Presentation Attack detection using Wavelet Transform and Deep Residual
  Neural Net
Prosenjit Chatterjee Alex Yalchin Joseph Shelton Kaushik Roy Xiaohong Yuan Kossi D. Edoh
Biometric authentication is becoming more prevalent for secured
authentication systems. However, the biometric substances can be deceived by
the imposters in several ways. Among other imposter attacks, print attacks,
mask attacks, and replay attacks fall under the presentation attack category.
The bio-metric images, especially the iris and face, are vulnerable to
different presentation attacks. This research applies deep learning approaches
to mitigate presentation attacks in a biometric access control system. Our
contribution in this paper is two-fold: First, we applied the wavelet transform
to extract the features from the biometric images. Second, we modified the deep
residual neural net and applied it to the spoof datasets in an attempt to
detect the presentation attacks. This research applied the proposed approach to
biometric spoof datasets, namely ATVS, CASIA two class, and CASIA cropped image
sets. The datasets used in this research contain images that are captured in
both a controlled and uncontrolled environment along with different resolutions
and sizes. We obtained the best accuracy of 93% on the ATVS Iris datasets. For
CASIA two class and CASIA cropped datasets, we achieved test accuracies of 91%
and 82%, respectively.