Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?
This addresses security vulnerabilities in biometric systems for smartphone users, but it is incremental as it compares existing methods rather than introducing new ones.
The paper tackled the problem of presentation attack detection in contactless fingerprint verification by evaluating the generalizability of eight deep feature techniques, finding that ResNet50 CNN achieved the best generalization performance on unseen presentation attack instruments.
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.