A Benchmark on Masked Face Recognition

With the COVID-19 pandemic’s emergency, using facial masks and contactless biometric systems became even more relevant to reduce the risk of contamination. Several direct and indirect problems gained relevance with the pandemic. Among them, masked face recognition (MFR) aims to recognize a person even when the person is wearing a face mask. Some state-of-the-art algorithms that work well for unmasked faces have suffered a severe performance drop when receiving masked faces as input. In this sense, the scientific community proposed approaches and competitions related to this topic. In this paper, we introduce a comparative study of four prominent solutions pipelines that use different techniques to tackle the masked face recognition problem, proposed by Huber et al. [1], Neto et al. [2], Boutros et al. [3], and Hsu et al. [4]. The performance evaluation was conducted on a real masked face database (MFR2 [5]), and using synthetic masks in three mainstream databases (LFW, AgeDB30, and CFP-FP). We report results regarding unmasked-masked (U-M) and masked-masked (M-M) face verification performance. The unmasked-unmasked (U-U) scenario was also reported as a baseline to evaluate the drop of the selected models on non-occluded face verification. We further analyze the obtained results, generating a comprehensive comparative study of the selected approaches.