A hybrid wavelet-based fingerprint matcher

In this work we present a hybrid fingerprint matcher system based on the multi-resolution analysis of the fingerprint pattern and on minutiae-based registration module. Two fingerprints are first aligned using their minutiae, then the images are divided in sub-windows and each sub-window is decomposed into frequency sub-bands at different decomposition levels using a set of wavelet functions, finally a distinct classifier is trained on each sub-band to distinguish matching pairs of fingerprint from non-matching one (defining a two-class matching problem). The features extracted for the matching are the standard deviation of the image convolved with 16 Gabor filters. The selection among the pool of matchers, is performed by running Sequential Forward Floating Selection. The retained matchers are weighted by a novel localized quality measure and combined by a fusion rule. Extensive experiments conducted over the four FVC2002 fingerprint databases show the effectiveness of the proposed approach.