Volume 4, Issue 1, March 2019, Page: 7-12
Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping
Hemad Heidari Jobaneh, Department of Electrical Engineering, Azad University, South Tehran Branch, Tehran, Iran
Received: Apr. 4, 2019;       Accepted: May 16, 2019;       Published: Jun. 4, 2019
DOI: 10.11648/j.mlr.20190401.12      View  19      Downloads  4
The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have been performed according to beneficial points called core point, singularities, or minutiae. However, points always are sensitive to noise and distortion, thus inaccurate results. Hence, instead of extracting a point, two lines are defined to bring down the risk of finding a point. Plus, two approaches are proposed with the intention of extracting statistical features predicated upon Kernel and Markov chain. In fact, two sets of features are extracted from both horizontal and vertical Markov chain, derived from the ridges angle around the aforementioned lines. In addition, all features are trained and tested via two divergent neural networks, consisting Generalized Regression Neural Network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP). Fingerprint verification competition (FVC) database is used to analyze the system. The performances of networks with different sets of features are simulated and compared with MATLAB. The results coming from simulation are compared and 93.5% and 83.5% accuracy is achieved for GRNN and ARTMAP respectively. Furthermore, the system is tested by both networks with features coming from just vertical and horizontal features.
ARTMAP, Fingerprint Recognition, GRNN, Kernel, Markov Chain, Neural Network
To cite this article
Hemad Heidari Jobaneh, Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping, Machine Learning Research. Vol. 4, No. 1, 2019, pp. 7-12. doi: 10.11648/j.mlr.20190401.12
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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