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

A. Alsubari, S. A. Hannan, M. Alzahrani, R. J. Ramteke

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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References


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