人脸反欺骗的细粒度标注
Fine-Grained Annotation for Face Anti-Spoofing
AI总结：
本文提出一种基于SAM和关键点提示的细粒度标注方法，生成三通道标注图并引入MCREA增强，在数据集内和跨数据集评估中优于现有最先进方法。
摘要：
人脸反欺骗在保护人脸识别系统免受呈现攻击方面发挥着关键作用。尽管现有的深度学习方法取得了有前景的结果，但它们仍然面临缺乏细粒度标注的问题，这导致模型学习到与任务无关或不忠实的特征。在本文中，我们提出了一种用于人脸反欺骗的细粒度标注方法。具体来说，我们首先利用Segment Anything Model (SAM)通过使用人脸关键点作为点提示来获取像素级分割掩码。人脸关键点提供了分割语义，将人脸分割成多个区域。然后，我们将这些区域作为掩码，并将它们组装成三个独立的标注图：欺骗图、活体图和背景图。最后，我们将这三个独立的图合并成一个三通道图，作为模型训练的标注。此外，我们引入了多通道区域交换增强（MCREA）来多样化训练数据并减少过拟合。实验结果表明，我们的方法在数据集内和跨数据集评估中均优于现有的最先进方法。
英文摘要：
Face anti-spoofing plays a critical role in safeguarding facial recognition systems against presentation attacks. While existing deep learning methods show promising results, they still suffer from the lack of fine-grained annotations, which lead models to learn task-irrelevant or unfaithful features. In this paper, we propose a fine-grained annotation method for face anti-spoofing. Specifically, we first leverage the Segment Anything Model (SAM) to obtain pixel-wise segmentation masks by utilizing face landmarks as point prompts. The face landmarks provide segmentation semantics, which segments the face into regions. We then adopt these regions as masks and assemble them into three separate annotation maps: spoof, living, and background maps. Finally, we combine three separate maps into a three-channel map as annotations for model training. Furthermore, we introduce the Multi-Channel Region Exchange Augmentation (MCREA) to diversify training data and reduce overfitting. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches in both intra-dataset and cross-dataset evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
