LAUSR

Long-tailed distribution generally exists in large-scale face datasets, which poses challenges for learning discriminative feature in face recognition. Although a few works conduct preliminary research on this problem, the value of the tail data is still underestimated. This paper addresses the long-tailed problem from the perspective of maximally exploiting the tail data. We propose a Joint Alternating Training (JAT) framework to learn discriminative feature from both the long-tailed data and the tail data by using alternating training strategy. JAT consists of two branches: 1) the long-tailed data branch is adopted to learn the universal discrimination information from the whole long-tailed data with instance-balanced sampling. 2) the tail data branch is designed to exploit the discriminative information in the tail data with class-balanced sampling. To compensate the insufficient samples and lack of intra-class variations, we apply data augmentation (DA) to the tail data. We further propose margin-based mixup (MarginMix) for data augmentation, which can deal with the nonlinearity of margin-based softmax loss and stabilize the training process in mixup. Furthermore, we obtain the best combination of strategies (i.e., JAT+DA+ MarginMix) for long-tailed face recognition, which can maximally exploit the discriminative information in the tail data while retaining the universal discrimination learned from the long-tailed data. Extensive experiments on 8 face datasets demonstrate that our proposed methods and combination of strategies can effectively address the long-tailed problem in face recognition.