Citation

Lee, Kim Wang and Lim, Jit Yan and Lim, Kian Ming and Lee, Chin Poo (2023) Face Presentation Attack Detection via Ensemble Learning Algorithm. In: 2023 IEEE 11th Conference on Systems, Process & Control (ICSPC), 16-16 December 2023, Malacca, Malaysia.

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Abstract

Face recognition systems are vulnerable to a variety of presentation assaults, including print, mask, and replay attacks. To successfully address the issues faced by these assaults, we offer a deep learning-based technique based on the VGG19, ResNet152, and DenseNet161 models in this study. We also investigate the ensemble learning bagging strategy to improve classification reliability further. The experimental findings show that our proposed strategy is successful at recognising and categorising presentation assaults. The ensemble learning approach significantly increases overall accuracy when compared with training each model independently, producing groundbreaking outcomes on the investigated datasets. Based on the results, we were able to propose bagging technique, which performed quite well in Replay-Attack and OULU-NPU with 1.22% and 4.86%, respectively.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep Learning, Ensemble learning, Bagging approach, Face anti-spoofing, face recognition
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	27 Mar 2024 00:47
Last Modified:	27 Mar 2024 00:47
URII:	http://shdl.mmu.edu.my/id/eprint/12195

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