Recognition of Faces Wearing Masks Using Skip Connection Based Dense Units Augmented With Self Restrained Triplet Loss

Abstract

Facial recognition-based systems are the most efficient and cost-effective of all the contactless biometric verification systems available. But, in the COVID-19 scenario, the performance of available facial recognition systems has been affected badly due to the presence of masks on people's faces. Various studies have reported the degradation of the performance of facial recognition systems due to masks. Therefore, there is a need for improvement in the performance of currently available facial recognition algorithms. In this research, we propose using Skip Connection based Dense Unit (SCDU) trained with Self Restrained Triplet Loss, to handle the embeddings produced by existing facial recognition algorithms for masked images. The SCDU is trained to make facial embeddings for unmasked and masked images of the same identity similar, as well as, embeddings for unmasked and masked images of different identities dissimilar. We have evaluated our results on the LFW dataset with synthetic masks as well as the real-world masked face recognition dataset, i.e., MFR2 and achieved improvement in verification performance in terms of Equal Error Rate, False Match Rate, False Non-Match Rate, and Fisher discriminant ratio.