Knot Magnify Loss for Face Recognition

Deep Convolutional Neural Netowrks (DCNN) have significantly improved the performance of face recognition in recent years. Softmax loss is the most widely used loss function for training the DCNN-based face recognition system. It gives the same weights to easy and hard samples in one batch, which would leads to performance gap on the quality imbalanced data. In this paper, we discover that the rare hard samples in the training dataset has become a main obstacle for training a robust face recognition model. We propose to address this problem by a new supervisor signal that pays more attention to the rare hard samples and reduces the effects of the easy samples relatively. Our proposed novel Knot Magnify (KM) loss modulates the classical softmax loss to suppress the influence of easy samples and up-weight the loss of hard samples during training. Our results show that after training with KM loss, face recognition model is able to get competing accuracy on the well-known face recognition benchmark LFW dataset and the challenging CFP dataset.