Recognizing Faces of Moving People by Hierarchical Image-Set Matching

This paper proposes a novel method for recognizing faces in a cluster of moving people. In this task, there are two problems caused by motion, which are occlusions, and changes in facial pose and illumination. Multiple cameras are used to acquire near-frontal faces to avoid occlusions and profile faces. The hierarchical image-set matching (HISM) creates a distribution for each individual by integrating a set of face images of the same individual acquired from the multiple cameras. By adopting a method for comparing between test and training distributions in identification, variation in pose and illumination is alleviated, and good recognition accuracy can be obtained. Experimental results using video sequences containing 349 people show that the proposed method achieves high recognition performance compared with conventional methods, which use frame-by-frame identification and a distribution obtained from a single camera.