Palmprint classification using principal lines

Xiangqian Wu, Dapeng Zhang, Kuanquan Wang, Bo Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

168 Citations (Scopus)


This paper proposes a novel algorithm for the automatic classification of low-resolution palmprints. First the principal lines of the palm are defined using their position and thickness. Then a set of directional line detectors is devised. After that we use these directional line detectors to extract the principal lines in terms of their characteristics and their definitions in two steps: the potential beginnings ("line initials") of the principal lines are extracted and then, based on these line initials, a recursive process is applied to extract the principal lines in their entirety. Finally palmprints are classified into six categories according to the number of the principal lines and the number of their intersections. The proportions of these six categories (1-6) in our database containing 13,800 samples are 0.36%, 1.23%, 2.83%, 11.81%, 78.12% and 5.65%, respectively. The proposed algorithm has been shown to classify palmprints with an accuracy of 96.03%.
Original languageEnglish
Pages (from-to)1987-1998
Number of pages12
JournalPattern Recognition
Issue number10
Publication statusPublished - 1 Oct 2004


  • Biometrics
  • Head line
  • Heart line
  • Life line
  • Palmprint classification
  • Principal lines

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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