Presentation Attack detection using Wavelet Transform and Deep Residual Neural Net

TL;DR

This paper presents a novel biometric presentation attack detection method combining wavelet transform feature extraction with a deep residual neural network, achieving high accuracy across multiple datasets.

Contribution

The study introduces a combined wavelet transform and deep residual neural network approach for detecting presentation attacks in biometric systems, applied to iris and face datasets.

Findings

Achieved 93% accuracy on ATVS iris dataset.

Achieved 91% accuracy on CASIA two class dataset.

Achieved 82% accuracy on CASIA cropped dataset.

Abstract

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.