A novel approach inspired by optic nerve characteristics for few-shot occluded face recognition

Abstract

Although there has been a growing body of work for face recognition, it is still a challenging task for faces under occlusion with limited training samples. In this work, we propose a novel framework to address the problem of few-shot occluded face recognition. In particular, inspired by the human being’s optic nerves characteristics that humans recognize the face under occlusion using contextual information rather than paying attention to the facial parts, we propose an effective feature extraction approach to capture the local and contextual information for face recognition. To enhance the robustness, we further introduce an adaptive fusion method to incorporate multiple features, including the proposed structural element feature, connected-granule labeling feature, and Reinforced Centrosymmetric Local Binary Pattern (RCSLBP). Final recognition is derived from the fusion of all classification results according to our proposed novel fusion method. Experimental results on three popular face image datasets of AR, Extended Yale B, and LFW demonstrate that our method performs better than many existing ones for few-shot face recognition in the presence of occlusion.