Masked Face Recognition with Latent Part Detection

This paper focuses on a novel task named masked faces recognition (MFR), which aims to match masked faces with common faces and is important especially during the global outbreak of COVID-19. It is challenging to identify masked faces for two main reasons. Firstly, there is no large-scale training data and test data with ground truth for MFR. Collecting and annotating millions of masked faces is labor-consuming. Secondly, since most facial cues are occluded by mask, it is necessary to learn representations which are both discriminative and robust to mask wearing. To handle the first challenge, this paper collects two datasets designed for MFR: MFV with 400 pairs of 200 identities for verification, and MFI which contains 4,916 images of 669 identities for identification. As is known, a robust face recognition model needs images of millions of identities to train, and hundreds of identities is far from enough. Hence, MFV and MFI are only considered as test datasets to evaluate algorithms. Besides, a data augmentation method for training data is introduced to automatically generate synthetic masked face images from existing common face datasets. In addition, a novel latent part detection (LPD) model is proposed to locate the latent facial part which is robust to mask wearing, and the latent part is further used to extract discriminative features. The proposed LPD model is trained in an end-to-end manner and only utilizes the original and synthetic training data. Experimental results on MFV, MFI and synthetic masked LFW demonstrate that LPD model generalizes well on both realistic and synthetic masked data and outperforms other methods by a large margin.