1 Ocak 2008 Dergi makalesi Açık Erişim

Ekinci, Murat; Aykut, Murat

DataCite XML

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/37257</identifier>
  <creators>
    <creator>
      <creatorName>Ekinci, Murat</creatorName>
      <givenName>Murat</givenName>
      <familyName>Ekinci</familyName>
      <affiliation>Karadeniz Tech Univ, Comp Vis Lab, Dept Comp Engn, TR-61080 Trabzon, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Aykut, Murat</creatorName>
      <givenName>Murat</givenName>
      <familyName>Aykut</familyName>
      <affiliation>Karadeniz Tech Univ, Comp Vis Lab, Dept Comp Engn, TR-61080 Trabzon, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Palmprint Recognition By Applying Wavelet-Based Kernel Pca</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2008</publicationYear>
  <dates>
    <date dateType="Issued">2008-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/37257</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.37256</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.37257</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.</description>
  </descriptions>
</resource>