Fingerprinting classification using fuzzy cerebellar model arithmetic     computer neural networks

Zheng Jason Geng; Weicheng Shen

doi:10.1117/12.269896

1 July 1997 Fingerprinting classification using fuzzy cerebellar model arithmetic computer neural networks

Zheng Jason Geng, Weicheng Shen

Author Affiliations +

Journal of Electronic Imaging, Vol. 6, Issue 3, (July 1997). https://doi.org/10.1117/12.269896

Abstract

We present some preliminary study results of an automated fingerprint pattern classification approach based on a novel neural network structure called the fuzzy cerebellar model arithmetic computer (CMAC) neural network. The fingerprint images are first preprocessed to generate ridge flow, then the Karhunen-Loever (K-L) transform is used to extract the features from the ridge-flow images. The feature vector is then sent to a fuzzy CMAC neural network for classification. Excellent results were obtained through our preliminary experiments on the "two classes" problem.

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Zheng Jason Geng and Weicheng Shen "Fingerprinting classification using fuzzy cerebellar model arithmetic computer neural networks," Journal of Electronic Imaging 6(3), (1 July 1997). https://doi.org/10.1117/12.269896

Published: 1 July 1997

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