A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection

• URL: http://arxiv.org/abs/2202.10286v1
• Date: Mon, 21 Feb 2022 15:04:39 GMT
• Title: A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection
• Authors: Anjith George and David Geissbuhler and Sebastien Marcel
• Abstract summary: presentation attack detection (PAD) systems try to address this problem. Lack of generalization and robustness continues to be a major concern. We use a multi-channel convolutional network-based architecture, which uses pixel-wise binary supervision.
• Score: 6.488575826304023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The vulnerability against presentation attacks is a crucial problem undermining the wide-deployment of face recognition systems. Though presentation attack detection (PAD) systems try to address this problem, the lack of generalization and robustness continues to be a major concern. Several works have shown that using multi-channel PAD systems could alleviate this vulnerability and result in more robust systems. However, there is a wide selection of channels available for a PAD system such as RGB, Near Infrared, Shortwave Infrared, Depth, and Thermal sensors. Having a lot of sensors increases the cost of the system, and therefore an understanding of the performance of different sensors against a wide variety of attacks is necessary while selecting the modalities. In this work, we perform a comprehensive study to understand the effectiveness of various imaging modalities for PAD. The studies are performed on a multi-channel PAD dataset, collected with 14 different sensing modalities considering a wide range of 2D, 3D, and partial attacks. We used the multi-channel convolutional network-based architecture, which uses pixel-wise binary supervision. The model has been evaluated with different combinations of channels, and different image qualities on a variety of challenging known and unknown attack protocols. The results reveal interesting trends and can act as pointers for sensor selection for safety-critical presentation attack detection systems. The source codes and protocols to reproduce the results are made available publicly making it possible to extend this work to other architectures.

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