CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations

• URL: http://arxiv.org/abs/2007.12342v3
• Date: Sat, 1 Aug 2020 07:16:18 GMT
• Title: CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations
• Authors: Yuanhan Zhang, Zhenfei Yin, Yidong Li, Guojun Yin, Junjie Yan, Jing Shao, and Ziwei Liu
• Abstract summary: CelebA-Spoof is a large-scale face anti-spoofing dataset. It includes 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. It contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset.
• Score: 85.14435479181894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Though promising progress has been achieved, existing works still have difficulty in handling complex spoof attacks and generalizing to real-world scenarios. The main reason is that current face anti-spoofing datasets are limited in both quantity and diversity. To overcome these obstacles, we contribute a large-scale face anti-spoofing dataset, CelebA-Spoof, with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. 2) Diversity: The spoof images are captured from 8 scenes (2 environments * 4 illumination conditions) with more than 10 sensors. 3) Annotation Richness: CelebA-Spoof contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset. Equipped with CelebA-Spoof, we carefully benchmark existing methods in a unified multi-task framework, Auxiliary Information Embedding Network (AENet), and reveal several valuable observations.

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