Palmprint authentication using multiple classifiers

Ajay Kumar Pathak, Dapeng Zhang

Research output: Journal article publicationConference articleAcademic researchpeer-review

16 Citations (Scopus)


This paper investigates the performance improvement for palmprint authentication using multiple classifiers. The proposed methods on personal authentication using palmprints can be divided into three categories; appearance-, line -, and texture-based. A combination of these approaches can be used to achieve higher performance. We propose to simultaneously extract palmprint features from PCA, Line detectors and Gabor-filters and combine their corresponding matching scores. This paper also investigates the comparative performance of simple combination rules and the hybrid fusion strategy to achieve performance improvement. Our experimental results on the database of 100 users demonstrate the usefulness of such approach over those based on individual classifiers.
Original languageEnglish
Pages (from-to)20-29
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 1 Dec 2004
EventBiometric Technology for Human Identification - Orlando, FL, United States
Duration: 12 Apr 200413 Apr 2004


  • Biometrics
  • Combination Rules
  • Fusion
  • Multiple Classifiers
  • Palmprint Authentication

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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