Son Thai
ABSTRACT
Facial anti-spoofing distinguishes between real and fake faces in images and videos, which is crucial for security systems like facial authentication and payment applications. The Zalo AI Challenge organized a competition for more than 250 teams to use real-world face data to address this problem. In this article, we proposed to use the Swin Transformer backbone combined with a suitable pipeline, which helped us achieve second place in the competition. The competition evaluation results indicate that the proposed solution offers high accuracy with a Time-constraint EER of 7.1% and a faster processing time compared to other solutions.
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Index Terms
- Enhancing Face Anti-Spoofing with Swin Transformer-driven Multi-stage Pipeline
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