Palmprint verification for images captured in peg-less scenarios

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationInnovation for Applied Science and Technology
Pages3178-3183
Number of pages6
DOIs
StatePublished - 2013
Event2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012 - Kaohsiung, Taiwan
Duration: 2 Nov 20126 Nov 2012

Publication series

NameApplied Mechanics and Materials
Volume284-287
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012
Country/TerritoryTaiwan
CityKaohsiung
Period2/11/126/11/12

Keywords

  • Multiresolution representation
  • Palmprint verification
  • Support vector machine

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