Original Article

Minutiae-Based Fingerprint Identification Using Gabor Wavelets and CNN Architecture


Department of Computer Engineering, İstanbul University-Cerrahpaşa, Faculty of Engineering, İstanbul, Turkey

ELECTRICA 2021; 21: 480-490
DOI: 10.5152/electr.2021.21065
Read: 572 Downloads: 361 Published: 26 August 2021

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.

Cite this article as: Görgel P, Ekşi A. “Minutiae-based Fingerprint Identification using Gabor Wavelets and CNN Architecture,” Electrica. vol. 21, no. 3, pp. 480-490, Sep. 2021.

EISSN 2619-9831