Fingerprints Classification through Image Analysis and Machine Learning Method
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 process...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/12/11/241 |
| _version_ | 1828400891513798656 |
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| author | Huong Thu Nguyen Long The Nguyen |
| author_facet | Huong Thu Nguyen Long The Nguyen |
| author_sort | Huong Thu Nguyen |
| collection | DOAJ |
| description | 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%) <inline-formula> <math display="inline"> <semantics> <mrow> <mn>2</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics> </math> </inline-formula> databases. |
| first_indexed | 2024-12-10T09:38:36Z |
| format | Article |
| id | doaj.art-e20a1e0a1be045fc97e52210656e9b3b |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-10T09:38:36Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-e20a1e0a1be045fc97e52210656e9b3b2022-12-22T01:54:06ZengMDPI AGAlgorithms1999-48932019-11-01121124110.3390/a12110241a12110241Fingerprints Classification through Image Analysis and Machine Learning MethodHuong Thu Nguyen0Long The Nguyen1Baikal School of BRICS, Irkutsk National Research Technical University, Irkutsk 664074, RussiaArtificial Intelligence Laboratory, Institute of Information Technology and Data Science, Irkutsk National Research Technical University, 664074 Irkutsk, RussiaThe 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%) <inline-formula> <math display="inline"> <semantics> <mrow> <mn>2</mn> <mo>/</mo> <mn>3</mn> </mrow> </semantics> </math> </inline-formula> databases.https://www.mdpi.com/1999-4893/12/11/241fingerprint classificationsingularity featureimage pre-processingrandom forestsupport vector machinemachine learning |
| spellingShingle | Huong Thu Nguyen Long The Nguyen Fingerprints Classification through Image Analysis and Machine Learning Method Algorithms fingerprint classification singularity feature image pre-processing random forest support vector machine machine learning |
| title | Fingerprints Classification through Image Analysis and Machine Learning Method |
| title_full | Fingerprints Classification through Image Analysis and Machine Learning Method |
| title_fullStr | Fingerprints Classification through Image Analysis and Machine Learning Method |
| title_full_unstemmed | Fingerprints Classification through Image Analysis and Machine Learning Method |
| title_short | Fingerprints Classification through Image Analysis and Machine Learning Method |
| title_sort | fingerprints classification through image analysis and machine learning method |
| topic | fingerprint classification singularity feature image pre-processing random forest support vector machine machine learning |
| url | https://www.mdpi.com/1999-4893/12/11/241 |
| work_keys_str_mv | AT huongthunguyen fingerprintsclassificationthroughimageanalysisandmachinelearningmethod AT longthenguyen fingerprintsclassificationthroughimageanalysisandmachinelearningmethod |