JAMsFace: joint adaptive margins loss for deep face recognition

Abstract

Deep feature learning has become crucial in large-scale face recognition, and margin-based loss functions have demonstrated impressive success in this field. These methods aim to enhance the discriminative power of the softmax loss by increasing the feature margin between different classes. These methods assume class balance, where a fixed margin is sufficient to squeeze intra-class variation equally. However, real-face datasets often exhibit imbalanced classes, where the fixed margin is suboptimal, limiting the discriminative power and generalizability of the face recognition model. Furthermore, margin-based approaches typically focus on enhancing discrimination either in the angle or cosine space, emphasizing one boundary while disregarding the other. To overcome these limitations, we propose a joint adaptive margins loss function (JAMsFace) that learns class-related margins for both angular and cosine spaces. This approach allows adaptive margin penalties to adjust adaptively for different classes. We explain and analyze the proposed JAMsFace geometrically and present comprehensive experiments on multiple face recognition benchmarks. The results show that JAMsFace outperforms existing face recognition losses in mainstream face recognition tasks. Specifically, JAMsFace advances the state-of-the-art face recognition performance on LFW, CPLFW, and CFP-FP and achieves comparable results on CALFW and AgeDB-30. Furthermore, for the challenging IJB-B and IJB-C benchmarks, JAMsFace achieves impressive true acceptance rates (TARs) of 89.09% and 91.81% at a false acceptance rate (FAR) of 1e-4, respectively.