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This paper contributes to the concept of activity-related biometric authentication in ambient Intelligence environments. The motivation behind the proposed approach derives from activity-related biometrics and is mainly focusing on everyday activities. The activity sequence is captured by a stereoscopic camera and the resulting 2.5D data are processed to extract valuable unobtrusive activity-related features. The novel contribution of the current work lies in the warping of the extracted movements trajectories, so as to compensate for different environmental settings. Authentication is performed utilizing both HMM and GMMs.
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