Bi-feature verification for palmprint images captured in pegless scenarios

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems.

Original languageEnglish
Article number1356007
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number5
StatePublished - Aug 2013


  • bifeature
  • histogram of oriented gradient
  • multiresolution representation
  • Palmprint verification
  • support vector machine


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