Fusion of movement specific human identification experts

Authors:
Nikolaos Gkalelis;Anastasios Tefas;Ioannis Pitas
Affiliations:
Informatics and Telematics Institute, CERTH, Greece and Department of Informatics, Aristotle University of Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Greece;Informatics and Telematics Institute, CERTH, Greece and Department of Informatics, Aristotle University of Thessaloniki, Greece
Venue:
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Year:
2009

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Abstract

In this paper a multi-modal method for human identification that exploits the discriminant features derived from several movement types performed from the same human is proposed. Utilizing a fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA) based algorithm, an unknown movement is first classified, and, then, the person performing the movement is recognized from a movement specific person recognition expert. In case that the unknown person performs more than one movements, a multi-modal algorithm combines the scores of the individual experts to yield the final decision for the identity of the unknown human. Using a publicly available database, we provide promising results regarding the human identification strength of movement specific experts, as well as we indicate that the combination of the outputs of the experts increases the robustness of the human recognition algorithm.