We propose a person verification method based on behav-
ioral patterns from complex human m
ovements. Behavioral patterns are
represented by anthropometric and kinematic features of human body
motion acquired by a Kinect RGBD sensor. We focus on complex move-
ments 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 er-
ror rate was 15.7% when using subject-preferred movements and 27.3%
when using a predefined sequence of movements.
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