Feature band selection for online multispectral palmprint recognition

Zhenhua Guo, Dapeng Zhang, Lei Zhang, Wenhuang Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

75 Citations (Scopus)


A palmprint is a unique and reliable biometric feature with high usability. In the past decades, many palmprint recognition systems have been successfully developed. However, most of the previous work used the white light as the illumination source, and the recognition accuracy and anti-spoof capability is limited. Recently, multispectral imaging has attracted considerable research attention as it can acquire more discriminative information in a short time. One crucial step in developing online multispectral palmprint systems is how to determine the optimal number of spectral bands and select the most representative bands to build the system. This paper presents a study on feature band selection by analyzing hyperspectral palmprint data (520-1050 nm). Our experimental results showed that three spectral bands could provide most of the discriminate information of a palmprint. This finding could be used as the guidance for designing new online multispectral palmprint systems.
Original languageEnglish
Article number6158597
Pages (from-to)1094-1099
Number of pages6
JournalIEEE Transactions on Information Forensics and Security
Issue number3
Publication statusPublished - 22 May 2012


  • Anti-spoof
  • Biometrics
  • Clustering
  • Multispectral palmprint recognition

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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