Gait analysis for human identification through manifold learning and HMM

  • Authors:

  • Ming-Hsu Cheng;Meng-Fen Ho;Chung-Lin Huang

  • Affiliations:

  • Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan;Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan and Department of Electronic Engineering, Hsiuping Institute of Technology, Taichung, Taiwan;Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan and Department of Informatics, Fo-Guang University, I-Lan, Taiwan

  • Venue:
  • Pattern Recognition
  • Year:
  • 2008

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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.