Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

Authors

  • A. Alsubari School oF Computer Sciences, KBC North Maharashtra University, India
  • S. A. Hannan Department of Computer Science & I.T., AlBaha University, Saudi Arabia
  • M. Alzahrani Department of Computer Science & I.T., AlBaha University, Saudi Arabia
  • R. J. Ramteke School oF Computer Sciences, KBC North Maharashtra University, India
Volume: 9 | Issue: 1 | Pages: 3807-3813 | February 2019 | https://doi.org/10.48084/etasr.2500

Abstract

Palm-print and iris biometric traits fusion are implemented in this paper. The region of interest (ROI) of a palm is extracted by using the valley detection algorithm and the ROI of an iris is extracted based on the neighbor-pixels value algorithm (NPVA). Statistical local binary pattern (SLBP) is applied to extract the local features of palm and iris. For enhancing the palm features, a combination of histogram of oriented gradient (HOG) and discrete cosine transform (DCT) is applied. Gabor-Zernike moment is used to extract the iris features. This experimentation was carried out in two modes: verification and identification. The Euclidean distance is used in the verification system. In the identification system, the fuzzy-based classifier was proposed along with built-in classification functions in MATLAB. CASIA datasets of palm and iris were used in this research work. The proposed system accuracy was found to be satisfactory.

Keywords:

palm-print, iris, valley detection, SLBP, HOG, DCT, Zernike moment, fuzzy classifier

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How to Cite

[1]
A. Alsubari, S. A. Hannan, M. Alzahrani, and R. J. Ramteke, “Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits”, Eng. Technol. Appl. Sci. Res., vol. 9, no. 1, pp. 3807–3813, Feb. 2019.

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