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Open AccessFeature PaperArticle

Fingerprints Classification through Image Analysis and Machine Learning Method

1
Baikal School of BRICS, Irkutsk National Research Technical University, Irkutsk 664074, Russia
2
University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen 24000, Viet Nam
3
Artificial Intelligence Laboratory, Institute of Information Technology and Data Science, Irkutsk National Research Technical University, 664074 Irkutsk, Russia
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(11), 241; https://doi.org/10.3390/a12110241
Received: 3 October 2019 / Revised: 7 November 2019 / Accepted: 8 November 2019 / Published: 11 November 2019
(This article belongs to the Special Issue Algorithms for Content Based Image Retrieval)
The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%) 2 / 3 databases. View Full-Text
Keywords: fingerprint classification; singularity feature; image pre-processing; random forest; support vector Machine; machine learning fingerprint classification; singularity feature; image pre-processing; random forest; support vector Machine; machine learning
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Nguyen, H.T.; Nguyen, L.T. Fingerprints Classification through Image Analysis and Machine Learning Method. Algorithms 2019, 12, 241.

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