Modified geometry of ring-wedge detector for sampling Fourier transform of fingerprints for classification using neural networks

Dinesh Ganotra; Joby Joseph; Kehar Singh

doi:10.1117/12.525222

19 November 2003 Modified geometry of ring-wedge detector for sampling Fourier transform of fingerprints for classification using neural networks

Dinesh Ganotra, Joby Joseph, Kehar Singh

Author Affiliations +

Proceedings Volume 4829, 19th Congress of the International Commission for Optics: Optics for the Quality of Life; (2003) https://doi.org/10.1117/12.525222
Event: 19th Congress of the International Commission for Optics: Optics for the Quality of Life, 2002, Florence, Italy

Abstract

Sampling of the Fourier transforms (FTs) of fingerprints is studied with neural networks to detect regions useful for their classification. Ring-wedge detector (RWD) is modified and simulated to sample such regions. The output of the detector is propagated through a three-layer backpropagation neural network (BPNN) for checking the classification performance. Modified detector's performance is also compared with that of RWD. It has been found that fingerprints scanned at 500 dpi resolution contain useful information for their classification in a band of width 20 pixels with inner radius approx. 60 pixels.

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Dinesh Ganotra, Joby Joseph, and Kehar Singh "Modified geometry of ring-wedge detector for sampling Fourier transform of fingerprints for classification using neural networks", Proc. SPIE 4829, 19th Congress of the International Commission for Optics: Optics for the Quality of Life, (19 November 2003); https://doi.org/10.1117/12.525222

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