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Many recent works in video-based face recognition involved the extraction of exemplars to summarize face appearances in video sequences. However, there has been a lack of attention towards modeling the causal relationship between classes and their associated exemplars. In this paper, we propose a novel Exemplar-Driven Bayesian Network (EDBN) classifier for face recognition in video. Our Bayesian framework addresses the drawbacks of typical exemplar-based approaches by incorporating temporal continuity between consecutive video frames while encoding the causal relationship between extracted exemplars and their parent classes within the framework. Under the EDBN framework, we describe a non-parametric approach of estimating probability densities using similarity scores that are computationally quick. Comprehensive experiments on two standard face video datasets demonstrated good recognition rates achieved by our method.