Masked Face Recognition with Generative Data Augmentation and Domain Constrained Ranking

Abstract

Masked faces recognition (MFR) aims to match a masked face with its corresponding full face, which is an important task especially during the global outbreak of COVID-19. However, most existing face recognition models generalize poorly in this case, and it is hard to train a robust MFR model due to two main reasons: 1) the absence of large scale training data as well as ground truth testing data, and 2) the presence of large intra-class variation between masked faces and full faces. To address the first challenge, this paper firstly contributes a new dataset denoted as MFSR, which consists of two parts. The first part contains 9,742 masked face images with mask region segmentation annotation. The second part contains 11,615 images of 1,004 identities, and each identity has masked and full face images with various orientations, lighting conditions and mask types. However, it is still not enough for training MFR models with deep learning. To obtain sufficient training data, based on the MFSR, we introduce a novel Identity Aware Mask GAN (IAMGAN) with segmentation guided multi-level identity preserve module to generate the synthetic masked face images from the full face images. In addition, to tackle the second challenge, a Domain Constrained Ranking (DCR) loss is proposed by adopting a center-based cross-domain ranking strategy. For each identity, two centers are designed which correspond to the full face images and the masked face images respectively. The DCR forces the feature of masked faces getting closer to its corresponding full face center and vice-versa. Experimental results on the MFSR dataset demonstrate the effectiveness of the proposed approaches.