Weakly-supervised deep self-learning for face recognition

For recent years, state-of-the-art deep learning systems for face recognition task completely use supervised training. Their performances depend critically on the amount of manually-labeled examples and the correctness of label data. In real life, however, it is very costly and time-consuming to collect and label such database. Therefore, we intend to build a feasible self-learning system, handling the face images which are unlabeled. In this paper, we first build a challenging unlabeled database and propose an efficient Self-Learning DCNN structure (SL-DCNN) to handle weakly-supervised training for face recognition using complicated and unlabeled training data. Our main contribution is that we introduce a novel modification signal as an ingenious supervision to distinguish the misclassifications, and to correctly reduce intra-class variations and enlarge inter-class distances in combination with identification-verification. Then, we investigate the method of feature merging and whether rich identity improves feature learning under unlabeled data. Finally, 97.47% face verification accuracy on LFW [1] is impressively achieved by our method, which is much higher than state-of-the-art methods under noise.