HMMs based palmprint identification

Xiangqian Wu, Kuanquan Wang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)

Abstract

This paper presents a novel approach of palmprint identification with Hidden Markov Models (HMMs). Palmprint is first aligned and normalized by using the boundary of the fingers. Then the continuous HMMs are used to identify palmprints. The palmprint features are extracted by using Sobel operators and projecting technique. It shows that HMMs with six states and two Gaussian mixtures can obtain the highest identification rate, 97.80%, in one-to-320 matching test. Experimental results demonstrate the feasibility of HMMs on the palmprint identification task.
Original languageEnglish
Pages (from-to)775-781
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3072
Publication statusPublished - 1 Dec 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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