Wavelet based palmprint recognition

Xiang Qian Wu, Kuan Quan Wang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

53 Citations (Scopus)

Abstract

Palmprint is a new biometric method to recognize a person. The features in a palmprint include principal lines, wrinkles and ridges, etc. Line structure feature, which includes principal lines and wrinkles, is one of the most popular methods in palmprint recognition. However, the line structure feature does not contain the thickness and width information of principal lines and wrinkles, which are very important to discriminate palmprints. Ridges are not included in line structure feature either. So these methods cannot distinguish different palmprints with similar line structure. Furthermore, the line extraction is a difficult task. The fact that principal lines, wrinkles and ridges have different resolutions motivates us to analyze the palmprint using multi-resolution analysis method. A novel palmprint feature, named wavelet energy features, is defined employing wavelet, which is a powerful tool of multi-resolution analysis, in this paper. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in several directions at different wavelet decomposition level (scale), so its ability to discriminate palms is very strong. Easiness to compute is another virtue of WEF. The very high recognition rates obtained in experiments shows the effect of the proposed method.
Original languageEnglish
Title of host publicationProceedings of 2002 International Conference on Machine Learning and Cybernetics
Pages1253-1257
Number of pages5
Volume3
Publication statusPublished - 1 Dec 2002
EventProceedings of 2002 International Conference on Machine Learning and Cybernetics - Beijing, China
Duration: 4 Nov 20025 Nov 2002

Conference

ConferenceProceedings of 2002 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityBeijing
Period4/11/025/11/02

Keywords

  • Biometrics
  • Feature extraction
  • Palmprint recognition
  • Wavelet energy feature

ASJC Scopus subject areas

  • Engineering(all)

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