MaskDUF: Data uncertainty learning in masked face recognition with mask uncertainty fluctuation

Abstract

As an essential component of experts and intelligent systems, Masked Face Recognition (MFR) has been applied to various applications, but it is still a challenging task due to the aggravated uncertainty caused by mask occlusion. Most existing MFR methods are deterministic point embedding models that are limited in representing the uncertainty in masked face images. Data Uncertainty Learning (DUL) is an advanced uncertainty modeling method, but there are two problems when it is applied to MFR task: (1) the masked face tends to be regarded as noise due to the mask occlusion, which weakens its optimization; (2) the large representation difference between face and masked face results in a dispersed intra-class distribution. To solve the above problems, we propose a novel masked face data uncertainty learning method (MaskDUF) for MFR task, which can adaptively adjust the optimization weight by modeling the uncertainty and measuring the recognizability of samples, thus learning an ideal sample distribution with compact intra-class, discrepant inter-class and distant noise. Specifically, a Hard Kullback–Leibler Divergence (H-KLD) method is proposed to serve as an adaptive variance regularizer for masked faces, which contributes to learning more accurate uncertainty representations and avoiding overfitting noise. Moreover, by combining feature magnitude and variance uncertainty, Mask Uncertainty Fluctuation (MUF) is proposed to comprehensively measure sample recognizability, which contributes to enhancing the learning preference of masked faces and constructing a more compact cone-like intra-class distribution. Finally, compared with other advanced models, MaskDUF achieves an average accuracy improvement of 1.33% to 13.28%, and its effectiveness and strong robustness are also proved by ablation study, noise experiment and parametric analysis.