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A Novel Gait Recognition System Based on Hidden Markov Models

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Advances in Visual Computing (ISVC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7432))

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Abstract

The advances in computing power, availability of large- capacity storage devices and research in computer vision have contributed to recent developments in gait recognition. The ease of acquiring human videos by low cost equipments makes gait recognition much easier and less intrusive than other biometric systems. In this paper, a gait recognition system using a space-model-based approach is proposed. The proposed mechanism is able to detect moving object of interest, while track them and analyzing their gait for recognition. The system captures videos of subjects in front a stationary camera. The identification module makes use of the shape and dynamics of the system using HMM. Then it models the gait properties by accepting the feature vectors as input and model the dynamics through state transitions and observation probabilities. The experimental results show that the proposed gait recognition system successfully recognizes humans using their gait.

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Author information

Authors and Affiliations

  1. University of Houston, Victoria, USA

    Akintola Kolawole & Alireza Tavakkoli

Authors
  1. Akintola Kolawole
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  2. Alireza Tavakkoli
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science, University of California at Irvine, 92697-3435, Irvine, CA, USA

    Charless Fowlkes

  6. Eastman Kodak Company, 14650-2102, Rochester, NY, USA

    Sen Wang

  7. Department of Computer Science and Engineering, University of Colorado at Denver, 80217, Denver, CO, USA

    Min-Hyung Choi

  8. VRVis Zentrum für Virtual Reality and Visualisierung, 1220, Vienna, Austria

    Stephan Mantler

  9. California Institute for Telecommunications and Information Technology, University of California,,, San Diego, 92093, La Jolla, CA, USA

    Jürgen Schulze

  10. KAUST Visualizatioin Core Lab., 23955-6900, Thurwal, Saudi Arabia

    Daniel Acevedo

  11. Stony Brook University, 11794-4400, NY, USA

    Klaus Mueller

  12. Argonne National Laboratory, 60439, IL, USA

    Michael Papka

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© 2012 Springer-Verlag Berlin Heidelberg

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Kolawole, A., Tavakkoli, A. (2012). A Novel Gait Recognition System Based on Hidden Markov Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_13

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  • DOI: https://doi.org/10.1007/978-3-642-33191-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33190-9

  • Online ISBN: 978-3-642-33191-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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