Refining Uncertain Features With Self-Distillation for Face Recognition and Person Re-Identification

Deep recognition models aim to recognize targets with various quality levels in uncontrolled application circumstances, and typically low-quality images usually retard the recognition performance dramatically. As such, a straightforward solution is to restore low-quality input images as pre-processing during deployment. However, this scheme cannot guarantee that deep recognition features of the processed images are conducive to recognition accuracy. How deep recognition features of low-quality images can be refined during training to optimize recognition models has largely escaped research attention in the field of metric learning. In this paper, we propose a quality-aware feature refinement framework based on the dedicated quality priors obtained according to the recognition performance, and a novel quality self-distillation algorithm to learn recognition models. We further show that the proposed scheme can significantly boost the performance of the recognition model with two popular deep recognition tasks, including face recognition and person re-identification. Extensive experimental results provide sufficient evidence on the effectiveness and impressive generalization capability of the proposed framework. Moreover, our framework can be essentially integrated with existing state-of-the-art classification loss functions and network architectures, without extra computation costs during deployment.