Face Presentation Attack Detection via Ensemble Learning Algorithm

Kim Wang Lee; Jit Yan Lim; Kian Ming Lim; Chin Poo Lee

doi:10.1109/ICSPC59664.2023.10420311

Face Presentation Attack Detection via Ensemble Learning Algorithm

Kim Wang Lee, Jit Yan Lim, Kian Ming Lim, Chin Poo Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

2 Citations (Scopus)

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.

Original language	English
Title of host publication	2023 IEEE 11th Conference on Systems, Process & Control (ICSPC) - Conference Proceedings
Editors	Ramli Adnan
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	101-106
Number of pages	6
ISBN (Electronic)	9798350340860, 9798350340853
ISBN (Print)	9798350340877
DOIs	https://doi.org/10.1109/ICSPC59664.2023.10420311
Publication status	Published - 2023
Externally published	Yes
Event	IEEE Conference on Systems, Process and Control (ICSPC) 2023 - Hatten Hotel, Malacca, Malaysia Duration: 16 Dec 2023 → 16 Dec 2023 Conference number: 11th https://ieeexplore.ieee.org/xpl/conhome/10419865/proceeding (Published proceedings) https://sites.google.com/view/icspc/home (Website)

Conference

Conference	IEEE Conference on Systems, Process and Control (ICSPC) 2023
Abbreviated title	ICSPC 2023
Country/Territory	Malaysia
City	Malacca
Period	16/12/23 → 16/12/23
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10419865/proceeding (Published proceedings) https://sites.google.com/view/icspc/home (Website)

Keywords

Bagging approach
Deep Learning
Ensemble learning
Face anti-spoofing

Access to Document

10.1109/ICSPC59664.2023.10420311

Cite this

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title = "Face Presentation Attack Detection via Ensemble Learning Algorithm",

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.",

keywords = "Bagging approach, Deep Learning, Ensemble learning, Face anti-spoofing",

author = "Lee, {Kim Wang} and Lim, {Jit Yan} and Lim, {Kian Ming} and Lee, {Chin Poo}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; IEEE Conference on Systems, Process and Control (ICSPC) 2023, ICSPC 2023 ; Conference date: 16-12-2023 Through 16-12-2023",

year = "2023",

doi = "10.1109/ICSPC59664.2023.10420311",

language = "English",

isbn = "9798350340877",

pages = "101--106",

editor = "Ramli Adnan",

booktitle = "2023 IEEE 11th Conference on Systems, Process & Control (ICSPC) - Conference Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/10419865/proceeding, https://sites.google.com/view/icspc/home",

}

Lee, KW, Lim, JY, Lim, KM & Lee, CP 2023, Face Presentation Attack Detection via Ensemble Learning Algorithm. in R Adnan (ed.), 2023 IEEE 11th Conference on Systems, Process & Control (ICSPC) - Conference Proceedings. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 101-106, IEEE Conference on Systems, Process and Control (ICSPC) 2023, Malacca, Malaysia, 16/12/23. https://doi.org/10.1109/ICSPC59664.2023.10420311

Face Presentation Attack Detection via Ensemble Learning Algorithm. / Lee, Kim Wang; Lim, Jit Yan; Lim, Kian Ming et al.
2023 IEEE 11th Conference on Systems, Process & Control (ICSPC) - Conference Proceedings. ed. / Ramli Adnan. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2023. p. 101-106.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Face Presentation Attack Detection via Ensemble Learning Algorithm

AU - Lee, Kim Wang

AU - Lim, Jit Yan

AU - Lim, Kian Ming

AU - Lee, Chin Poo

N1 - Conference code: 11th

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Bagging approach

KW - Deep Learning

KW - Ensemble learning

KW - Face anti-spoofing

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U2 - 10.1109/ICSPC59664.2023.10420311

DO - 10.1109/ICSPC59664.2023.10420311

M3 - Conference Paper

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SN - 9798350340877

SP - 101

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BT - 2023 IEEE 11th Conference on Systems, Process & Control (ICSPC) - Conference Proceedings

A2 - Adnan, Ramli

PB - IEEE, Institute of Electrical and Electronics Engineers

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T2 - IEEE Conference on Systems, Process and Control (ICSPC) 2023

Y2 - 16 December 2023 through 16 December 2023

ER -

Face Presentation Attack Detection via Ensemble Learning Algorithm

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Keywords

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