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Proceedings Paper

Optical fingerprint identification using cellular neural network and joint transform correlation
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Paper Abstract

An important step in the fingerprint identification system is the extraction of relevant details against distributed complex features. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial intelligence based feature extraction techniques are attractive due to their adaptive learning properties. In this paper, we propose a cellular neural network (CNN) based filtering technique due to its ability of parallel processing and generating learnable filtering features. CNN offers high efficient feature extraction and enhancement possibility for fingerprint images. The enhanced fingerprint images are then introduced to joint transform correlator (JTC) architecture to identify unknown fingerprint from the database. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.

Paper Details

Date Published: 22 October 2004
PDF: 7 pages
Proc. SPIE 5557, Optical Information Systems II, (22 October 2004); doi: 10.1117/12.559760
Show Author Affiliations
Abdullah Bal, Univ. of South Alabama (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)
Aed El-Saba, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 5557:
Optical Information Systems II
Bahram Javidi; Demetri Psaltis, Editor(s)

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