Re-evaluation of Face Anti-spoofing Algorithm in Post COVID-19 Era Using Mask Based Occlusion Attack

TL;DR

This study evaluates how face anti-spoofing algorithms perform under mask and glasses occlusions common in the post-COVID-19 era, revealing significant vulnerabilities and the need for more robust detection methods.

Contribution

The paper systematically tests existing PAD algorithms with synthetic occlusions and introduces a hybrid CNN-LBP model to address occlusion challenges.

Findings

Occlusions significantly degrade PAD performance

All tested algorithms are vulnerable to mask and glasses occlusions

The hybrid CNN-LBP model shows improved robustness

Abstract

Face anti-spoofing algorithms play a pivotal role in the robust deployment of face recognition systems against presentation attacks. Conventionally, full facial images are required by such systems to correctly authenticate individuals, but the widespread requirement of masks due to the current COVID-19 pandemic has introduced new challenges for these biometric authentication systems. Hence, in this work, we investigate the performance of presentation attack detection (PAD) algorithms under synthetic facial occlusions using masks and glasses. We have used five variants of masks to cover the lower part of the face with varying coverage areas (low-coverage, medium-coverage, high-coverage, round coverage), and 3D cues. We have also used different variants of glasses that cover the upper part of the face. We systematically tested the performance of four PAD algorithms under these occlusion attacks using a benchmark dataset. We have specifically looked at four different baseline PAD algorithms that focus on, texture, image quality, frame difference/motion, and abstract features through a convolutional neural network (CNN). Additionally we have introduced a new hybrid model that uses CNN and local binary pattern textures. Our experiment shows that adding the occlusions significantly degrades the performance of all of the PAD algorithms. Our results show the vulnerability of face anti-spoofing algorithms with occlusions, which could be in the usage of such algorithms in the post-pandemic era.