63.Cnf-75 Paper View Page |
Title |
Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition |
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Authors |
Mr. Andres Rojas, Instituto Nacional de Astrofísica, Puebla, Mexico Prof. Gordana J. Dolecek, Instituto Nacional de Astrofísica, Puebla, Mexico
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Abstract |
This paper presents the application of the Classification Learner MATLAB tool from the Statistics and Machine Learning Toolbox for the classification process in a fingerprint recognition system based on the set B from the public databases FVC2000, FVC2002, and FVC2004. The general results indicate that this system can achieve high accuracy values for several sub-databases using multiple supervised machine learning algorithms including decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, naive Bayes, and ensemble classifiers. |
Track |
MLSP: Machine Learning for Signal Processing |
Conference |
2nd Mosharaka International Conference on Digital Signal Processing (MIC-Signals 2021) |
Congress |
2021 Global Congress on Electrical Engineering (GC-ElecEng 2021), 10-12 December 2021, Valencia, Spain |
Pages |
8-11 |
Topics |
Fingerprint Recognition Supervised Learning |
ISSN |
2227-331X |
DOI |
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BibTeX |
@inproceedings{75ElecEng2021,
title={Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition},
author={Andres Rojas, and Gordana J. Dolecek},
booktitle={2021 Global Congress on Electrical Engineering (GC-ElecEng 2021)},
year={2021},
pages={8-11},
doi={}},
organization={Mosharaka for Research and Studies}
}
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Paper Views |
61 |
Paper Views Rank |
107/524 |
Paper Downloads |
35 |
Paper Downloads Rank |
79/524 |
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