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Improving Monocular Facial Presentation-Attack-Detection Robustness with Synthetic Noise Augmentations.

Ali Hassani¹, Jon Diedrich², Hafiz Malik²

¹Information Systems, Security and Forensics Lab, University of Michigan-Dearborn, Dearborn, MI 48128, USA.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

Synthetic noise augmentations enhance face presentation-attack-detection (PAD) systems against real-world noise. This approach improves accuracy and simplifies data collection for more robust security.

Keywords:

PAD augmentation face monocular noise texture

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Area of Science:

Computer Vision
Biometrics
Machine Learning

Background:

Face presentation-attack-detection (PAD) is crucial for secure authentication, protecting against spoofing methods like photos and videos.
Current high-performance PAD systems often rely on expensive 3D imaging, driving research into cost-effective monocular (single-camera) approaches.
Monocular PAD methods excel in controlled settings but struggle with real-world imaging variations such as sensor noise and dynamic lighting.

Purpose of the Study:

To develop a synthetic augmentation strategy to improve the robustness of monocular face PAD against real-world noise.
To create a practical and cost-effective solution for enhancing PAD performance without extensive data collection under all conditions.

Main Methods:

Introduced a physics-informed noise augmentation toolbox with twelve generators simulating sensor and lighting effects.
Evaluated the generated features against popular augmentation methods on noisy test datasets.
Conducted an ablation study to quantify the impact of synthetic augmentations versus real data.

Main Results:

The proposed synthetic noise augmentations generated more robust PAD features compared to existing methods in noisy evaluations.
The augmentation toolbox also improved accuracy on clean test data, indicating better discrimination between spoof and imaging artifacts.
Achieved superior test accuracy with only 30% of participants requiring full imaging, demonstrating significant data collection simplification.

Conclusions:

Physics-informed synthetic noise augmentations offer a pragmatic approach to enhance monocular face PAD robustness.
This method effectively addresses real-world noise challenges, leading to improved security and reduced data acquisition costs.
Synthetic augmentations are a valuable tool for developing more resilient and accessible biometric authentication systems.