Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation
- PDF / 1,426,867 Bytes
- 8 Pages / 600 x 792 pts Page_size
- 64 Downloads / 188 Views
Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation Yilong Yin College of Computer Science & Technology, Shandong University, Shanda South Road 27, Jinan 250100, China Email: [email protected]
Jie Tian Intelligent Bioinformatics Systems Division, Institute of Automation, The Chinese Academy of Sciences, Beijing 100080, China Email: [email protected]
Xiukun Yang Identix Inc, One Exchange Place Suite 800, Jersey City, NJ 07302, USA Email: [email protected] Received 17 October 2002; Revised 27 September 2003 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. Keywords and phrases: fingerprint, ridge distance, spectral analysis, statistical window, hybrid method.
1.
INTRODUCTION
Fingerprint identification is the most popular biometric technology and has drawn a substantial attention recently [1]. An automated fingerprint identification system (AFIS) includes fingerprint acquisition, feature extraction, fingerprint matching, and/or fingerprint classification. Most AFISs are based on comparison of minutiae, the most prominent being ridge endings and ridge bifurcations [2]. A critical step in automatic fingerprint matching is to extract minutiae automatically and reliably from fingerprint images. Performance of a minutiae extraction algorithm relies heavily, however, on the quality of fingerprint images. 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. 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 enhance-
ment mask. It is thus important to be able to estimate ridge distance in fingerprint images reliably in an AFIS. Fingerprint ridge distance is defined as the distance from a given ridge to adjacent ridges. It can be measured as the distance from the center of one ridge to the ce
Data Loading...
