Deep Features for Contactless Fingerprint Presentation Attack Detection:
  Can They Be Generalized?
Hailin Li Raghavendra Ramachandra
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.