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Multilinear analysis for task recognition and person identification

This paper introduces a Multi Factor Tensor(MFT) model to recognize motion styles and person identities in dance sequences. We apply a musical information analysis method in segmenting the motion sequence relevant to the key poses and the musical rhythm. We define a task model considering the repeated motion segments, where the motion is decomposed into person invariant factor task and person dependant factor style. We capture the motion data of different people for a few cycles, segment it using the musical analysis approach, normalize the segments using a vectorization method, and realize our MFT model. The experiments are conducted according to two approaches. Various experiments that we conduct to evaluate the potential of the recognition ability of our proposed approaches and the results demonstrate the high accuracy of our model. The recognition results and the motion decomposition will be used in further extending the motion generation process in various styles and for different tasks.