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


  • 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 |


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.


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


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CASIA Iris Image Database and CASIA Palm Image Database,


How to Cite

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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