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Fingerprint is one of the most widely used biometric in law enforcement. However, low-quality fingerprint images can drastically degrade the performance of automated fingerprint identification systems (AFIS). AFIS can be substantially advanced by: 1) establishing a metric to evaluate the image quality accurately and 2) utilizing this metric to enable an automated enhancement process. This paper offers a novel localized quality measure (LQM) to evaluate the quality of fingerprint images, and a genetic localized quality measure enhancement (LQME) algorithm, which is tailored to iteratively enhance poor-quality fingerprint images. In addition, a method is introduced to automatically choose the enhancement algorithm's parameters based on the proposed measure such that it yields the best enhancement result. The presented LQM measure uses fingerprint image characteristics, which include sharpness, contrast, orientation certainty level, symmetry features, and imprints of friction ridge structure (minutiae) information. The FVC2004 Set B database containing fingerprint images from four different sensors and a total of 240 images (80 from each sensor) is used to evaluate the performance of the presented algorithms and methods. The computer simulations demonstrate that the LQM measure is useful in predicting the quality of the fingerprint images captured from various devices. Furthermore, the experiments show that LQME can recover retrievable-corrupt fingerprint regions.
Panetta et al. (Tue,) studied this question.