Authentication of Facial Images with Masks Using Periocular Biometrics

Due to COVID-19 pandemic, wearing a mask rapidly becomes a new social norm that people in the society should comply. Although it is altruistic behavior preventing all people from serious infections, it brings about hassles for individuals. For example, when a face is partly covered with a mask, identification using a face recognition is going to be malfunctioning. To this end, we propose a novel computational framework that enables the personal authentication with a partial face image, a face covered with a mask. For the experiments, we constructed the datasets of facial images containing the periocular regions only, extracted from full facial images covered with the mask. Given the datasets, we trained our framework, a variant of a Siamese network, with various configuration of hyper-parameters. As a result, RMSprop optimizer with the learning rate 1 x 10(-5) trained from periocular datasets showed the highest accuracy for the personal authentication. Next, we conducted a comparative experiment with our proposal and the model trained with datasets containing the full facial regions. When testing with the periocular region images, our proposal is superior in the authentication accuracy to that of the model trained with the full facial regions. This result raises the optimistic expectation that in the era of COVID-19, facial images covered with mask can still be used for the authentication using face recognition at a nearly same level of accuracy. This means that people can use the face recognition applications without taking off the mask, which provides the safe circumstances against the infections.