The choice of methods for the construction of PCA-based features and the selection of SVM parameters for person identification by gait

The paper presents the results of the continued research of the principal component analysis (PCA) and support vector machine (SVM) techniques for person identification by gait. The experimental studies performed using the CASIA GAIT dataset allowed us to compare two methods of work with input data for PCA. According to the results of experiments, the optimal method of forming the feature space and the most effective parameters of the SVM-classifier were selected. The classification of video sequences recorded by video cameras located frontally, at an angle and orthogonal to the direction of objects movement was carried out.