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.
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
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}
}
