Abstract
Non-referential Face Image Quality Assessment (FIQA) methods have gained popularity as a pre-filtering step in Face Recognition (FR) systems. In most of them, the quality score is usually designed with face comparison in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Attack Detection (PAD). In this paper, we study the effect of quality assessment methods on filtering bona fide and attack samples, their impact on PAD systems, and how the performance of such systems is improved when training on a filtered (by quality) dataset. On a Vision Transformer PAD algorithm, a reduction of 20% of the training dataset by remoing lower-quality samples allowed us to improve the Bona fide Presentation Classification Error Rate (BPCER) by 3% in a cross-dataset test.
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This dataset is sequestered available only for evaluation.
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Acknowledgements
This work is supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 883356 and the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE.
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Aravena, C., Pasmiño, D., Tapia, J., Busch, C. (2024). Face Image Quality Estimation on Presentation Attack Detection. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_26
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