IHEM Loss: Intra-Class Hard Example Mining Loss for Robust Face Recognition

Abstract

Recently, angular margin-based methods have become the mainstream approach for unconstrained face recognition with remarkable success. However, robust face recognition still remains a challenge, as the face is subject to variations in pose, age, expression, occlusion, and illumination, especially in unconstrained scenarios. Since the training dataset are always collected in unconstrained scenarios, it is inevitable that there’re significant number of hard examples in the training process. In this paper, we design a hard example selection function to effectively identify hard examples in the training procedure with the supervision of angular margin-based losses. Furthermore, a novel Intra-class Hard Example Mining (IHEM) loss function is proposed, which penalizes the cosine distance between the hard examples and their class centers to enhance the discriminative power of face representations. To ensure high performance for face recognition, we combine the supervision of angular margin-based loss and IHEM loss for model training. Specifically, during the training procedure, the angular margin-based loss guarantees the power of feature discrimination for face recognition, while the IHEM loss further encourages the intra-class compactness of hard example. Extensive results demonstrate the superiority of our approach.