In addition, a method is introduced that automatically chooses the enhancement algorithm's parameters based on the proposed measure, such that it yields the best enhancement result. 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 establishing a metric to evaluate the image quality accurately and then using this metric to enable an automated enhancement process. The LQM measure presented in the current project uses fingerprint image characteristics that 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. (publisher abstract modified)
LQM: Localized Quality Measure for Fingerprint Image Enhancement
NCJ Number
253983
Journal
Ieee Access Volume: 7 Dated: 2019 Pages: 104567-104576
Date Published
2019
Length
10 pages
Annotation
This article reports on the development of a novel localized quality measure (LQM) to evaluate the quality of fingerprint images, as well as a genetic localized quality measure enhancement (LQME) algorithm, which is tailored to iteratively enhance poor-quality fingerprint images.
Abstract
Date Published: January 1, 2019