Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

Bilal Attallah; Amina Serir; Youssef Chahir; Abdelwahhab Boudjelal

doi:10.1117/1.JEI.26.6.063006

8 November 2017 Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

Bilal Attallah, Amina Serir, Youssef Chahir, Abdelwahhab Boudjelal

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 063006 (November 2017). https://doi.org/10.1117/1.JEI.26.6.063006

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.

Citation Download Citation

Bilal Attallah, Amina Serir, Youssef Chahir, and Abdelwahhab Boudjelal "Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction," Journal of Electronic Imaging 26(6), 063006 (8 November 2017). https://doi.org/10.1117/1.JEI.26.6.063006

Received: 1 August 2017; Accepted: 11 October 2017; Published: 8 November 2017

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