Minutiae-Based Fingerprint Identification Using Gabor Wavelets and CNN Architecture

GÖRGEL P., Eksi A.

ELECTRICA, cilt.21, sa.3, ss.480-490, 2021 (ESCI, Scopus, TRDizin) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 21 Sayı: 3
  • Basım Tarihi: 2021
  • Doi Numarası: 10.5152/electr.2021.21065
  • Dergi Adı: ELECTRICA
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.480-490
  • Anahtar Kelimeler: Biometric image processing, convolutional neural network (CNN), Gabor wavelet transform, RESOLUTION, POINT
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

Fingerprint identification is still a challenging issue for confident authentication. In this study, we present a methodology that comprises pre-processing, minutiae detection, and Gabor wavelet transform. Both Gabor wavelet and minutiae features, such as ridge bifurcation and ending enhancement, represent the significant information belonging to fingerprint images. Pre-processing algorithm affects minutiae extraction performance. So we use the dilation morphological operation and thinning for the enhancement. Then Gabor wavelet transform is applied to minutiae extracted images to increase the identification performance. The classification problem is solved using a proper convolutional neural network (CNN) with a three layer convolutional model and appropriate filter sizes. Experimental results demonstrate that the classification accuracy is 91.50% and the proposed approach can achieve good results even with poor quality images.