Probabilistic Bayesian network classifier for face recognition in video sequences

The inherent properties of video sequences allow for representation of data in both spatial and temporal dimensions. Using conventional image-based methods for face recognition in video is often an ineffective approach as the essential spatio-temporal properties are not fully harnessed. This paper proposes a probabilistic Bayesian network classifier to accomplish effective recognition of faces in video sequences. In our model, we introduce a joint probability function that encodes the causal dependencies between video frames, selected exemplars or representative images of a video, and subject classes. This enables both the temporal continuity between video frames and also the spatial relationships between exemplars and their respective exemplar-set classes to be captured. To simplify the tedious estimation of densities, the proposed method also utilizes probabilistic similarity scores that are computationally inexpensive. Good recognition rates were achieved by our proposed method in comprehensive experiments conducted on two standard face video datasets.