LightQNet: Lightweight Deep Face Quality Assessment for Risk-Controlled Face Recognition

End-to-end face quality assessment based on deep learning can directly predict the overall quantitative score of face quality, thus helping to control the risk of face recognition system. Thanks to the development of automatic quality pseudo-label generation, most recent methods can use large-scale face datasets to learn the quality model. However, existing methods use regression models to fit the pseudo-labels, which lack attention to samples that are easy to be misidentified, and require large models for training. The paper treats the quality assessment as a classification problem, focusing on difficult samples near the classification boundary. Specifically, pairwise binary quality pseudo-label is generated based on the face similarity score without additional manual annotation. An identification quality loss is used to decouple the pairwise network training. In addition, a lightweight quality network is trained by performing knowledge distillation on the quality prediction branch of the face recognition network. Experiments show that the proposed quality network achieves state-of-the-art results with only 0.45 M parameters and 77 M FLOPs.