Fingerprint CLassification by Ridge DIstribution Sequences and Ridge Distribution Model
| Autor: | Jeng-Horng Chang, 張正弘 |
|---|---|
| Rok vydání: | 2001 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 89 Ridges and ravines are the main components constituting a fingerprint. Traditional Automatic Fingerprint Identification Systems (AFIS) are mainly based on minutiae matching techniques. The minutiae for fingerprint identification are defined by ridge terminations and ridge bifurcations. Most AFIS perform ridge line following process to automatically detect minutiae based on binary or skeleton fingerprint images. For low-quality fingerprint images, the preprocessing stage of an AFIS produces redundant minutiae or even destroys real minutiae. The minutiae detection algorithms in direct gray-scale domain have been developed to overcome these problems. The first step of gray-scale minutiae detection algorithm is to determine ridge locations and then perform gray-scale ridge line following algorithm to extract minutiae. However, the existing gray-scale minutiae detection techniques can only work on partial fingerprint images due to the ignorance of image background. Moreover, the gray value variation inside a ridge also generates redundant ridge points. In this dissertation, we propose a novel method, based on gray-level histogram decomposition, to locate the ridge points in complete fingerprint images. By decomposing the gray-level histogram, redundant ridge points can be eliminated according to some statistical parameters. Experimental results demonstrate that the correct rate can be over 96% even applied to poor-quality fingerprint images. For automatic fingerprint classification problem, a novel method is introduced which is a combination of structural and syntactic approaches. The goal of the proposed Ridge Distribution (R-D) Model is to present the idea of the possibility for classifying a fingerprint into the complete seven classes in the Henry''s classification. From our observation, there exist only ten basic ridge patterns which construct fingerprints. Fingerprint classes can be interpreted as a combination of these ten ridge patterns with different ridge distribution sequences. In this thesis, the classification task is performed depending on the global distribution of the ten basic ridge patterns by analyzing the ridge shapes and the sequence of ridges distribution. The regular expression for each class is formulated and a NFA model is constructed accordingly. An explicit rejection criterion is also defined in this thesis. For the seven-class fingerprint classification problem, our method can achieve the classification accuracy of 93.4% with 5.1% rejection rate. For the five-class problem, the accuracy rate of 94.8% is achieved. Experimental results reveal the feasibility and validity of the proposed approach in fingerprint classification. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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