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Fractional Order Derivative Mechanism to Extract Biometric Features

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Theory and Engineering of Dependable Computer Systems and Networks (DepCoS-RELCOMEX 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1389))

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Abstract

Currently growing market of mobile devices demands algorithms for biometric identification, which require low computational complexity and reliability. The paper presents the use of a fractional derivative to construct a feature vector. Using the IriShield scanner, a database of iris photos obtained in the near infrared was generated. The iris area was segmented and normalized using the Daugman algorithm. Iris feature vectors were obtained by weaving a standardized iris image with a kernel of a gradient edge detector using derivatives of fractional orders according to the Riemann-Liouville definition. For the efficiency assessment of the proposed approach, the comparison of results obtained with a Log-Gabor filter, which is a classic method of quadrant coding in the Daugman algorithm were performed. A statistical analysis of the effectiveness of the proposed method was carried out using the Hamming distance measurement to compare the generated iris codes. The conducted experiments allowed to determine the optimal order of derivative and size of the fractional derivative kernel in order to increase the discrepancy of feature vectors data. The use of fractional derivative convolution mechanism show the great potential for distinguishing iris. Proposed approach might be applied in programming tools for iris recognition.

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Authors and Affiliations

  1. College of Natural Sciences, University of Rzeszow, Pigonia Street 1, 35-959, Rzeszow, Poland

    Zbigniew Gomolka & Boguslaw Twarog

  2. Department of Applied Information, University of Information Technology and Management in Rzeszow, Sucharskiego Street 2, 35-225, Rzeszow, Poland

    Ewa Zeslawska

Authors
  1. Zbigniew Gomolka
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  2. Boguslaw Twarog
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  3. Ewa Zeslawska
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Corresponding author

Correspondence to Zbigniew Gomolka .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Wojciech Zamojski

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Jacek Mazurkiewicz

  3. Wrocław University of Science and Technology, Wrocław, Poland

    Jarosław Sugier

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Tomasz Walkowiak

  5. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Gomolka, Z., Twarog, B., Zeslawska, E. (2021). Fractional Order Derivative Mechanism to Extract Biometric Features. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Theory and Engineering of Dependable Computer Systems and Networks. DepCoS-RELCOMEX 2021. Advances in Intelligent Systems and Computing, vol 1389. Springer, Cham. https://doi.org/10.1007/978-3-030-76773-0_13

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