A statistical approach for person verification using human behavioral patterns

We propose a person verification method using behavioral patterns of human upper body motion. Behavioral patterns are represented by three-dimensional features obtained from a time-of-flight camera. We take a statistical approach to model the behavioral patterns using Gaussian mixture models (GMM) and support vector machines. We employ the maximum likelihood linear regression adaptation method to estimate GMM parameters with a limited amount of data. Experimental results show that it reduced by 28.6% the relative equal error rates from a system using the maximum likelihood estimation with 25 samples per subject. We also demonstrate that the proposed approach is robust against variations in body motion over time.