Masked Face Recognition With Mask Transfer and Self-Attention Under the COVID-19 Pandemic

IEEE Access

卷:10

Rujie Liu¹  Daisuke Deguchi²  Meng Zhang²  Hiroshi Murase² 

[1] Fujitsu Research and Development Center, Beijing, China;

[2] Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan;

关键词: Masked face recognition;    mask transfer;    face synthesis;    attention mechanism;   

DOI  :  10.1109/ACCESS.2022.3150345

来源: DOAJ

【 摘 要 】

Face masks bring a new challenge to face recognition systems especially against the background of the COVID-19 pandemic. In this paper, a method used for mitigating the negative effects of mask defects on face recognition is proposed. Firstly, a low-cost, accurate method of masked face synthesis, i.e. mask transfer, is proposed for data augmentation. Secondly, an attention-aware masked face recognition (AMaskNet) is proposed to improve the performance of masked face recognition, which includes two modules: a feature extractor and a contribution estimator. Therein, the contribution estimator is employed to learn the contribution of the feature elements, thus achieving refined feature representation by simple matrix multiplications. Meanwhile, the end-to-end training strategy is utilized to optimize the entire model. Finally, a mask-aware similarity matching strategy (MS) is adopted to improve the performance in the inference stage. The experiments show that the proposed method consistently outperforms on three masked face recognition datasets: RMFRD, COX and Public-IvS. Meanwhile, a qualitative analysis experiments using CAM indicate that the contribution learned by AMaskNet is more conducive to masked face recognition.

【 授权许可】

Unknown