Improving Masked Face Recognition Using Dense Residual Unit Aided with Quadruplet Loss

All over the world, people are wearing face masks and practising social distancing to protect themselves against the Coronavirus disease (COVID-19). The need for contactless biometric systems has increased to avoid the common point of contact. Among contactless biometric systems, facial recognition systems are the most economical and effective ones. Conventional face recognition systems rely heavily upon the facial features of the eyes, nose, and mouth. But due to people wearing face masks, the important facial features of the nose and mouth get hidden under the mask, resulting in degraded performance by the facial recognition systems on masked faces. In this paper, we propose a Dense Residual Unit (DRU) aided with Quadruplet loss on top of existing facial recognition systems. This solution tries to unveil the masked faces by producing embeddings for masked faces, which are similar to embeddings of unmasked faces of the same identity but different from embeddings of different identities. We have evaluated our method using two pre-trained facial recognition models' backbones, i.e. Resnet-101 and MobileFaceNet, and upon two datasets, among them, one is a real-world dataset, i.e. MFR2, and one is a simulated masked face dataset i.e. masked version of LFW. We have achieved improvement in the performance of masked face recognition in terms of False Match Rate, False Non-Match Rate, Fisher Discriminant ratio, and Equal Error Rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Triplet Decoupling Network for Masked Face Verification

Face verification has been widely applied to identity authentication in many areas. However, due to the mask information embedded into the facial feature representation, existing face verification systems generally fail to identify the faces covered by masks during the COVID-19 coronavirus epidemic period. To address this issue, we propose a new triplet decoupling network (TDN) for the challenging masked face verification. Different from existing works, our proposed TDN seeks to remove the mask information included in extracted facial features by feature decoupling, such that more discriminative facial feature representations can be obtained for masked face verification. In addition, a new triplet similarity margin loss (TSM) is designed to enlarge the margin between the intra-class similarity and the inter-class similarity of faces. Experimental results show that the proposed method significantly outperforms the other state-of-the-art methods on masked face datasets, which demonstrates the effectiveness of our proposed method. © 2021 IEEE.