Singular points detection based on multi-resolution in fingerprint images

  • Authors:

  • Dawei Weng;Yilong Yin;Dong Yang

  • Affiliations:

  • School of Computer Science and Technology, Shandong University, Jinan 250101, PR China;School of Computer Science and Technology, Shandong University, Jinan 250101, PR China;School of Computer Science and Technology, Shandong University, Jinan 250101, PR China

  • Venue:
  • Neurocomputing
  • Year:
  • 2011

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Abstract

The singular points of fingerprints, namely core and delta, play an important role in fingerprint recognition and classification systems. Several traditional methods have been proposed; however, these methods cannot achieve the reliable and accurate detection of poor-quality fingerprints. In this paper, an algorithm is proposed which combines improved Poincare index and multi-resolution analysis to detect singular points. Conventional Poincare index method is improved on the basis of the Zero-pole Model analysis to detect singular points with different resolutions. A model is presented to extract the multi-resolution information of the fingerprint pattern; this model divides fingerprint image into nonoverlapping blocks corresponding to different block sizes on the basis of wavelet functions to compute multiple resolution directional fields, and block position shifting is performed on these resolution levels to capture the features of the ridge direction patterns, where the corresponding shifting intervals are based on Sampling theorem. The relationship of singularities detected by improved Poincare index in different resolution directional fields is used to confirm singular points accurately and reliably. The combination of local and global information makes our algorithm more robust to noise than methods that use local information only, and the existence of this algorithm increases the insight into the nature of singular points extraction. The accuracy and reliability of the method are demonstrated by experiment on database NIST-4, public fingerprint databases FVC02 DB1 and DB2.