Ring-Regularized Cosine Similarity Learning for Fine-Grained Face Verification

Abstract Face verification aims to determine whether a pair of face images belong to the same person. Different from the traditional face verification, the negative sample pairs in fine-grained face verification are composed of similar face images, e.g., facial images of twins, which makes it still very challenging. In this paper, we investigate the fine-grained face verification problem via metric learning techniques, and propose a ring-regularized cosine similarity learning (RRCSL) method to distinguish the negative face pairs. The proposed RRCSL method seeks a linear transformation to enlarge the cosine similarity of intra-class and reduce the cosine similarity of inter-class as much as possible, and adaptively learns the norm of samples to the scaled circle by exploiting the ring regularization term simultaneously. Experimental results on three face datasets demonstrate the effectiveness of RRCSL for fine-grained face verification.