Hand-geometry recognition using entropy-based discretization

Ajay Kumar Pathak, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

56 Citations (Scopus)


The hand-geometry-based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature-level representation. We investigate the possibilities to improve the performance of the existing hand-geometry systems using the discretization of extracted features. This paper proposes employing discretization of hand-geometry features, using entropy-based heuristics, to achieve the performance improvement. The performance improvement due to the unsupervised and supervised discretization schemes is compared on a variety of classifiers: k-NN, naïve Bayes, SVM, and FFN. Our experimental results on the database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in hand-geometry-based systems.
Original languageEnglish
Pages (from-to)181-187
Number of pages7
JournalIEEE Transactions on Information Forensics and Security
Issue number2
Publication statusPublished - 1 Jun 2007


  • Biometrics
  • Feature discretization
  • Feature representation
  • Hand geometry
  • Personal recognition

ASJC Scopus subject areas

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


Dive into the research topics of 'Hand-geometry recognition using entropy-based discretization'. Together they form a unique fingerprint.

Cite this