Face clustering in videos using constraint propagation
In this paper, we propose a novel approach to automatic detection and clustering of human faces presented in videos. In each video shot, continuously appearing human faces are firstly associated to form face sequences. Instead of matching the face sequences directly, we partition them into subsequences consisting of similar poses for the ease of comparison. Face subsequences can then be clustered by graph partitioning with the computed affinity matrix. Prior to that, however, a set of constraints need to be formulated so as to incorporate domain knowledge into the graph. Moreover, we propose a constraint propagation algorithm to fully exploit the space-level implications of these constraints. Experimental results demonstrate the effectiveness of our approach in identifying the main cast in movie clips.