Abstract
Fingerprint is a type of biometrics that is used for human recognition because it is rich in details called minutiae. The focus of this paper is to develop an algorithm for fingerprint recognition through extracting and matching of fingerprint image minutiae. In order to achieve accurate results, the fingerprint is first pre-processed to enhance its quality. An alignment-based matching algorithm was developed for minutia matching. This algorithm is capable of finding the correspondences between the input minutia pattern and the stored template minutia pattern without resorting to exhaustive search. The performance of the developed system was evaluated with matrix laboratory (MATLAB) software along with fingerprint from the standard FVC2000 fingerprint database and fingerprints from different people at university of Uyo. From the curves of false match rate (FMR) and false non match rate (FNMR) against similarity score, a threshold value of 0.4 was obtained as guided by the equal error rate (EER) value. For verification and identification testing, the developed system was able to accurately differentiate between genuine users and imposter irrespective of the orientation and noise level of the fingerprints.
Highlights
In order to improve on the quality, an enhancement algorithm based on the combination of Gabor filter [9, 10, 11, 12] and Fast Fourier transform (FFT) [13, 14, 15, 16] is developed
The proposed fingerprint system is simulated with matrix laboratory (MATLAB) software and the performance analysis of the system is conducted using FVC2002 fingerprint database
One major problem of the existing fingerprint image recognition system is the poor quality of the image acquired from the capturing device and this has plagued a lot of fingerprint recognition systems [1, 2, 3, 4]
Summary
In order to improve on the quality, an enhancement algorithm based on the combination of Gabor filter [9, 10, 11, 12] and Fast Fourier transform (FFT) [13, 14, 15, 16] is developed. The results of the two enhancement processes (Gabor filtering and FFT) are combined to get the final image before the minutiae are extracted. With the two reference points, the two images are rotated to the same direction (orientation) and the images are superimposed (matched) before computing the similarity score. The proposed fingerprint system is simulated with MATLAB software and the performance analysis of the system is conducted using FVC2002 fingerprint database
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