Video shot classification using human faces

People usually make up a lot of the information content in videos. The abilities to answer queries and facilitate browsing related to people in videos are crucial. In a single video sequence, a particular person may appear multiple number of times. We propose a scheme to automatically detect the repeated occurrences of the same people to enable fast people related searching. In particular, we propose a video shot classification scheme using human faces, regardless of scale and background. Video shots are classified by clustering facial features extracted from these shots. Potential applications include video indexing and browsing. Employing unsupervised clustering algorithms, this scheme requires no human intervention. Experimental results on a 4-minute news sequence show that it achieves encouraging results.