Temporal-spatial face recognition using multi-atlas and Markov process model

Although video-based face recognition algorithms can provide more information than image-based algorithms, their performance is affected by subjects´ head poses, expressions, illumination and so on. In this paper, we present an effective video-based face recognition algorithm. Multi-atlas is employed to efficiently represent faces of individual persons under various conditions, such as different poses and expressions. The Markov process model is used to propagate the temporal information between adjacent video frames. The combination of multi-atlas and Markov model provides robust face recognition by taking both spatial and temporal information into account. The performance of our algorithm was evaluated on three standard test databases: the Honda/UCSD video database, the CMU Motion of Body database, and the multi-modal VidTIMIT database. Experimental results demonstrate that our video-based face recognition algorithm outperforms other methods on all three test databases.