Human identification based on the reduced kinematic data of the gait

We propose the method of human identification based on the reduced kinematic data of the gait. In the first stage the pose descriptions of the given skeleton model are reduced by the linear principal component analysis. We obtain the n-dimensional motion trajectories of principal components. Afterwards, we use two approaches: feature extraction and dynamic time warping. In the feature extraction the Fourier transform with low pass filtering is applied. To suppress the gait dynamic Fourier components for the velocities and accelerations are calculated. Such processing transforms gait´s data into the vector features space, in which the supervised learning is used to identify humans. To discover most valuable features - principal and Fourier components, PCA values, velocities and accelerations and to improve the classification, we prepare the features selection scenarios and observe the identification efficiency. To evaluate the proposed method we have collected gait database in the motion capture laboratory consisting of 353 motions of the 25 different people. We use preprocessing filters to detect the main double step and to scale time domain to the given number of motion frames. We have obtained satisfactory results with classification accuracy above 98%.