Abstract
Face spoofing is an attack attempt to obtain unauthorized access by using photos, videos, or 3D maps of a user’s face. The development of anti-spoofing strategies evolves at the same time as facial authentication technologies. Many methods for preventing such attacks have been proposed recently [1,2,3], showing excellent results accuracy in fraud detection. However, most of these methods are very efficient in detecting patterns—such as fraud—present a major disadvantage: a high computational cost. This cost directly impacts the user experience of the facial authentication system, since the spoofing verification adds an extra layer of inference by artificial intelligence models, causing a longer waiting time for the authentication system’s user. This impact is most noted when the inference is performed on devices with limited computational power, such as mobile, tablets, and edge devices. In this work, we carry out an experimental analysis of the common anti-spoofing strategies considering the trade-off between correctness fraud detection and computational cost, aimed at optimizing the user experience. We also propose to use a fine-tuned Convolutional Neural Network (CNN) with a base network trained on a larger dataset and adds to our analysis.
Supported by Sidia Institute of Science and Technology, and Samsung Eletrônica da Amazônia Ltda, under the auspice of the Brazilian informatics law no 8.387/91.
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Khurshid, A., Grunitzki, R. (2021). An Experimental Analysis of Face Anti-spoofing Strategies for Real Time Applications. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1499. Springer, Cham. https://doi.org/10.1007/978-3-030-90179-0_59
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