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

• Main Authors: S. Ametefe, Divine (Author), S. Seroja, Suzi (Author), M. Ali, Darmawaty (Author)
• Format: Book
• Published: Universiti Teknologi MARA, 2021-10.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata

Summary

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.

Item Description

https://ir.uitm.edu.my/id/eprint/52075/1/52075.pdf