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

被引:9
作者
Attallah, Bilal [1 ,2 ]
Serir, Amina [1 ]
Chahir, Youssef [2 ]
Boudjelal, Abdelwahhab [2 ]
机构
[1] USTHB, LTIR, Elect Dept, Bab Ezzouar, Algeria
[2] Normandy Univ, UNICAEN, ENSICAEN, CNRS,GREYC, Caen, France
关键词
palmprint recognition; histogram of gradient; binarized statistical image features; feature extraction; principal component analysis; extreme learning machine;
D O I
10.1117/1.JEI.26.6.063006
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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. (C) 2017 SPIE and IS&T
引用
收藏
页数:9
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