Title: Fingerprint multiple-class classifier: performance evaluation on known and unknown fingerprint spoofing materials

Authors: Divine Senanu Ametefe; Suzi Seroja Sarnin; Darmawaty Mohd Ali; Dah B. John; Abdulmalik Adozuka Aliu

Addresses: Wireless Communication Technology Group (WiCOT), College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia ' Wireless Communication Technology Group (WiCOT), College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia ' Wireless Communication Technology Group (WiCOT), College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia ' School of Information Science, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), 40150 Puncak Perdana, Selangor, Malaysia ' College of Built Environment, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia

Abstract: Fingerprint recognition is a popular and reliable biometric technology used in many security-sensitive applications. However, the use of fake fingerprints made from ubiquitous spoofing materials poses a significant threat to security systems. While several studies have proposed binary classifiers to detect fingerprint presentation attacks, relatively few have explored the effectiveness of multiple-class classifiers in detecting known and unknown spoofs. In this study, we evaluated the efficacy of multiple-class classifiers using deep transfer learning to detect presentation attacks made with different spoofing materials. Our experiments on the LivDet 2009-2015 datasets showed that while a classifier model developed without data augmentation performed better on known spoofs, it showed poor performance on cross-material detection of all seven fingerprint spoofing materials. These results suggest that modelling a multiple-class classifier is not an efficient approach for detecting cross-material presentation attacks in fingerprint recognition systems.

Keywords: fingerprint spoofing; multiple-class classifier; known spoofing materials; unknown spoofing materials; deep transfer learning.

DOI: 10.1504/IJBM.2024.137088

International Journal of Biometrics, 2024 Vol.16 No.2, pp.113 - 132

Received: 17 Feb 2023
Accepted: 28 May 2023
Published online: 01 Mar 2024 *

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