On the Potential of Activity Related Recognition

This paper proposes an innovative activity related authentication method for ambient intelligence environments, based on Hidden Markov Models (HMM). The biometric signature of the user is extracted, throughout the performance of a couple of common, every-day office activities. Specifically, the behavioral response of the user, stimuli related to an office scenario, such as the case of a phone conversation and the interaction with a keyboard panel is examined. The motion based, activity related, biometric features that correspond to the dynamic interaction with objects that exist in the surrounding environment are extracted in the enrollment phase and are used to train an HMM. The authentication potential of the proposed biometric features has been seen to be very high in the performed experiments. Moreover, the combination of the results of these two activities further increases the authentication rate. Extensive experiments carried out on the proprietary ACTIBIO-database verify this potential of activity related authentication within the proposed scheme.

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