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Human Gait Recognition Using GEI-Based Local Texture Descriptors

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Advances in Smart Vehicular Technology, Transportation, Communication and Applications (VTCA 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 128))

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Abstract

Human gait is a useful biometric feature for human identification because it can be perceived remotely without physical contact. One critical step for human gait recognition is to accurately extract visual features. In this paper, we apply the center-symmetric local ternary pattern for feature extraction to identify the person from the gait images. The classification is performed by using a support vector machine. Experiments on the CASIA gait database (Dataset B) are given to illustrate the feasibility of the proposed approach.

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References

  1. Cunado, D., Nixon, M.S., Carter, J.N.: Automatic extraction and description of human gait models for recognition purposes. Comput. Vis. Image Underst. 90, 1–41 (2003)

    Article  Google Scholar 

  2. Yam, C., Nixon, M.S., Carter, J.N.: Automated person recognition by walking andrunning via model-based approaches. Pattern Recogn. 37, 1057–1072 (2004)

    Article  Google Scholar 

  3. Huang, X., Boulgouris, N.V.: Model-based human gait recognition using fusion of features. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1469–1472. IEEE Press (2009)

    Google Scholar 

  4. Tafazzoli, F., Safabakhsh, R.: Model-based human gait recognition using leg and arm movements. Eng. Appl. Artif. Intell. 23, 1237–1246 (2010)

    Article  Google Scholar 

  5. Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28, 316–322 (2006)

    Article  Google Scholar 

  6. Bashir, K., Xiang, T., Gong, S.: Gait recognition using gait entropy image. In: 3rd International Conference on Imaging for Crime Detection and Prevention, pp. 1–6. IET Press (2009)

    Google Scholar 

  7. Choudhury, S.D., Tjahjadi, T.: Gait recognition based on shape and motion analysis of silhouette contours. Comput. Vis. Image Underst. 117, 1770–1785 (2013)

    Article  Google Scholar 

  8. Lee, C.P., Tan, A.W.C., Tan, S.C.: Time-sliced averaged motion history image for gait recognition. J. Vis. Commun. Image Represent. 25, 822–826 (2014)

    Article  Google Scholar 

  9. El-Alfy, H., Mitsugami, I., Yagi, Y.: Gait recognition based on normal distance maps. IEEE Trans. Cybern. 48, 1526–1539 (2018)

    Article  Google Scholar 

  10. Gupta, R., Patil, H., Mittal, A.: Robust order-based methods for feature description. In: 23rd IEEE Conference on Computer Vision and Pattern Recognition, pp. 334–341. IEEE Press, San Francisco (2010)

    Google Scholar 

  11. Heikkila, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42, 425–436 (2009)

    Article  Google Scholar 

  12. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19, 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

  13. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Google Scholar 

  14. Chang, C.-C., Lin, C.-J.: LIBSVM: a libary for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm

  15. Zheng, S., Zhang, J., Huang, K., He, R., Tan, T.: Robust view transformation model for gait recognition. In: 2011 18th IEEE International Conference on Image Processing, pp. 2073–2076. IEEE Press, Brussels (2011)

    Google Scholar 

  16. Dupuis, Y., Savatier, X., Vasseur, P.: Feature subset selection applied to model-free gait recognition. Image Vis. Comput. 31, 580–591 (2013)

    Article  Google Scholar 

  17. Mohan Kumar, H.P., Nagendraswamy, H.S.: Symbolic representation and recognition of gait: an approach based on LBP of split gait energy images. Sig. Image Process.: Int. J. 5, 15–28 (2014)

    Google Scholar 

  18. Lishani, A.O., Boubchir, L., Khalifa, E., Bouridane, A.: Human gait recognition using GEI-based local multi-scale feature descriptors. Multimedia Tools Appl. (2018)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by grants MOST 106-2221-E-390-023 and 107-2221-E-390-019-MY2, by the NSYSU-NUK Joint Research Project #NSYSUNUK-107-P006, and by grant B106002, STSP AI Robot Project, MOST, Taiwan.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, 81148, Taiwan

    Chih-Chin Lai & Tsung-Pin Wen

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 81148, Taiwan

    Shing-Tai Pan

  3. Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, 80424, Taiwan

    Shie-Jue Lee

Authors
  1. Chih-Chin Lai
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  2. Shing-Tai Pan
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  3. Tsung-Pin Wen
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  4. Shie-Jue Lee
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Corresponding author

Correspondence to Chih-Chin Lai .

Editor information

Editors and Affiliations

  1. College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian, China

    Yong Zhao

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Tsu-Yang Wu

  3. School of Transportation, Fujian University of Technology, Fuzhou, Fujian, China

    Tang-Hsien Chang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  5. University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Lai, CC., Pan, ST., Wen, TP., Lee, SJ. (2019). Human Gait Recognition Using GEI-Based Local Texture Descriptors. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_35

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