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Fingerprint classification with the extreme learning machine algorithm for multilayer perceptron
| dc.contributor.author | Zabala-Blanco, David | |
| dc.contributor.author | Quinteros, Axel | |
| dc.contributor.author | Mora, Marco | |
| dc.contributor.author | Hernández-García, Ruber | |
| dc.contributor.author | Flores-Calero, Marco | |
| dc.date.accessioned | 2023-03-08T13:36:55Z | |
| dc.date.available | 2023-03-08T13:36:55Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/4497 | |
| dc.description.abstract | Fingerprint classification comes to be a relevant guarantee for efficient as well as accurate fingerprint identification, in particular in the case of dealing with one-to-many fingerprint identification. Nevertheless, owing to massive intraclass variability, insignificant inter-class variability, and perturbations, the current fingerprint classification methods still need to enhance the accuracy without increasing the computational cost. In this paper, we introduce a novel method that combines the best extractor of features reported in the literature (Hong08) with multilayer extreme learning machines to maintain the superior classification capability (more than 90%) by simplifying the training time (feasibility for realization in a commercial firmware). | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, 1-6 | es_CL |
| dc.subject | Training | es_CL |
| dc.subject | Extreme learning machines | es_CL |
| dc.subject | Perturbation methods | es_CL |
| dc.subject | Fingerprint recognition | es_CL |
| dc.subject | Multilayer perceptrons | es_CL |
| dc.subject | Feature extraction , | es_CL |
| dc.subject | Nonhomogeneous media | es_CL |
| dc.title | Fingerprint classification with the extreme learning machine algorithm for multilayer perceptron | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | ieeexplore.ieee.org/document/10006187 | es_CL |
| dc.ucm.doi | doi.org/10.1109/ICA-ACCA56767.2022.10006187 | es_CL |
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