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.

本研究提出了一种新的损失函数 ——sigmoid 限制超球面损失（SFace），可缓解过度拟合问题，实现对模型的精细控制，有效地解决了大规模数据集的深度面部识别问题。