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Human Gait Recognition With Matrix Representation

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6 Author(s)
Dong Xu ; Dept. of Electr. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China ; Shuicheng Yan ; Dacheng Tao ; Lei Zhang
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Human gait is an important biometric feature. It can be perceived from a great distance and has recently attracted greater attention in video-surveillance-related applications, such as closed-circuit television. We explore gait recognition based on a matrix representation in this paper. First, binary silhouettes over one gait cycle are averaged. As a result, each gait video sequence, containing a number of gait cycles, is represented by a series of gray-level averaged images. Then, a matrix-based unsupervised algorithm, namely coupled subspace analysis (CSA), is employed as a preprocessing step to remove noise and retain the most representative information. Finally, a supervised algorithm, namely discriminant analysis with tensor representation, is applied to further improve classification ability. This matrix-based scheme demonstrates a much better gait recognition performance than state-of-the-art algorithms on the standard USF HumanID Gait database

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Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:16 ,  Issue: 7 )