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Found 6 papers, 1 papers with code
Semi-Supervised learning for Face Anti-Spoofing using Apex frame
Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance.
Saliency-based Video Summarization for Face Anti-spoofing
Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing detection that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency.
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems.
Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection
The latter combines the predictions from the base models, leveraging their complementary information to better handle unseen target domains and enhance the overall performance.
Face Anti-Spoofing from the Perspective of Data Sampling
In particular, the proposed scheme provides a much lower error (from 15. 2% to 6. 7% on CASIA-FASD and 5. 9% to 4. 9% on Replay-Attack) compared to baselines in cross-database scenarios.
Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets
To achieve this, we exploit the temporal consistency based on three RGB frames which are acquired at three different times in the video sequence.
