CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations ECCV2020
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations
论文原文
构建了大规模密集标注数据集CelebA-Spoof
- Large-Scale. CelebA-Spoof comprises of a total of 10177 subjects, 625537 images, which is the largest dataset in face anti-spoofing.
- Diversity. For collecting images, we use more than 10 different input tensors, including phones, pads and personal computers (PC). Besides, we cover images in 8 different sessions. （combination of illumination and environment forms the “session”）
- Rich Annotations. Each image in CelebA-Spoof is defined with 43 different attributes: 40 types of Face Attribute defined in CelebA [25] plus 3 attributes of face anti-spoofing, including: Spoof Type, Illumination Condition and Environment.
设计了Auxiliary information Embedding Network (AENet)
AENetC,S多任务共同学习辅助语义属性和二值分类标签
AENetC,G多任务共同学习辅助几何信息和二值分类标签
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