Efficient clustering of face sequences with application to character-based movie browsing

Human face has emerged as a useful feature in recent advances in semantic-based video analysis. Due to the large variation of facial poses in videos, conventional face recognition methods for authentication and identification generally experience difficulties in this particular application domain. Based on the recently proposed affinity propagation algorithm, we present in this paper a new method to match and cluster human faces with large pose variations. We also devise a novel approach to exploring the storyline of character(s) of particular interest for efficient movie navigation and browsing. Experimental results are reported to show the efficacy of our method.