Masked face recognition using domain adaptation

Abstract

Wearing facial masks has become a must in our daily life due to the global COVID-19 pandemic. However, the performance of a face recognition system is severely degraded due to the fact that the face images in the gallery are unmasked faces while the probe face images captured by the camera are masked faces, making the probe face images different from gallery face images in the activated region and the distribution domain. In this paper, we propose a novel face recognition system to address the issue. The system is integrated with a domain adaptation layer and a feature refinement layer. The feature refinement layer is based on the structure of the self-attention mechanism to align activated regions of unmasked faces with those of masked faces. The domain adaptation layer works by adapting the system from the unmasked face domain to the synthetically masked face domain and the real- world masked face domain. The system is tested on real-world data through face verification and face identification. The face verification accuracy is improved by 6.83% for the RMFD_FV dataset and 4.2% for the MFR2 dataset, and the face identification accuracy is improved by 15.43% for the MFRFI dataset.