Learning fingerprint minutiae location and type
For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and classification/matching is conventionally adopted, where each stage transforms its input relatively independently. In practice, the interaction between these modules is limited. Some of the errors in this end-to-end sequential processing can be eliminated, especially for the feature extraction stage, by revisiting the input pattern. We propose a feedforward of the original grayscale image data to a feature (minutiae) verification stage in the context of a minutiae-based fingerprint verification system. This minutiae verification stage is based on reexamining the grayscale profile in a detected minutia's spatial neighborhood in the sensed image. We also show that a feature refinement (minutiae classification) stage that assigns one of two class labels to each detected minutia (ridge ending and ridge bifurcation) can improve the matching accuracy by similar to1% and when combined with the proposed minutiae verification stage, the matching accuracy can be improved by similar to3.2% on our fingerprint database. (C) 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
feature extraction feedforward minutia verification minutia classification fingerprint matching gabor filters learning vector quantization