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 [BibTeX] [Marc21]
A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection
Type of publication: Idiap-RR
Citation: George_Idiap-RR-02-2022
Number: Idiap-RR-02-2022
Year: 2022
Month: 2
Institution: Idiap
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.
Keywords:
Projects Idiap
ODIN/BATL
Authors George, Anjith
Geissbuhler, David
Marcel, S├ębastien
Added by: [ADM]
Total mark: 0
Attachments
  • George_Idiap-RR-02-2022.pdf (MD5: 3c9950388f3d6ecf751cf5c9a11f060f)
       (Arxiv Version.)
Notes