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The authors present results of the research aiming at human identification based on gait motion capture data. Tensor objects were chosen as the appropriate representation of data. High-dimensional tensor samples were reduced by means of the multilinear principal component analysis (MPCA). For the purpose of classification the following methods from the WEKA library were used: k Nearest Neighbors (kNN), Naive Bayes, Multilayer Perceptron, and Radial Basis Function Network. The maximum value of the correct classification rate (CCR) was achieved for the classifier based on the multilayer perceptron.

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