Evaluation of different feature sets for gait recognition using skeletal data from Kinect

Abstract

Gait is a persons manner of walking. It is a biometric that can be used for identifying humans. Gait is an unobtrusive metric that can be obtained from distance, and this is its main strength compared to other biometrics. In this paper we construct and evaluate feature sets with the purpose of finding out the role of different types of features and body parts in the recognition process. The feature sets were constructed from skeletal images in three dimensions made with a Kinect sensor. The Kinect is a low-cost device that includes RGB, depth and audio sensors. In our work automated gait cycle extraction algorithm was performed on the Kinect recordings. Metrics like angles and distances between joints were aggregated within a gait cycle, and from those aggregations the different feature datasets were constructed. Multilayer perceptron, support vector machine with sequential minimal optimization and J48 algorithms were used for classification on these datasets. At the end we give conclusions on which groups of features and body parts gave the best recognition rates.