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arXiv · 2108.01513

SphereFace2: Binary Classification is All You Need for Deep Face Recognition

Yandong Wen·Weiyang Liu·Adrian Weller·Bhiksha Raj·Rita Singh
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Abstract

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.

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Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, Rita Singh. 2021-08-03. SphereFace2: Binary Classification is All You Need for Deep Face Recognition. https://arxiv.org/abs/2108.01513

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