Fingerprint recognition using Gabor Filter

Abstract

Fingerprint recognition is the most popular methods used for identification with higher degree of success. The fingerprint has unique characteristics called minutiae, which are points where a curve track finishes, intersect or branches off. In this work a method for Fingerprint recognition is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters for enhancing the image. The proposed method involves combination of Gabor filter and Frequency domain filtering for enhancing the fingerprint. With eight different orientations of Gabor filter, features of the fingerprint extracting are combined. In Frequency domain filtering, the fingerprint image is subdivided into 32*32 small frames. Features are extracted from these frames in frequency domain. Final enhanced fingerprint is obtained with the results of Gabor filter and frequency domain filtering. Binarization and Thinning follows next where the enhanced fingerprint is converted into binary and the ridges are thinned to one pixel width. This helps in extracting the Minutiae parts (ridge bifurcation and ridge endings). The overall recognition rate for the proposed method is 95% which is much better than histogram method where the recognition rate is 64%. This project is implemented in MATLAB.