Abstract
In the current context of digital transformation, the increasing trend in the use of personal devices for accessing online services has fostered the necessity of secure cyberphysical solutions. Biometric technologies for mobile devices, and face recognition specifically, have emerged as a secure and convenient approach. However, such a mobile scenario also brings some specific threats, and spoofing attack detection is, without any doubt, one of the most challenging. Although much effort has been devoted in anti-spoofing techniques over the past few years, there are still many challenges to be solved when implementing these systems in real use cases. This chapter analyses some of the gaps between research and real scenario deployments, including generalisation, usability, and performance. More specifically, we will focus on how to select and configure an algorithm for real scenario deployments, paying special attention to use cases involving limited processing capacity devices (e.g., mobile devices), and we will present a publicly available evaluation framework for this purpose.
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The evaluation framework may be downloaded using the following URL: https://github.com/Gradiant/bob.chapter.hobpad2.facepadprotocols.
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Acknowledgements
We thank the colleagues of the Biometrics Team at Gradiant for their assistance in developing the reproducible toolkit.
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Costa-Pazo, A., Vazquez-Fernandez, E., Alba-Castro, J.L., González-Jiménez, D. (2019). Challenges of Face Presentation Attack Detection in Real Scenarios. In: Marcel, S., Nixon, M., Fierrez, J., Evans, N. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-92627-8_12
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