SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition

TL;DR

This paper introduces SFace, a novel loss function for deep face recognition that balances intra-class compactness and inter-class separation, especially handling low-quality images to improve robustness.

Contribution

The paper proposes the sigmoid-constrained hypersphere loss (SFace), which moderates gradient updates to prevent overfitting and enhances robustness in face recognition models.

Findings

SFace outperforms existing loss functions on multiple benchmarks.

Models trained with SFace show improved robustness to noisy data.

Extensive experiments validate the effectiveness of SFace across datasets.

Abstract

Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions. The existing algorithms devote to realizing an ideal idea: minimizing the intra-class distance and maximizing the inter-class distance. However, they may neglect that there are also low quality training images which should not be optimized in this strict way. Considering the imperfection of training databases, we propose that intra-class and inter-class objectives can be optimized in a moderate way to mitigate overfitting problem, and further propose a novel loss function, named sigmoid-constrained hypersphere loss (SFace). Specifically, SFace imposes intra-class and inter-class constraints on a hypersphere manifold, which are controlled by two sigmoid gradient re-scale functions respectively. The sigmoid curves precisely re-scale the intra-class and inter-class gradients so that training samples can be optimized to some degree. Therefore, SFace can make a better balance between decreasing the intra-class distances for clean examples and preventing overfitting to the label noise, and contributes more robust deep face recognition models. Extensive experiments of models trained on CASIA-WebFace, VGGFace2, and MS-Celeb-1M databases, and evaluated on several face recognition benchmarks, such as LFW, MegaFace and IJB-C databases, have demonstrated the superiority of SFace.