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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/18857

    Title: Gait analysis for human identification through manifold learning and HMM
    Authors: 黃仲陵;Huang, Chung-Lin
    Contributors: 資訊多媒體應用學系
    Date: 2008
    Issue Date: 2012-11-26 15:09:44 (UTC+8)
    Abstract: With the increasing demands of visual surveillance systems, human identification at a distance has gained more attention from the researchers recently. Gait analysis can be used as an unobtrusive biometric measure to identify people at a distance without any attention of the human subjects. We propose a novel effective method for both automatic viewpoint and person identification by using only the silhouette sequence of the gait. The gait silhouettes are nonlinearly transformed into low-dimensional embedding by Gaussian process latent variable model (GP-LVM), and the temporal dynamics of the gait sequences are modeled by hidden Markov models (HMMs). The experimental results show that our method has higher recognition rate than the other methods.
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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