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。

SphereFace 再现：统一的超球面人脸识别

SphereFace Revived: Unifying Hyperspherical Face Recognition

Much research has been conducted on both face identification and face
verification, with greater focus on the latter. Research on face identification
has mostly focused on using closed-set protocols, which assume that all probe
images used in evaluation contain identities of subjects that are enrolled in
the gallery. Real systems, however, where only a fraction of probe sample
identities are enrolled in the gallery, cannot make this closed-set assumption.
Instead, they must assume an open set of probe samples and be able to
reject/ignore those that correspond to unknown identities. In this paper, we
address the widespread misconception that thresholding verification-like scores
is a good way to solve the open-set face identification problem, by formulating
an open-set face identification protocol and evaluating different strategies
for assessing similarity. Our open-set identification protocol is based on the
canonical labeled faces in the wild (LFW) dataset. Additionally to the known
identities, we introduce the concepts of known unknowns (known, but
uninteresting persons) and unknown unknowns (people never seen before) to the
biometric community. We compare three algorithms for assessing similarity in a
deep feature space under an open-set protocol: thresholded verification-like
scores, linear discriminant analysis (LDA) scores, and an extreme value machine
(EVM) probabilities. Our findings suggest that thresholding EVM probabilities,
which are open-set by design, outperforms thresholding verification-like
scores.

本文提出了一种基于开放式协议的人脸识别方法，并对不同的相似性评估策略进行了评估，发现使用 EVM 概率阈值法可以在一个开放式的协议下实现最佳性能。