Comparative evaluation of face sequence matching for content-based video access

Abstract

The paper presents a comparative evaluation of matching methods of face sequences obtained from actual videos. Face information is quite important in videos, especially in news programs, dramas, and movies. Accurate face sequence matching enables many multimedia applications including content-based face retrieval, automated face annotation, video authoring, etc. However, face sequences in videos are subject to variation in lighting condition, pose, facial expression, etc., which cause difficulty in face matching. In order to cope with this problem, several face sequence matching methods are proposed by extending face still image matching, traditional pattern recognition, and recent pattern recognition techniques. They are expected to be applicable to face sequences extracted from actual videos. The performance of these methods is evaluated as the accuracy of face sequence annotation using the methods. The accuracy is evaluated using a considerable amount of actual drama videos. The evaluation results reveal merits and demerits of these methods, and indicate future research directions of face matching for videos.