Fingerprint presentation attack detection using deep transfer learning and densenet201 network / Divine S. Ametefe, Suzi S. Seroja, and Darmawaty M. Ali

S. Ametefe, Divine and S. Seroja, Suzi and M. Ali, Darmawaty (2021) Fingerprint presentation attack detection using deep transfer learning and densenet201 network / Divine S. Ametefe, Suzi S. Seroja, and Darmawaty M. Ali. Journal of Electrical and Electronic Systems Research (JEESR), 19: 11. pp. 95-105. ISSN 1985-5389

Abstract

Fingerprint presentation attack, which involves presenting spoof fingerprints to fingerprint bio metric sensors to gain illicit access, is a significant challenge faced by Automatic Fingerprint Identification Systems (AFIS). As a result, various hardware-based and software-based approaches have been posited to help remedy this concern. However, the software-based methods have seen enormous utilization relative to the hardware-based techniques due to their robust cognitive feature extraction for spoof detection. Nonetheless, most software-based techniques that utilize handcrafted features proffer shallow features for discriminating against spoofs due to their manual feature extraction process, which, as a result, affects the model's robustness. Motivated by this concern, we propose a deep transfer learning approach to automatically learn deep hierarchical semantic fingerprint features as a means of discriminating against spoofs. Experiments were conducted on the LivDet competition standard database, encompassing datasets from LivDet-2009, 2011, 2013, and 2015, resulting in the acquisition of real fingerprints and fake fingerprints fabricated from twelve (12) different spoofing materials. The developed model recorded an average classification accuracy of 99.8%, a sensitivity of 99.73% and a specificity of 99.77%, showcasing a state-of-the-art performance.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
S. Ametefe, Divine
UNSPECIFIED
S. Seroja, Suzi
UNSPECIFIED
M. Ali, Darmawaty
UNSPECIFIED
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electric apparatus and materials. Electric circuits. Electric networks
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Radio frequency identification systems
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Scanning systems
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Journal or Publication Title: Journal of Electrical and Electronic Systems Research (JEESR)
UiTM Journal Collections: UiTM Journal > Journal of Electrical and Electronic Systems Research (JEESR)
ISSN: 1985-5389
Volume: 19
Page Range: pp. 95-105
Keywords: Terms—Presentation Attack, Spoof Detection, Deep Transfer Learning
Date: October 2021
URI: https://ir.uitm.edu.my/id/eprint/52075
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