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.
Keywords: Person verification; Behavioral biometrics; GMM; SVM; Time-of-flight camera
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