Survey on the impact of fingerprint image enhancement

Abstract

The performance of fingerprint comparison algorithms depends on the reliability and accuracy of the features extracted from the fingerprints. The accuracy of the feature extraction algorithms is assumed to depend on the quality of the fingerprint images. Especially, low-quality images can be challenging for feature extraction algorithms. Image enhancement may allow to extract features more accurately. There is a lack of extensive and quantitative evaluation of image enhancement methods. This study investigates the impact of seven typical image enhancement methods on biometric sample quality and on biometric performance. The interrelation of image quality and biometric performance is investigated on 14 datasets. Biometric quality measures are estimated based on image quality metrics NFIQ1 and NFIQ2.0. Biometric performance is tested using MINDTCT and FingerJetFX for feature extraction and BOZORTH3 for biometric comparison. This work shows that the biometric performance can be improved by image enhancement. The significance of improvements depends on both the quality of the datasets and the feature extraction. Thus, there is no single best improvement algorithm. A correlation of changes in scores and image qualities can only be found on the level of entire datasets. No significant correlation can be found for single biometric comparisons.

Deep learning‐based approach to latent overlapped fingerprints mask segmentation

“…The segmentation of single latent fingerprints is a challenging problem. There are several approaches for segmentation and enhancement of single latent fingerprints in the literature [1][2][3][4][5][6][7][8][9][10][23][24][25], some of them dealing with difficult databases, but none of them deals with segmentation of overlapped latent fingerprints, the special case where the overlapping fingerprint area should be distinct from single fingerprint area, in addition to distinguishing between fingerprint area and background.…”

Filters to Dictionary based Enhancement Techniques for Latent Fingerprints Matchingc

“…The experimental results are calculated using Veridicom COMS sensor based fingerprint image dataset. The results in the form of accuracy rate are calculated and are compared with the Fourier filter enhancement algorithm [13]. The accuracy rate of proposed algorithm indicates the improvement in fingerprint image quality and accurate minutia extraction.…”

“…The viewer itself is the judge of the particular method of image enhancement by examining the quality of image and its visual interpretations. However, Wang and Wang [10] evaluated their proposed latent fingerprint enhancement technique by comparing it with Fourier enhancement technique [13] in terms of average accuracy rate. The accuracy rate (AR) was calculated by using the formula presented in equation (iii).…”

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

“…Moreover, IE is useful for different tasks and fields that benefit from pre-processing an image to improve human or machine perception. Such fields are medical imagery, space imagery, bio-metric, photography, and video editing, among others [1][2][3]. Aesthetic improvement via IE is also one of the most popular uses of these techniques.…”