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Human gait recognition based on histogram of oriented gradients and Haralick texture descriptor

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Abstract

Gait recognition is an evolving technology in the biometric domain; it aims to recognize people through an analysis of their walking pattern. One of the significant challenges of the appearance-based gait recognition system is to augment its performance by using a distinctive low-dimensional feature vector. Therefore, this study proposes the low-dimensional features that are capable of effectively capturing the spatial, gradient, and texture information in this context. These features are obtained by the computation of histogram of oriented gradients, followed by sum variance Haralick texture descriptor from nine cells of gait gradient magnitude image. Further, the performance of the proposed method is validated on five widely used gait databases. They include CASIA A gait database, CASIA B gait database, OU-ISIR D gait database, CMU MoBo database, and KTH video database. The experimental results demonstrated that the proposed approach could choose significant discriminatory features for individual identification and consequently, outperform certain state-of-the-art methods in terms of recognition performance.

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Acknowledgements

We thank to (1) The team behind Institute of Automation, Chinese Academy of Sciences (CASIA) for sharing the CASIA gait database [43], (2) Prof Yasushi Yagi, Osaka University Japan and his whole research group for providing, OU-ISIR D gait database [26], (3) Christian Schuldt and his team for sharing KTH video database [32], and (4) Ralph Gross and his team, Carnegie Mellon University for sharing CMU MoBo motion database [8]. This work is supported by Visvesvaraya Ph.D. Scheme, MeitY, Government of India.

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  1. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, 575 025, Karnataka, India

    R. Anusha & C. D. Jaidhar

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  1. R. Anusha
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  2. C. D. Jaidhar
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Correspondence to R. Anusha.

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Anusha, R., Jaidhar, C.D. Human gait recognition based on histogram of oriented gradients and Haralick texture descriptor. Multimed Tools Appl 79, 8213–8234 (2020). https://doi.org/10.1007/s11042-019-08469-1

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