Human gait recognition is the process of identifying individuals by their walking manners. The gait as one of biometrics has recently drawn attention to the computer vision researchers. The stance estimation of one’s each gait sequence is based on the dynamic programming-based Viterbi algorithm, which returns a sample stance sequence. The corresponding stances of sample stance sequences are averaged to get his feature stance sequence. Principal component analysis and linear discriminant analysis are used for feature transformation of feature stances in the feature stance sequence. The recognition is achieved by nearest neighbor algorithm. The experiments made on CASIA gait databases obtain comparatively high correction identification ratio and comparatively strong robustness for variety of bodily form.

ImprovedGaitRecognitionApproach.pdf

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