Harnessing social context for improved face recognition

TLDR: Experimental results on two publicly available datasets show that social context information can improve face recognition and help bridge the gap between humans and machines in face recognition.

Abstract: Face recognition is traditionally based on features extracted from face images which capture various intrinsic characteristics of faces to distinguish between individuals. However, humans do not perform face recognition in isolation and instead utilize a wide variety of contextual cues as well in order to perform accurate recognition. Social context or co-occurrence of individuals is one such cue that humans utilize to reinforce face recognition output. A social graph can adequately model social-relationships between different individuals and this can be utilized to augment traditional face recognition methods. In this research, we propose a novel method to generate a social-graph based on a collection of group photographs and learn the social context information. We also propose a novel algorithm to combine results from a commercial face recognition system and social context information to perform face identification. Experimental results on two publicly available datasets show that social context information can improve face recognition and help bridge the gap between humans and machines in face recognition.