Application of a new type of singular points in fingerprint classification

The singular points, core and delta, are widely used in fingerprint classification. However a true pair of core and delta that are close to one another is often ignored. In this paper, we define a new type of singular point denoted by S-CD for representing a pair of core and delta. A new algorithm based on the distribution of Gaussian-Hermite moments is used to detect S-CD. With core, delta and S-CD, the accuracy of fingerprint classification is improved, especially for tented arches. The proposed method has been tested on the NIST-4. We can improve the accuracy of algorithm (Zhang and Yan, 2004) [Zhang, Q., Yan, H., 2004. Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recognit. 37, 2233-2243] by 26.7% for identifying tented arch, and the classification accuracy can be improved by 4.3% for five-class problem. (c) 2007 Elsevier B.V. All rights reserved.