Abstract
Face recognition has achieved remarkable improvements with the help of the angular margin based softmax losses. However, the margin is usually manually set and kept constant during the training process, which neglects both the optimization difficulty and the informative similarity structures among different instances. Although some works have been proposed to tackle this issue, they adopt similar methods by simply changing the margin for different classes, leading to limited performance improvements. In this paper, we propose a novel sample-wise adaptive margin loss function from the perspective of the hypersphere manifold structure, which we call companion guided soft margin (CGSM). CGSM introduces the information of distribution in the feature space, and conducts teacher-student optimization within each mini-batch. Samples of better convergence are considered as teachers, while students are optimized with extra soft penalties, so that the intra-class distances of inferior samples can be further compacted. Moreover, CGSM does not require sophisticated mining techniques, which makes it easy to implement. Extensive experiments and analysis on MegaFace, LFW, CALFW, IJB-B and IJB-C demonstrate that our approach outperforms state-of-the-art methods using the same network architecture and training dataset.
Y. Su and Y. Wu—Contributed equally to this work.
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Notes
- 1.
For open-set protocol, the testing identities are usually disjoint from the training set [12].
- 2.
For simplicity, in this paper, we use the weight of FC layers as the center.
- 3.
In fact, we try to reimplement this method but get non-convergence result. The open source project claimed by [11] is blank, and many researchers also report the same problem on github.
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Acknowledgement
This work was supported in part by Beijing Postdoctoral Research Foundation, the National Natural Science Foundation of China under Grant Nos. U19B2034 and 61836014.
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Su, Y. et al. (2021). Companion Guided Soft Margin for Face Recognition. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12459. Springer, Cham. https://doi.org/10.1007/978-3-030-67664-3_30
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