Palmprint identification using feature-level fusion

Adams Kong, Dapeng Zhang, Mohamed Kamel

Research output: Journal article publicationJournal articleAcademic researchpeer-review

314 Citations (Scopus)


In this paper, we propose a feature-level fusion approach for improving the efficiency of palmprint identification. Multiple elliptical Gabor filters with different orientations are employed to extract the phase information on a palmprint image, which is then merged according to a fusion rule to produce a single feature called the Fusion Code. The similarity of two Fusion Codes is measured by their normalized hamming distance. A dynamic threshold is used for the final decisions. A database containing 9599 palmprint images from 488 different palms is used to validate the performance of the proposed method. Comparing our previous non-fusion approach and the proposed method, improvement in verification and identification are ensured.
Original languageEnglish
Pages (from-to)478-487
Number of pages10
JournalPattern Recognition
Issue number3
Publication statusPublished - 1 Mar 2006


  • Biometrics
  • Fusion
  • Palmprint
  • Security
  • Zero-crossing

ASJC Scopus subject areas

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


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