Motion capture as data source for gait-based human identification

The authors present results of the research aiming at human identification based on tensor representation of the gait motion capture 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 software 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.