Exemplar extraction using spatio-temporal hierarchical agglomerative clustering for face recognition in video

Many recent works have attempted to improve object recognition by exploiting temporal dynamics, an intrinsic property of video sequences. In this paper, a new spatio-temporal hierarchical agglomerative clustering (STHAC) method is proposed for automatic extraction of face exemplars for face recognition in video sequences. Two variants of STHAC are presented - a global variety that unifies spatial and temporal distances between points, and a local variety that introduces perturbation of distances based on a local spatio-temporal neighborhood criterion. Faces that are nearest to the cluster means are chosen as exemplars for the testing stage, where subjects in the test video sequences are recognized using a probabilistic-based classifier. Extensive evaluation on a face video database demonstrates the effectiveness of our proposed method, and the significance of incorporating temporal information for exemplar extraction.