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Proceedings Paper

Human gait recognition by pyramid of HOG feature on silhouette images
Author(s): Guang Yang; Yafeng Yin; Jeanrok Park; Hong Man
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Paper Abstract

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

Paper Details

Date Published: 29 April 2013
PDF: 6 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 87480J (29 April 2013); doi: 10.1117/12.2015981
Show Author Affiliations
Guang Yang, Stevens Institute of Technology (United States)
Yafeng Yin, Stevens Institute of Technology (United States)
Jeanrok Park, Stevens Institute of Technology (United States)
Hong Man, Stevens Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)

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