Adaptive Face Recognition for Multi-Type Occlusions

Due to the prevalence of influenza outbreaks and outdoor scenarios with various obstructing decorations, recognizing faces with occlusions has become a pressing challenge to address. However, current research mainly focuses on facial recognition with one kind of occlusion and does not provide compatible solutions for different kinds of common occlusions like glasses, sunglasses, and masks. Therefore, an Adaptive Multi-Type Occluded Face Recognition Model (AMOFR) is proposed to effectively handle multiple occlusion types simultaneously in this paper. In AMOFR, a generator is developed to produce diverse occluded face images for training, achieved by simulating various occlusion types on unoccluded face images. Subsequently, an occlusion type-based adapter is formulated to address a range of occlusion scenarios, guided by prompts from a Visual-Language model. To enhance overall performance by leveraging complete facial information, a feature-level knowledge distillation loss function is implemented, facilitating joint learning of unoccluded-face and occluded-face features. Furthermore, a new sunglasses-wearing dataset (CALFW-SUNGLASSES) is generated for more comprehensive test for AMOFR and further occlusion recognition research. Experimental results on datasets containing different types of occlusions have demonstrated that AMOFR achieves significantly higher accuracy compared to other advanced face recognition models. The implementation codes of AMOFR is available at https://github.com/LIU-YUXI/Adaptive-Multi-occlusion-Face-Recognition.