State-of-the-art deep face recognition methods are mostly trained with a
softmax-based multi-class classification framework. Despite being popular and
effective, these methods still have a few shortcomings that limit empirical
performance. In this paper, we start by identifying the discrepancy between
training and evaluation in the existing multi-class classification framework
and then discuss the potential limitations caused by the "competitive" nature
of softmax normalization. Motivated by these limitations, we propose a novel
binary classification training framework, termed SphereFace2. In contrast to
existing methods, SphereFace2 circumvents the softmax normalization, as well as
the corresponding closed-set assumption. This effectively bridges the gap
between training and evaluation, enabling the representations to be improved
individually by each binary classification task. Besides designing a specific
well-performing loss function, we summarize a few general principles for this
"one-vs-all" binary classification framework so that it can outperform current
competitive methods. Our experiments on popular benchmarks demonstrate that
SphereFace2 can consistently outperform state-of-the-art deep face recognition
methods. The code has been made publicly available.

本文提出了一种新颖的二分类训练框架 SphereFace2，通过绕过 softmax normalization 和相应的封闭集假设，使得训练与评估之间的差距得以弥合，有效提高了表征的性能，实验证明 SphereFace2 可以持续超过现有最佳深度人脸识别方法。