A directional margin paradigm for noise suppression in face recognition

Abstract

Convolutional neural networks (CNN) have achieved outstanding face recognition (FR) performance with increasing large-scale face datasets. With face dataset size grown, noisy data will inevitably increase, undoubtedly bringing difficulties to data cleaning. In this paper, the probability that the sample belongs to noise can be determined based on the cosine distance (cosθ) of normalized angle center and face feature vector in the margin-based loss functions. According to this finding, we propose a two-step learning method integrated into the loss function. The new proposed directional margin loss function combines the noise probability with the label as the supervision information. Experiments show that our method can tolerate noisy data and get high FR accuracy when the training datasets mix with more than 30% noise. Our approach can also achieve a great result of 79.33% in MegaFace challenge one using a noisy training dataset.