Palmprint recognition by applying wavelet-based kernel PCA

EKİNCİ M., AYKUT M.

JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, cilt.23, sa.5, ss.851-861, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 5
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1007/s11390-008-9173-4
  • Dergi Adı: JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.851-861
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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.