22 October 2004 Optical fingerprint identification using cellular neural network and joint transform correlation
Author Affiliations +
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.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abdullah Bal, Abdullah Bal, Mohammad S. Alam, Mohammad S. Alam, Aed El-Saba, Aed El-Saba, } "Optical fingerprint identification using cellular neural network and joint transform correlation", Proc. SPIE 5557, Optical Information Systems II, (22 October 2004); doi: 10.1117/12.559760; https://doi.org/10.1117/12.559760

Back to Top