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Improving Gait Cryptosystem Security Using Gray Code Quantization and Linear Discriminant Analysis

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Information Security (ISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10599))

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Abstract

Gait has been considered as an efficient biometric trait for user authentication. Although there are some studies that address the task of securing gait templates/models in gait-based authentication systems, they do not take into account the low discriminability and high variation of gait data which significantly affects the security and practicality of the proposed systems. In this paper, we focus on addressing the aforementioned deficiencies in inertial-sensor based gait cryptosystem. Specifically, we leverage Linear Discrimination Analysis to enhance the discrimination of gait templates, and Gray code quantization to extract high discriminative and stable binary template. The experimental results on 38 different users showed that our proposed method significantly improve the performance and security of the gait cryptosystem. In particular, we achieved the False Acceptant Rate of \(6\times 10^{-5} \%\) (i.e., 1 fail in 16983 trials) and False Rejection Rate of \(9.2 \%\) with 148-bit security.

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Acknowledgments

This research was supported by Korea NRF-2017R1D1A1B03035343 project.

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Authors and Affiliations

  1. ECE, Chonnam National University, Gwangju, South Korea

    Lam Tran & Deokjai Choi

  2. EECS, Oregon State University, Corvallis, OR, USA

    Thang Hoang

  3. FIT, Ho Chi Minh University Of Science, Ho Chi Minh, Vietnam

    Lam Tran & Thuc Nguyen

Authors
  1. Lam Tran
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  2. Thang Hoang
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  3. Thuc Nguyen
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  4. Deokjai Choi
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Corresponding authors

Correspondence to Lam Tran or Deokjai Choi .

Editor information

Editors and Affiliations

  1. Inria, Tokyo, Japan

    Phong Q. Nguyen

  2. Singapore University of Technology and Design, Singapore, Singapore

    Jianying Zhou

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Tran, L., Hoang, T., Nguyen, T., Choi, D. (2017). Improving Gait Cryptosystem Security Using Gray Code Quantization and Linear Discriminant Analysis. In: Nguyen, P., Zhou, J. (eds) Information Security. ISC 2017. Lecture Notes in Computer Science(), vol 10599. Springer, Cham. https://doi.org/10.1007/978-3-319-69659-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-69659-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69658-4

  • Online ISBN: 978-3-319-69659-1

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