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.

本文介绍了一个统一框架来理解超球面人脸识别中的大角度间隔，进一步扩展了 SphereFace 的研究并提出了一个稳定性较好的改进版，结果表明这个改进版在识别准确率上比当前最先进的方法更好或竞争力更强。