Gait exhibits several advantages with respect to other biometrics features: acquisition can be performed through cheap technology, at a distance and without people collaboration. In this paper we perform gait analysis using skeletal data provided by the Microsoft Kinect sensor. We defined a rich set of physical and behavioral features aiming at identifying the more relevant parameters for gait description. Using SVM we showed that a limited set of behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.
In the work REF , gait analysis is performed using skeletal data provided by Microsoft Kinect sensors and a set of physical and behavioural features is defined, in order to identify the more relevant parameters for gait description.
Human Classification Using Gait Features
BIOMET
Chemistry Computer Science