Best view selection with geometric feature based face recognition

Nowadays, an important problem in multi-camera systems is how to select the camera with the best frontal view of a person in order to visualize to an observer. Therefore, we present a minimum score based criterion for best view selection, based on face recognition with geometric features. In this approach, faces are represented with Curve Edge Maps (CEMs), which are collections of polynomial curves with a convex region. Face recognition is performed by matching face CEMs driven by histograms of intensities and histograms of relative positions. The resulting face recognition scores are employed as quality-of-view measures. They indicate whether or not persons are seen by cameras in frontal view. Experiments show that the method is robust and efficient when selecting the best view in a multi-camera system. Furthermore, our method outperforms view selection based on face detection only.