Dynamic Bayesian Network for Unconstrained Face Recognition in Surveillance Camera Networks
The demand for robust face recognition in real-world surveillance cameras is increasing due to the needs of practical applications such as security and surveillance. Although face recognition has been studied extensively in the literature, achieving good performance in surveillance videos with unconstrained faces is inherently difficult. During the image acquisition process, the noncooperative subjects appear in arbitrary poses and resolutions in different lighting conditions, together with noise and blurriness of images. In addition, multiple cameras are usually distributed in a camera network and different cameras often capture a subject in different views. In this paper, we aim at tackling this unconstrained face recognition problem and utilizing multiple cameras to improve the recognition accuracy using a probabilistic approach. We propose a dynamic Bayesian network to incorporate the information from different cameras as well as the temporal clues from frames in a video sequence. The proposed method is tested on a public surveillance video dataset with a three-camera setup. We compare our method to different benchmark 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 is improved over the other classifiers with various feature descriptors and the recognition result is better than using any of the single camera.