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"C"D for representing a pair of core and delta. A new algorithm based on the distribution of Gaussian-Hermite moments is used to detect S"C"D. With core, delta and S"C"D, 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.

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