SphereFace Revived: Unifying Hyperspherical Face Recognition
By Weiyang Liu, Yandong Wen, Bhiksha Raj, Rita Singh, Adrian Weller. 2021
60 citations recorded by the source corpus.
This paper addresses the deep face recognition problem under an open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. To this end, hyperspherical face recognition, as a promising line of research, has attracted increasing attention and gradually become a major focus in face recognition research. As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin. However, SphereFace still suffers from severe training instability which limits its application in practice. In order to address this problem, we introduce a unified framework to understand large angular margin in hyperspherical face recognition. Under this framework, we extend the study of SphereFace and propose an improved variant with substantially better training stability – SphereFace-R. Specifically, we propose two novel ways to implement the multiplicative margin, and study SphereFace-R under three different feature normalization schemes (no feature normalization, hard feature normalization and soft feature normalization). We also propose an implementation strategy – “characteristic gradient detachment” – to stabilize training. Extensive experiments on SphereFace-R show that it is consistently better than or competitive with state-of-the-art methods.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
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In Computer Vision and Pattern Recognition
- Adrian Weller
- Zhen Liu
- Weiyang Liu
- Lanqing Hong
- L. Yu
- Tianyang Hu
- Rongmei Lin
- Krzysztof Marcin Choromanski
In Signal Processing
Citations in the corpus
- Continual Learning by Modeling Intra-Class Variation (2022, background): “There are many other possible forms for ∆θ in practice (Liu et al., 2022).”