Abstract
Nowadays, adoption of face recognition for biometric authentication systems is widespread, mainly because this is one of the most accessible biometric characteristic. Techniques intended on deceive these kinds of systems by using a forged biometric sample, such as a printed paper or a recorded video of a genuine access, are known as presentation attacks. Presentation Attack Detection is a crucial step for preventing this kind of unauthorized accesses into restricted areas or devices. In this paper, we propose a new method that relies on a combination of the intrinsic properties of the image with deep neural networks to detect presentation attack attempts. Exploring depth, salience and illumination properties, along with a Convolutional Neural Network, proposed method produce robust and discriminant features which are then classified to detect presentation attacks attempts. In a very challenging cross-dataset scenario, proposed method outperform state-of-the-art methods in two of three evaluated datasets.
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Notes
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Since this paper focus on data-driven techniques, we focused our literature review on this kind of methods.
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A classifier which consider scene information could lead to undesirable features and an unfair comparison against literature methods.
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Acknowledgments
We would like to thank São Paulo Research Foundation (FAPESP) (#2017/12631-6), to the National Council for Scientific and Technological Development - CNPq (#423797/2016-6), and to NVIDIA for the donation of a TITAN XP GPU to be used on this research.
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Bresan, R., Beluzo, C., Carvalho, T. (2020). Exposing Presentation Attacks by a Combination of Multi-intrinsic Image Properties, Convolutional Networks and Transfer Learning. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_14
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