Leonid Stoimenov, Assistant Professor, Research interests: GIS, Artificial intelligence and databases; ontologies, mediation, and KB systems. Teaching: Artificial intelligence, Database, Data structures

Management and Information Systems Research Group Web Page at the University of York

A comprehensive set of notes for students doing Computer Science or Computing at A-Level (Published by Simon Chester from original notes compiled by Jonathan Greenald).

A critical step in automatic fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint images. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images. In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

A. James Clark School of Engineering at the University of Maryland: degree programs, research, faculty, students, alumni, and industry.