Abstract
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.
Highlights
Biometric identifiers can be broadly classified into anatomical and behavioral characteristics
localized quality measure (LQM) was able to differentiate the quality of fingerprint images satisfactorily and perform much better when compared to the existing error measurement techniques
Fingerprint quality measurement and enhancement is essential to improve the performance of automated fingerprint identification systems (AFIS), especially when the quality of the original fingerprint is low
Summary
Biometric identifiers can be broadly classified into anatomical and behavioral characteristics. Hanoon [33] uses CLAHE and Wiener filter to enhance the fingerprint image to eliminate artificially induced boundaries This approach increases the ability of a fingerprint system to extract minutiae, it provides a noise gain as well. The enhancement technique proposed by Hong et al [34] decomposes the image into several filtered images and calculates the orientation of the resulting fingerprints to extract the fingerprint ridges. This is because the algorithm cannot differentiate between smudged valleys and ridges. The rest of this paper is organized as follows: the proposed methods localized quality measure (LQM) estimation and fingerprint image enhancement (LQME) are described, FIGURE 4.
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