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A Robust Human Gait Recognition Approach Using Multi-interval Features

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Data Analytics and Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 43))

Abstract

This paper has demonstrated a simple and robust interval type features based gait recognition approach. For each an individual, first gait energy image (GEI) is generated. Next, gradient matrices are obtained based on the neighborhood gradient computational procedures (i.e., horizontal/vertical, opposite, and diagonal pixels). Then, multi-interval features are extracted from these matrices in order to achieve the gait covariate conditions. For the gallery and probe interval matching, a new dissimilarity measure is proposed in this work by counting the minimum number of arithmetic operations required to transform one interval into the other. Lastly, a simple KNN classifier is incorporated in the classification procedure. Three standard datasets (Chinese Academy of sciences B and C) are used for the experimental procedures and satisfactory results are obtained. The effective comparative analysis with the current state-of-the-art algorithms has shown that the proposed approach is robust to changes in appearance and different walking speed conditions.

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Acknowledgements

The authors would like to thank the creators of CASIA A, B and C datasets for providing the publicly available gait datasets.

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Authors and Affiliations

  1. GFGC, Honnāli, India

    V. G. Manjunatha Guru

  2. VTU RRC, Belagavi, Karnataka, India

    V. G. Manjunatha Guru & V. N. Kamalesh

  3. TJIT, Bengaluru, India

    V. N. Kamalesh

Authors
  1. V. G. Manjunatha Guru
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  2. V. N. Kamalesh
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Corresponding author

Correspondence to V. G. Manjunatha Guru .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India

    P. Nagabhushan

  2. Department of Computer Science and Engineering, CBCS Education, University of Mysore, Mysuru, Karnataka, India

    D. S. Guru

  3. Department of Studies in Computer Science, Mangalore University, Mangalore, Karnataka, India

    B. H. Shekar

  4. Department of Information Science and Engineering, Maharaja Institute of Technology, Belawadi, Karnataka, India

    Y. H. Sharath Kumar

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Manjunatha Guru, V.G., Kamalesh, V.N. (2019). A Robust Human Gait Recognition Approach Using Multi-interval Features. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_5

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