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Gait recognition based on Gabor wavelets and (2D)2PCA

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Abstract

Gait recognition is one of the most important techniques in application areas such as video-based surveillance, human tracking and medical systems. In this study, a novel Gabor wavelets based gait recognition algorithm is proposed, which consists of three steps. First, the gait energy image (GEI) is formed by extracting different orientation and scale information from the Gabor wavelet. Secondly, A two-dimensional principal component analysis ((2D)2PCA) method is employed to reduce the feature space dimension. The (2D)2PCA method minimizes the within-class distance and maximizes the between-class distance. Last, the multi-class support vector machine (SVM) is adopted to recognize different gaits. Experimental results performed on CASIA gait database show that the proposed gait recognition algorithm is generally robust, and provides higher recognition accuracy comparing with existing methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (CN) (No. 61303146, 61602431), and is performed under the auspices by the AQSIQ of China (No.2010QK407).

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  1. College of Information Engineering, China Jiliang University, 258 Xueyuan Street, Hangzhou, 310018, China

    Xiuhui Wang, Jun Wang & Ke Yan

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  1. Xiuhui Wang
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  2. Jun Wang
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  3. Ke Yan
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Correspondence to Ke Yan.

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Wang, X., Wang, J. & Yan, K. Gait recognition based on Gabor wavelets and (2D)2PCA. Multimed Tools Appl 77, 12545–12561 (2018). https://doi.org/10.1007/s11042-017-4903-7

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  • DOI: https://doi.org/10.1007/s11042-017-4903-7

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