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Recognizing human facial expression and emotion by computer is an interesting and challenging problem. We propose a method for recognizing emotions through facial expressions displayed in video sequences. We introduce a tree-augmented naive-Bayes (TAN) classifier that learns the dependencies between facial features; we also provide an algorithm for finding the best TAN structure. Our person-dependent and person-independent experiments show that using this TAN structure provides significantly better results than using simpler NB-classifiers.