Fast robust fingerprint feature extraction and classification

NYONGESA, H. O., AL-KHAYATT, S., MOHAMED, S. M. and MAHMOUD, M. (2004). Fast robust fingerprint feature extraction and classification. Journal of intelligent and robotic systems, 40 (1), 103-112.

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Abstract

Automatic identification of humans based on their fingers is still one of the most reliable identification methods in criminal and forensic applications. Identification by fingerprint involves two processes: fingerprint feature extraction and feature classification. The basic idea of fingerprint feature extraction algorithms proposed is to locate the coarse features of fingerprints called singular-points using directional fields of the fingerprint image. The features are then classified by different types of neural networks. The "five-class" classification problem is addressed on the NIST-4 database of fingerprints. A maximum classification accuracy of 93.75% was achieved and the result shows a performance comparable to previous studies using either coarse features or the finer features called minutiae.

Item Type:	Article
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Identification Number:	https://doi.org/10.1023/B:JINT.0000034344.58449.fd
Page Range:	103-112
Depositing User:	Ann Betterton
Date Deposited:	17 Nov 2010 14:55
Last Modified:	18 Mar 2021 09:45
URI:	https://shura.shu.ac.uk/id/eprint/2657

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