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Journal of Electronic Imaging

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction
Author(s): Bilal Attallah; Amina Serir; Youssef Chahir; Abdelwahhab Boudjelal
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Paper Abstract

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Paper Details

Date Published: 8 November 2017
PDF: 9 pages
J. Electron. Imag. 26(6) 063006 doi: 10.1117/1.JEI.26.6.063006
Published in: Journal of Electronic Imaging Volume 26, Issue 6
Show Author Affiliations
Bilal Attallah, Univ. of Science and Technology Houari Boumediene (USTHB) (Algeria)
Univ. de Caen Basse-Normandie (France)
Amina Serir, Univ. des Sciences et de la Technologie Houari Boumediene (Algeria)
Youssef Chahir, Univ. de Caen Basse-Normandie (France)
Abdelwahhab Boudjelal, Univ. de Caen Basse-Normandie (France)


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