@inproceedings{
guo2024generalizable,
title={Generalizable Face Anti-spoofing via Style-conditional Prompt Token Learning},
author={Jiabao Guo and Huan Liu and Yizhi Luo and Xueli Hu and Hang Zou and Yuan Zhang and Hui Liu and Bo Zhao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=ucDj0MfF7m}
}
Face anti-spoofing (FAS) based on domain generalization (DG) has garnered increasing attention from researchers. The poor generalization is attributed to the model being overfitted to salient liveness-irrelevant signals. Previous methods addressed this issue by either mapping images from multiple domains into a common feature space or promoting the separation of image features from domain-specific and task-related features. However, direct manipulation of image features inevitably disrupts semantic structure. Utilizing the text features of vision-language pre-trained (VLP) models, such as CLIP, to dynamically adjust image features offers the potential for better generalization, exploring a broader feature space while preserving semantic information. Specifically, we propose a FAS method called style-conditional prompt token learning (S-CTPL), which aims to generate generalized text features by training introduced prompt tokens to encode visual styles. These tokens are then utilized as weights for classifiers, enhancing the model's generalization. Unlike inherently static prompt tokens, our dynamic prompt tokens adaptively capture live-irrelevant signals from instance-specific styles, increasing their diversity through mixed feature statistics to further mitigate model overfitting. Thorough experimental analysis demonstrates that S-CPTL outperforms current top-performing methods across four distinct cross-dataset benchmarks.
Style-conditional Prompt Token Learning for Generalizable Face Anti-spoofing
Jiabao Guo Huan Liu Yizhi Luo Xueli Hu Hang Zou Yuan Zhang Hui Liu Bo Zhao