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- W2034383054 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." @default.
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- W2034383054 date "2008-08-01" @default.
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- W2034383054 title "Gait analysis for human identification through manifold learning and HMM" @default.
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- W2034383054 doi "https://doi.org/10.1016/j.patcog.2007.11.021" @default.
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