Face recognition in multi-camera surveillance videos using Dynamic Bayesian Network

Face recognition in surveillance videos is inherently difficult due to the limitation of the camera hardware as well as the image acquisition process in which non-cooperative subjects are recorded in arbitrary poses and resolutions in different lighting conditions with noise and blurriness. Furthermore, as multiple cameras are usually distributed in a camera network and the subjects are moving, different cameras often capture the subject in different views. In this paper, we propose a probabilistic approach for face recognition suitable for a multi-camera video surveillance network. A Dynamic Bayesian Network (DBN) is used to incorporate the information from different cameras as well as the temporal clues from consecutive frames. The proposed method is tested on a public surveillance video dataset. We compare our method to different well-known classifiers with various feature descriptors. The results demonstrate that by modeling the face in a dynamic manner the recognition performance in a multi-camera network can be improved.