Type-Independent Pixel-Level Alignment Point Detection for Fingerprints

Abstract

Robust alignment point detection is still a challenging problem in fingerprint recognition, especially for arch type fingerprints. Proposed in this paper is a method of detecting a pixel-level alignment point from mated fingerprints regardless of the type based on pixel-level orientation field. Given a fingerprint, firstly, pixel-level orientation field is computed using multi-scale Gaussian filtering. Secondly, a vertical symmetry line is extracted from the orientation field, based on which the fingerprint type is classified, either arch or non-arch type. For non-arch mated pairs, the pixel-level singular points (core or delta) are adopted as candidate alignment points and be verified by point-pattern matching and the average orientation difference between the orientation fields. And, for arch mated pairs, the alignment points are detected at the maximum in the angular difference and the orientation certainty level over the symmetry lines. The proposed method is tested over the FVC 2000 DB2a, and 95.93% mated fingerprint pairs are aligned within one ridge-width displacement.