Statistical person verification using behavioral patterns from complex human motion

Abstract

We propose a person verification method based on behavioral patterns from complex human movements. Behavioral patterns are represented by anthropometric and kinematic features of human body motion acquired by a Kinect RGBD sensor. We focus on complex movements to demonstrate that independent and rhythmic movement of body parts carries a significant amount of behavioral information. We take a statistical approach by Gaussian mixture models to model the individual behavioral patterns. We demonstrate that subject-preferred movements are more robust against forgery attacks and variations over time than predetermined subject-independent movements. The obtained equal error rate was 15.7% when using subject-preferred movements and 27.3% when using a predefined sequence of movements. © 2013 Springer-Verlag.