SphereFace Revived: Unifying Hyperspherical Face Recognition

• URL: http://arxiv.org/abs/2109.05565v1
• Date: Sun, 12 Sep 2021 17:07:54 GMT
• Title: SphereFace Revived: Unifying Hyperspherical Face Recognition
• Authors: Weiyang Liu, Yandong Wen, Bhiksha Raj, Rita Singh, Adrian Weller
• Abstract summary: We introduce a unified framework to understand large angular margin in hyperspherical face recognition. Under this framework, we propose an improved variant with substantially better training stability -- SphereFace-R. We show that SphereFace-R is consistently better than or competitive with state-of-the-art methods.
• Score: 57.07058009281208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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