Towards Robust Combination of Neural Networks for Fingerprint Presentation Attack Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, S.H. | - |
dc.contributor.author | Lim, M.Y. | - |
dc.contributor.author | Kang, D. | - |
dc.contributor.author | Lee, Y.K. | - |
dc.date.accessioned | 2023-12-11T07:07:17Z | - |
dc.date.available | 2023-12-11T07:07:17Z | - |
dc.date.issued | 2022-01-01 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32071 | - |
dc.description.abstract | To resolve security threats on fingerprint authentication systems, a number of fingerprint Presentation Attack Detection (fingerprint PAD) methods have been proposed. However, existing methods still provide limited performance in terms of detection accuracy and generalization performance. In this paper, we propose a new fingerprint PAD method that ensembles Feature-based Neural Network (FNN) and Convolutional Neural Network (CNN) models based on a weighted voting mechanism. We also designed a new FNN architecture and a new CNN architecture, respectively, which provide improved performance for PAD. To verify the effectiveness of our method, we performed experimental evaluations using real-world datasets, LivDet 2015. The results showed that our method provided improved finger-print PAD accuracy compared to existing methods. © 2022 IEEE. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Towards Robust Combination of Neural Networks for Fingerprint Presentation Attack Detection | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ICTC55196.2022.9952921 | - |
dc.identifier.scopusid | 2-s2.0-85143253075 | - |
dc.identifier.bibliographicCitation | International Conference on ICT Convergence, v.2022-October, pp 1829 - 1834 | - |
dc.citation.title | International Conference on ICT Convergence | - |
dc.citation.volume | 2022-October | - |
dc.citation.startPage | 1829 | - |
dc.citation.endPage | 1834 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | ensemble learning | - |
dc.subject.keywordAuthor | finger-print anti-spoofing | - |
dc.subject.keywordAuthor | fingerprint presentation attack detection | - |
dc.subject.keywordAuthor | neural network | - |
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