Periocular Biometric Recognition for Masked Faces

Since the outbreak of Coronavirus Disease 2019 (COVID-19), people are recommended to wear facial masks to limit the spread of the virus. Under the circumstances, traditional face recognition technologies cannot achieve satisfactory results. In this paper, we propose a face recognition algorithm that combines the traditional features and deep features of masked faces. For traditional features, we extract Local Binary Pattern (LBP), Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) features from the periocular region, and use the Support Vector Machines (SVM) classifier to perform personal identification. We also propose an improved Convolutional Neural Network (CNN) model Angular Visual Geometry Group Network (A-VGG) to learn deep features. Then we use the decision-level fusion to combine the four features. Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces, including frontal and side faces taken at different angles. Images with motion blur were also tested to evaluate the robustness of the algorithm. Besides, the experiment of matching a masked face with the corresponding full face is accomplished. The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition, and the periocular region has rich biological features and high discrimination.