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.