LocalFace: Learning significant local features for deep face recognition

Abstract

As a widely mentioned topic in face recognition, the margin-based loss function enhances the discriminability of face recognition models by applying margin between class decision boundaries. However, there is still room to improve the representation of face features. Local face feature extraction has been employed in traditional face recognition methods, but with the increase of network depth in deep learning, the traditional approach requires a large number of computational resources. In this paper, we propose a novel face recognition architecture called LocalFace to extract local face features. First, by analyzing the distribution of significant features in face images, we propose an efficient face fixed-point local feature extraction approach and improve this method to propose a more effective face dynamic local feature extraction scheme. Subsequently, we propose a block-based random occlusion method for the limitations of the random face occlusion method to better simulate the occlusion situation in real scenes. In the end, we present a detailed discussion on the channel attention method that is more appropriate for face recognition and classification tasks. Our method enhances the representation of face features by ensembling local features into global features without extra parameters, which is efficient and easy to implement. Extensive experiments on various benchmarks demonstrate the superiority of our LocalFace, and part of the experimental results achieve SOTA results.