Fingerprint matching from minutiae texture maps -comZhuo Zhang
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This document describes improvements made to the Fingercode fingerprint matching algorithm. The original Fingercode approach extracts a 640-byte feature vector from a fingerprint image using minutiae points, tessellation into sections, and Gabor filtering. The first improvement uses minutiae as reference points and local orientation. The second improvement adds section weighting and allows for orientation and localization variations. Experimental results on two fingerprint databases show the improved approach achieves error rates below 6%, outperforming other top algorithms. The improvements make the approach more robust to noise and errors while increasing computational complexity.