Abstract

Context information other than faces, such as clothes, picture- taken-time and some logical constraints, can provide rich cues for recog- nizing people. This aim of this work is to automatically cluster pictures according to person’s identity by exploiting as much context information as possible in addition to faces. Toward that end, a clothes recognition al- gorithm is first developed, which is effiective for different types of clothes (smooth or highly textured). Clothes recognition results are integrated with face recognition to provide similarity measurements for clustering. Picture-taken-time is used when combining faces and clothes, and the cases of faces or clothes missing are handled in a principle way. A spectral clus- tering algorithm which can enforce hard constraints (positive and nega- tive) is presented to incorporate logic-based cues (e.g. two persons in one picture must be different individuals) and user feedback. Experiments on real consumer photos show the effiectiveness of the algorithm.