CoReFace: Sample-guided Contrastive Regularization for Deep Face Recognition

The discriminability of the feature representation is crucial for face recognition. However, previous methods rely solely on the learnable weights of the classification layer, which represent the identities. This reliance could be problematic as the evaluation process depends on the similarity between pairs of face images and requires minimal identity information learned during training. As a result, there is an inconsistency between the training and evaluation processes, which can confuse the feature encoder and hinder the effectiveness of identity-based methods. To address this problem, we propose a novel approach namely Contrastive Regularization for Face Recognition (CoReFace), which applies sample-level regularization in feature representation learning. Specifically, we employ sample-guided contrastive learning to directly regularize the training based on the sample-sample relationship and thus align it with the evaluation process. To avoid image quality degradation, we augment the embeddings instead of the images in order to integrate contrastive learning into face recognition. Additionally, we introduce a new contrastive loss function for the regularization of representation distribution. This function incorporates an adaptive margin and a supervised contrastive mask to ensure stable loss values and prevent interference with the identity supervision signals. Finally, we explore new pair-coupling protocols in order to overcome the problem of semantically repetitive signals in contrastive learning. Extensive experiments demonstrate the efficacy and efficiency of our CoReFace approach, which achieves competitive results compared to state-of-the-art methods. Code could be found https://github.com/IsidoreSong/CoreFace here.