Towards Robust Combination of Neural Networks for Fingerprint Presentation Attack Detection
- Authors
- Park, S.H.; Lim, M.Y.; Kang, D.; Lee, Y.K.
- Issue Date
- 1-Jan-2022
- Publisher
- IEEE Computer Society
- Keywords
- ensemble learning; finger-print anti-spoofing; fingerprint presentation attack detection; neural network
- Citation
- International Conference on ICT Convergence, v.2022-October, pp 1829 - 1834
- Pages
- 6
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2022-October
- Start Page
- 1829
- End Page
- 1834
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32071
- DOI
- 10.1109/ICTC55196.2022.9952921
- ISSN
- 2162-1233
- 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.
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- Appears in
Collections - College of Engineering > School of Electronic & Electrical Engineering > 1. Journal Articles
- College of Engineering > Computer Engineering > Journal Articles
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