Human gait recognition by pyramid of HOG feature on silhouette images
2013
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.
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