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View-Invariant Gait Recognition Using a Joint-DLDA Framework

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Trends in Applied Knowledge-Based Systems and Data Science (IEA/AIE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9799))

Abstract

In this paper, we propose a new view-invariant framework for gait analysis. The framework profits from the dimensionality reduction advantages of Direct Linear Discriminant Analysis (DLDA) to build a unique view-invariant model. Among these advantages is the capability to tackle the under-sampling problem (USP), which commonly occurs when the number of dimensions of the feature space is much larger than the number of training samples. Our framework employs Gait Energy Images (GEIs) as features to create a single joint model suitable for classification of various angles with high accuracy. Performance evaluations shows the advantages of our framework, in terms of computational time and recognition accuracy, as compared to state-of-the-art view-invariant methods.

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Notes

  1. 1.

    Our end-to-end implementation is available in: https://yadi.sk/d/MuEE2_tGjJxcq.

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Acknowledgments

This work has been financed by Consejo Nacional de Ciencia y Tecnologia (CONACyT), Mexico and by Secretaria de Educacion Publica, Mexico.

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Authors and Affiliations

  1. Postgraduate and Research Section ESIME Culhuacan, Instituto Politecnico Nacional, Mexico City, Mexico

    Jose Portillo, Gabriel Sanchez, Hector Perez-Meana, Jesus Olivares, Karina Toscano & Mariko Nakano

  2. Department of Computer Science, University of Warwick, Coventry, UK

    Roberto Leyva & Victor Sanchez

Authors
  1. Jose Portillo
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  2. Roberto Leyva
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  3. Victor Sanchez
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  4. Gabriel Sanchez
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  5. Hector Perez-Meana
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  6. Jesus Olivares
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  7. Karina Toscano
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  8. Mariko Nakano
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Corresponding author

Correspondence to Hector Perez-Meana .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University , Iwate, Japan

    Hamido Fujita

  2. Department Computer Science, Texas State University, San Marcos, Texas, USA

    Moonis Ali

  3. Universiti Teknologi Malaysis (UTM), Bahru, Malaysia

    Ali Selamat

  4. Iwate Prefectural University , Iwate, Japan

    Jun Sasaki

  5. Iwate Prefectural University , Iwate, Japan

    Masaki Kurematsu

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Portillo, J. et al. (2016). View-Invariant Gait Recognition Using a Joint-DLDA Framework. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_34

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

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