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Abstract
It is important to estimate the ridge distance accurately, an intrinsic texture property of a fingerprint image. Up to now, only several articles have touched directly upon ridge distance estimation. Little has been published providing detailed evaluation of methods for ridge distance estimation, in particular, the traditional spectral analysis method applied in the frequency field. In this paper, a novel method on nonoverlap blocks, called the statistical method, is presented to estimate the ridge distance. Direct estimation ratio (DER) and estimation accuracy (EA) are defined and used as parameters along with time consumption (TC) to evaluate performance of these two methods for ridge distance estimation. Based on comparison of performances of these two methods, a third hybrid method is developed to combine the merits of both methods. Experimental results indicate that DER is 44.7%, 63.8%, and 80.6%; EA is 84%, 93%, and 91%; and TC is 0.42, 0.31, and 0.34 seconds, with the spectral analysis method, statistical method, and hybrid method respectively.
Highlights
Fingerprint identification is the most popular biometric technology and has drawn a substantial attention recently [1]
In order to ensure a robust performance of an automated fingerprint identification system (AFIS) with respect to quality of fingerprint images, it is essential to incorporate an enhancement algorithm in the minutiae extraction module
Ridge distance is an intrinsic property of fingerprint images and it is used as a basic parameter in fingerprint enhancement in some enhancement methods, during which ridge distance is used to determine the period of enhancement mask
Summary
Fingerprint identification is the most popular biometric technology and has drawn a substantial attention recently [1]. In order to ensure a robust performance of an AFIS with respect to quality of fingerprint images, it is essential to incorporate an enhancement algorithm in the minutiae extraction module. It is important to be able to estimate ridge distance in fingerprint images reliably in an AFIS. Fingerprint ridge distance is very important in AFIS, it is difficult to estimate due to the following factors:. Hong et al [6] presented an oriented window method for estimating fingerprint ridge frequency This method performs well when ridges in the oriented window have distinct contrast and consistent ridge directions. Kovacs-Vajna et al [7] brought out geometric and spectral methods to estimate fingerprint ridge distance.
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