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.
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Gkalelis, N., Tefas, A., Pitas, I. (2009). Fusion of Movement Specific Human Identification Experts. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds) Biometric ID Management and Multimodal Communication. BioID 2009. Lecture Notes in Computer Science, vol 5707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04391-8_18
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DOI: https://doi.org/10.1007/978-3-642-04391-8_18
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