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.

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