Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?

• URL: http://arxiv.org/abs/2307.01845v1
• Date: Tue, 4 Jul 2023 17:46:20 GMT
• Title: Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?
• Authors: Hailin Li and Raghavendra Ramachandra
• Abstract summary: contactless fingerprint-verification systems are vulnerable to presentation attacks. We present a comparative study on the generalizability of seven different pre-trained Convolutional Neural Networks (CNN) and a Vision Transformer (ViT) to reliably detect presentation attacks.
• Score: 6.668147787950981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid evolution of high-end smartphones with advanced high-resolution cameras has resulted in contactless capture of fingerprint biometrics that are more reliable and suitable for verification. Similar to other biometric systems, contactless fingerprint-verification systems are vulnerable to presentation attacks. In this paper, we present a comparative study on the generalizability of seven different pre-trained Convolutional Neural Networks (CNN) and a Vision Transformer (ViT) to reliably detect presentation attacks. Extensive experiments were carried out on publicly available smartphone-based presentation attack datasets using four different Presentation Attack Instruments (PAI). The detection performance of the eighth deep feature technique was evaluated using the leave-one-out protocol to benchmark the generalization performance for unseen PAI. The obtained results indicated the best generalization performance with the ResNet50 CNN.

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