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In this paper, we propose a novel approach to dance pose recognition and body orientation estimation using multilinear analysis. By performing tensor decomposition and projection using silhouette images obtained from wide base-line binocular cameras, low dimensional pose and body orientation coefficient vectors can be extracted. Different from traditional tensor-based recognition methods, the proposed approach takes the pose coefficient vector as features to train a family of support vector machines as pose classifiers. Using the body orientation coefficient vectors, a one-dimensional orientation manifold is learned and further used for the estimation of body orientation. Experiment results obtained using both synthetic and real image data showed the efficacy of the proposed approach, and that our approach outperformed the traditional tensor-based approach in the comparative test.