Context-aided Human Recognition- Clustering

Abstract. Context information other than faces, such as clothes, picturetaken-time and some logical constraints, can provide rich cues for recognizing 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 algorithm is first developed, which uses color and texture information and is effective 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 clustering algorithm which can enforce hard constraints (positive and negative) 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 effectiveness of the algorithm.