Fingerprint Recognition Using Zernike Moments

In this paper, we present a fingerprint matching approach based on localizing the matching regions in fingerprint images. The determination of the location of such Region Of Interest (ROI) using only the information related to core points based on a novel feature vectors extracted for each fingerprint image by Zernike Moment Invariant (ZMI) as the shape descriptor. The Zernike Moments is selected as feature extractor due to its robustness to image noise, geometrical invariants property and orthogonal property. These features are used to identify corresponding ROI between two fingerprint impressions by computing the Euclidean distance between feature vectors. The fingerprint matching invariance under translations, rotations and scaling using Zemike Moment Invariants and the experimental results obtained from a FVC2002 DB1 database confirm the Zernike moment is able to match the fingerprint images with high accuracy.