FR models by using one threshold for all datasets. In this paper, we rethink the limitations of existing evaluation protocols for FR and propose to evaluate the performance of FR models from a new perspective. Specifically, in our OneFace, we first propose the One-Threshold-for-All (OTA) evaluation protocol for FR, which utilizes one fixed threshold called as Calibration Threshold to measure the performance on different datasets. Then, to improve the performance of FR models under the OTA protocol, we propose the Threshold Consistency Penalty (TCP) to improve the consistency of the thresholds among multiple domains, which includes Implicit Domain Division (IDD) as well as Calibration and Domain Thresholds Estimation (CDTE). Extensive experimental results demonstrate the effectiveness of our method for FR.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
An, X., et al.: Killing two birds with one stone: efficient and robust training of face recognition CNNs by partial FC. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4042–4051 (2022)
An, X., et al.: Partial FC: training 10 million identities on a single machine. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 1445–1449, October 2021
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 539–546. IEEE (2005)
Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S.: Sub-center arcface: boosting face recognition by large-scale noisy web faces. In: Proceedings of the IEEE Conference on European Conference on Computer Vision (2020)
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, pp. 4690–4699 (2019)
Deng, J., Guo, J., Yang, J., Lattas, A., Zafeiriou, S.: Variational prototype learning for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11906–11915, June 2021
Gong, S., Liu, X., Jain, A.K.: Jointly de-biasing face recognition and demographic attribute estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 330–347. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_20
Gong, S., Liu, X., Jain, A.K.: Mitigating face recognition bias via group adaptive classifier. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3414–3424 (2021)
Grother, P.J., Ngan, M.L., Hanaoka, K.K., et al.: Ongoing face recognition vendor test (FRVT) part 3: demographic effects. NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg (2019)
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Huang, Y., et al.: 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 (2020)
Karkkainen, K., Joo, J.: FairFace: face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1548–1558 (2021)
Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4873–4882 (2016)
Kim, M., Jain, A.K., Liu, X.: AdaFace: quality adaptive margin for face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18750–18759 (2022)
Kim, Y., Park, W., Roh, M.C., Shin, J.: GroupFace: learning latent groups and constructing group-based representations for face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5621–5630 (2020)
Kim, Y., Park, W., Shin, J.: BroadFace: looking at tens of thousands of people at once for face recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 536–552. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_31
Li, Z., et al.: Learning to auto weight: entirely data-driven and highly efficient weighting framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4788–4795 (2020)
Liu, C., et al.: Learning to learn across diverse data biases in deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4072–4082 (2022)
Liu, J., Qin, H., Wu, Y., Guo, J., Liang, D., Xu, K.: CoupleFace: relation matters for face recognition distillation. In: Proceedings of the European Conference on Computer Vision (2022)
Liu, J., Qin, H., Wu, Y., Liang, D.: AnchorFace: boosting tar@ far for practical face recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022)
Liu, J., et al.: DAM: discrepancy alignment metric for face recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3814–3823 (2021)
Liu, J., Zhou, S., Wu, Y., Chen, K., Ouyang, W., Xu, D.: Block proposal neural architecture search. IEEE Trans. Image Process. 30, 15–25 (2020)
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, pp. 212–220 (2017)
Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML, vol. 2, p. 7 (2016)
Meng, Q., Zhao, S., Huang, Z., Zhou, F.: MagFace: a universal representation for face recognition and quality assessment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14225–14234 (2021)
Ranjan, R., Castillo, C.D., Chellappa, R.: L2-constrained softmax loss for discriminative face verification. arXiv preprint arXiv:1703.09507 (2017)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900 (2015)
Sun, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6398–6407 (2020)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: 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 (2014)
Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Signal Process. Lett. 25(7), 926–930 (2018)
Wang, F., Xiang, X., Cheng, J., Yuille, A.L.: NormFace: L2 hypersphere embedding for face verification. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1041–1049 (2017)
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018)
Wang, M., Deng, W.: Deep face recognition: a survey. arXiv 2018. arXiv preprint arXiv:1804.06655 (2018)
Wang, M., Deng, W.: 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)
Wang, M., Deng, W., Hu, J., Tao, X., Huang, Y.: Racial faces in the wild: reducing racial bias by information maximization adaptation network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 692–702 (2019)
Wang, X., Zhang, S., Wang, S., Fu, T., Shi, H., Mei, T.: Mis-classified vector guided softmax loss for face recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12241–12248 (2020)
Whitelam, C., et al.: IARPA Janus Benchmark-B face dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 90–98 (2017)
Xu, X., et al.: Consistent instance false positive improves fairness in face recognition. In: CVPR, pp. 578–586 (2021)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)
Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5409–5418 (2017)
Zhang, X., Zhao, R., Qiao, Y., Wang, X., Li, H.: 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 (2019)
Zhu, Z., et al.: WebFace260M: a benchmark unveiling the power of million-scale deep face recognition. In: CVPR, pp. 10492–10502, June 2021
Acknowledgments
This research was supported by National Natural Science Foundation of China under Grant 61932002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, J. et al. (2022). OneFace: One Threshold for All. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13672. Springer, Cham. https://doi.org/10.1007/978-3-031-19775-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-19775-8_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19774-1
Online ISBN: 978-3-031-19775-8
eBook Packages: Computer ScienceComputer Science (R0)