Enhanced fingerprint identification using dynamic neural network and fringe-adjusted joint transform correlation

Abdullah Bal; Mohammad S. Alam; Aed M. El-Saba

doi:10.1117/12.603657

28 March 2005 Enhanced fingerprint identification using dynamic neural network and fringe-adjusted joint transform correlation

Abdullah Bal, Mohammad S. Alam, Aed M. El-Saba

Author Affiliations +

Proceedings Volume 5816, Optical Pattern Recognition XVI; (2005) https://doi.org/10.1117/12.603657
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The parallel processing capability and adaptive filtering features of dynamic neural networks offer highly efficient feature extraction and enhancement capability for fingerprint images. The most important aspect of the fingerprint enhancement is the extraction of relevant details with respect to distributed complex features. For this purpose, an efficient dynamic neural filtering technique has been proposed in this paper. After the enhancement process, fingerprint identification is/has been achieved using joint transform correlation (JTC) algorithm. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it in this study. The identification test results are presented to verify the effectiveness of the proposed enhancement and identification algorithms.

Citation Download Citation

Abdullah Bal, Mohammad S. Alam, and Aed M. El-Saba "Enhanced fingerprint identification using dynamic neural network and fringe-adjusted joint transform correlation", Proc. SPIE 5816, Optical Pattern Recognition XVI, (28 March 2005); https://doi.org/10.1117/12.603657

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