Reference point detection for improved fingerprint matching

One of the important stages of fingerprint recognition is the registration of the fingerprints with respect to the original template. This is not a straightforward task as fingerprint images may have been subject to rotations and translations. Popular techniques for fingerprint registration use a reference point to achieve alignment. The drawback of existing methods of core/reference point detection is their poor performance on rotated images. In this paper, we propose a new approach for rotation invariant and reliable reference point detection applicable to fingerprints of different quality and types. Our approach is based on the integration of a directional vector field (representing the doubled ridge orientations in fingerprints) over a closed contour. We define the reference point as the point of the highest curvature. Areas of high curvature in the fingerprint are characterized by large differences in the orientations and correspond to high curvatures in the directional vector fields. Closed contour integrals of orientation vector field, defined as above, over a circle centered around the reference point corresponds to maximal closed curve integrals, and the values associated with such integrals are rotation invariant. Experimental results prove that with the proposed approach we can locate the reference point with high accuracy. Comparison with existing methods is provided.