Abstract
Fairface recognition aims to the mitigate the bias between different attributes in face recognition task while maintaining the state-of-art accurancy. It is a challenging task due to high variances between different attributes and unbalancement of data. In this work, we provide an approach to make a fairface recognition by using asymmetric-arc-loss training and multi-step finetuning. First, we train a general model with an asymmetric-arc-loss, and then, we make a mutli-step finetuning to get higher auc and lower bias. Besides, we propose another viewpoint on reducing the bias and use bag of tricks such as reranking, boundary cut and hard-sample model ensembling to improve the performance. Our approach achieved the first place at ECCV 2020 ChaLearn Looking at People Fair Face Recognition Challenge.
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Zhou, S., Luo, J., Zhou, J., Ji, X. (2020). AsArcFace: Asymmetric Additive Angular Margin Loss for Fairface Recognition. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12540. Springer, Cham. https://doi.org/10.1007/978-3-030-65414-6_33
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DOI: https://doi.org/10.1007/978-3-030-65414-6_33
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