Exploring the Effectiveness of Lightweight Architectures for Face Anti-Spoofing - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2023

Exploring the Effectiveness of Lightweight Architectures for Face Anti-Spoofing

Abstract

Detecting spoof faces is crucial in ensuring the robustness of face-based identity recognition and access control systems, as faces can be captured easily without the user’s cooperation in uncontrolled environments. Several deep models have been proposed for this task, achieving high levels of accuracy but at a high computational cost. Considering the very good results obtained by lightweight deep networks on different computer vision tasks, in this work we explore the effectiveness of this kind of architectures for face anti-spoofing. Specifically, we asses the performance of three lightweight face models on two challenging benchmark databases. The conducted experiments indicate that face anti-spoofing solutions based on lightweight face models are able to achieve comparable accuracy results to those obtained by state-of-the-art very deep models, with a significantly lower computational complexity.
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hal-04393650 , version 1 (23-01-2024)

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Yoanna Martínez-Díaz, Heydi Méndez-Vázquez, Luis S. Luevano, Miguel González-Mendoza. Exploring the Effectiveness of Lightweight Architectures for Face Anti-Spoofing. CVPRW 2023 - IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Jun 2023, Vancouver, Canada. pp.6392-6402, ⟨10.1109/CVPRW59228.2023.00680⟩. ⟨hal-04393650⟩
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