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

• URL: http://arxiv.org/abs/2108.01513v1
• Date: Tue, 3 Aug 2021 13:58:45 GMT
• Title: SphereFace2: Binary Classification is All You Need for Deep Face Recognition
• Authors: Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, Rita Singh
• Abstract summary: State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-class classification framework. We propose a novel binary classification training framework, termed SphereFace2. We show that SphereFace2 can consistently outperform current state-of-the-art deep face recognition methods.
• Score: 57.07058009281208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 first identify 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. We conduct comprehensive experiments on popular benchmarks to demonstrate that SphereFace2 can consistently outperform current state-of-the-art deep face recognition methods.

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