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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 284-287Palmprint Verification for Images Captured in...

Palmprint Verification for Images Captured in Peg-Less Scenarios

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Abstract:

This paper presents a reliable and robust palmprint verification approach using palmprint feature point number (FPN). The feature verified by support vector machine (SVM). It has the advantages of capturing palm images in peg-less scenarios and by a low cost and low-resolution (100dpi) digital scanner. The low-resolution images lead a less database size. There are 4800 palmprint images were collected from 160 persons to verify the validity of the proposed approach and the results are satisfactory with 98.30% classification correct rate (CCR). Experimental results demonstrate that the proposed approach is feasible and effective in palmprint verification. Our findings will help to extend palmprint verification technologies to security access control systems.

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Periodical:

Applied Mechanics and Materials (Volumes 284-287)

Pages:

3178-3183

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.284-287.3178

Citation:

Cite this paper

Online since:

January 2013

Authors:

Chun Wei Lu, Chih Lung Lin, Kuo Chin Fan, Hsu Yung Cheng, Chang Jung Juan

Keywords:

Multiresolution Representation, Palmprint Verification, Support Vector Machine (SVM)

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[18] C. C. Chang and C. J. Lin, LIBSVM: A Library for Support Vector Machines 2001, Information on http://www.csie.ntu.edu.tw/verb~cjlin/libsvmVerification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output Verification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output Verification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output Verification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output Verification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output Verification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output Verification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output Verification Palm image The second and fourth finger-webs location Palmprint ROI location and alignment Palmprint bi-feature extraction Palmprint feature point number Histogram of oriented gradient 16×16 blocks 8×8 blocks Support vector machine Template library 16×16 blocks 8×8 blocks Training Output