A comparative study of palmprint recognition algorithms

Dapeng Zhang, Wangmeng Zuo, Feng Yue

Research output: Journal article publicationJournal articleAcademic researchpeer-review

199 Citations (Scopus)


Palmprint images contain rich unique features for reliable human identification, which makes it a very competitive topic in biometric research. A great many different low resolution palmprint recognition algorithms have been developed, which can be roughly grouped into three categories: holistic-based, feature-based, and hybrid methods. The purpose of this article is to provide an updated survey of palmprint recognition methods, and present a comparative study to evaluate the performance of the state-of-the-art palmprint recognition methods. Using the Hong Kong Polytechnic University (HKPU) palmprint database (version 2), we compare the recognition performance of a number of holistic-based (Fisherpalms and DCT+LDA) and local feature-based (competitive code, ordinal code, robust line orientation code, derivative of Gaussian code, and wide line detector) methods, and then investigate the error correlation and score-level fusion performance of different algorithms. After discussing the achievements and limitations of current palmprint recognition algorithms, we conclude with providing several potential research directions for the future.
Original languageEnglish
Article number2
JournalACM Computing Surveys
Issue number1
Publication statusPublished - 1 Jan 2012


  • Biometrics
  • Feature extraction
  • Palmprint recognition
  • Performance evaluation
  • Person identification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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