论文标题
柔性模态脸反启动：基准测试
Flexible-Modal Face Anti-Spoofing: A Benchmark
论文作者
论文摘要
面部反欺骗（FAS）在保护面部识别系统免于演示攻击方面起着至关重要的作用。受益于成熟的摄像头传感器，单模式（RGB）和多模式（例如，RGB+DEPTH）FAS，已在具有不同的传感器/模态配置的各种情况下应用。现有的单模式FAS方法通常会为每种可能的模态方案分别训练和部署模型，这可能是冗余且效率低下的。我们可以训练统一模型，并在各种模式方案下灵活部署它吗？在本文中，我们建立了第一个具有“所有人的训练训练”原则的灵活模式FAS基准。具体来说，使用训练有素的多模式（RGB+DEPTH+IR）FAS模型，在四个柔性模式子协议（RGB，RGB+DEPTH，RGB+IR和RGB+DEPTH+IR）上都进行了内部和交叉数据库测试。我们还研究了柔性模式FAS的普遍的深层模型和功能融合策略。我们希望这个新的基准将促进多模式FAS的未来研究。协议和代码可在https://github.com/zitongyu/flex-modal-fas上找到。
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Benefitted from the maturing camera sensors, single-modal (RGB) and multi-modal (e.g., RGB+Depth) FAS has been applied in various scenarios with different configurations of sensors/modalities. Existing single- and multi-modal FAS methods usually separately train and deploy models for each possible modality scenario, which might be redundant and inefficient. Can we train a unified model, and flexibly deploy it under various modality scenarios? In this paper, we establish the first flexible-modal FAS benchmark with the principle `train one for all'. To be specific, with trained multi-modal (RGB+Depth+IR) FAS models, both intra- and cross-dataset testings are conducted on four flexible-modal sub-protocols (RGB, RGB+Depth, RGB+IR, and RGB+Depth+IR). We also investigate prevalent deep models and feature fusion strategies for flexible-modal FAS. We hope this new benchmark will facilitate the future research of the multi-modal FAS. The protocols and codes are available at https://github.com/ZitongYu/Flex-Modal-FAS.
