Fingerprint Classification Based on Multilayer Extreme Learning Machines
Fingerprint recognition is one of the most effective and widely adopted methods for person identification. However, the computational time required for the querying of large databases is excessive. To address this, preprocessing steps such as classification are necessary to speed up the response tim...
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MDPI AG
2025-03-01
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| author | Axel Quinteros David Zabala-Blanco |
| author_facet | Axel Quinteros David Zabala-Blanco |
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| collection | DOAJ |
| description | Fingerprint recognition is one of the most effective and widely adopted methods for person identification. However, the computational time required for the querying of large databases is excessive. To address this, preprocessing steps such as classification are necessary to speed up the response time to a query. Fingerprints are typically categorized into five classes, though this classification is unbalanced. While advanced classification algorithms, including support vector machines (SVMs), multilayer perceptrons (MLPs), and convolutional neural networks (CNNs), have demonstrated near-perfect accuracy (approaching 100%), their high training times limit their widespread applicability across institutions. In this study, we introduce, for the first time, the use of a multilayer extreme learning machine (M-ELM) for fingerprint classification, aiming to improve training efficiency. A comparative analysis is conducted with CNNs and unbalanced extreme learning machines (W-ELMs), as these represent the most influential methodologies in the literature. The tests utilize a database generated by SFINGE software, which simulates realistic fingerprint distributions, with datasets comprising hundreds of thousands of samples. To optimize and simplify the M-ELM, widely recognized descriptors in the field—Capelli02, Liu10, and Hong08—are used as input features. This effectively reduces dimensionality while preserving the representativeness of the fingerprint information. A brute-force heuristic optimization approach is applied to determine the hyperparameters that maximize classification accuracy across different M-ELM configurations while avoiding excessive training times. A comparison is made with the aforementioned approaches in terms of accuracy, penetration rate, and computational cost. The results demonstrate that a two-layer hidden ELM achieves superior classification of both majority and minority fingerprint classes with remarkable computational efficiency. |
| format | Article |
| id | doaj-art-38fca653a57442548afedb285928e6f4 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-38fca653a57442548afedb285928e6f42025-08-20T02:59:07ZengMDPI AGApplied Sciences2076-34172025-03-01155279310.3390/app15052793Fingerprint Classification Based on Multilayer Extreme Learning MachinesAxel Quinteros0David Zabala-Blanco1Department of Computer Science and Industry, Faculty of Engineering Science, Universidad Católica del Maule, Talca 3480112, ChileDepartment of Computer Science and Industry, Faculty of Engineering Science, Universidad Católica del Maule, Talca 3480112, ChileFingerprint recognition is one of the most effective and widely adopted methods for person identification. However, the computational time required for the querying of large databases is excessive. To address this, preprocessing steps such as classification are necessary to speed up the response time to a query. Fingerprints are typically categorized into five classes, though this classification is unbalanced. While advanced classification algorithms, including support vector machines (SVMs), multilayer perceptrons (MLPs), and convolutional neural networks (CNNs), have demonstrated near-perfect accuracy (approaching 100%), their high training times limit their widespread applicability across institutions. In this study, we introduce, for the first time, the use of a multilayer extreme learning machine (M-ELM) for fingerprint classification, aiming to improve training efficiency. A comparative analysis is conducted with CNNs and unbalanced extreme learning machines (W-ELMs), as these represent the most influential methodologies in the literature. The tests utilize a database generated by SFINGE software, which simulates realistic fingerprint distributions, with datasets comprising hundreds of thousands of samples. To optimize and simplify the M-ELM, widely recognized descriptors in the field—Capelli02, Liu10, and Hong08—are used as input features. This effectively reduces dimensionality while preserving the representativeness of the fingerprint information. A brute-force heuristic optimization approach is applied to determine the hyperparameters that maximize classification accuracy across different M-ELM configurations while avoiding excessive training times. A comparison is made with the aforementioned approaches in terms of accuracy, penetration rate, and computational cost. The results demonstrate that a two-layer hidden ELM achieves superior classification of both majority and minority fingerprint classes with remarkable computational efficiency.https://www.mdpi.com/2076-3417/15/5/2793feature descriptorsfingerprint classificationidentification systemsbiometrymultilayer extreme learning machines |
| spellingShingle | Axel Quinteros David Zabala-Blanco Fingerprint Classification Based on Multilayer Extreme Learning Machines Applied Sciences feature descriptors fingerprint classification identification systems biometry multilayer extreme learning machines |
| title | Fingerprint Classification Based on Multilayer Extreme Learning Machines |
| title_full | Fingerprint Classification Based on Multilayer Extreme Learning Machines |
| title_fullStr | Fingerprint Classification Based on Multilayer Extreme Learning Machines |
| title_full_unstemmed | Fingerprint Classification Based on Multilayer Extreme Learning Machines |
| title_short | Fingerprint Classification Based on Multilayer Extreme Learning Machines |
| title_sort | fingerprint classification based on multilayer extreme learning machines |
| topic | feature descriptors fingerprint classification identification systems biometry multilayer extreme learning machines |
| url | https://www.mdpi.com/2076-3417/15/5/2793 |
| work_keys_str_mv | AT axelquinteros fingerprintclassificationbasedonmultilayerextremelearningmachines AT davidzabalablanco fingerprintclassificationbasedonmultilayerextremelearningmachines |