Face presentation attack detection (PAD) has received increasing attention ever since the vulnerabilities to spoofing have been widely recognized. The state of the art in unimodal and multi-modal face anti-spoofing has been assessed in eight international competitions organized in conjunction with major biometrics and computer vision conferences in 2011, 2013, 2017, 2019, 2020 and 2021, each introducing new challenges to the research community. In this chapter, we present the design and results of the five latest competitions from 2019 until 2021. The first two challenges aimed to evaluate the effectiveness of face PAD in multi-modal setup introducing near-infrared (NIR) and depth modalities in addition to colour camera data, while the latest three competitions focused on evaluating domain and attack type generalization abilities of face PAD algorithms operating on conventional colour images and videos. We also discuss the lessons learnt from the competitions and future challenges in the field in general.
翻译:2011年、2013年、2013年、2017年、2019年、2020年和2021年,结合大型生物鉴别学和计算机愿景会议组织了八次国际竞赛,评估了单式和多式面部反面部反面部的先进水平,每场竞赛都给研究界带来了新的挑战。本章介绍2019年至2021年5次最新竞赛的设计和成果。前两项挑战旨在评估面部面部PAD在引入近红外线和深度模式的多式结构中的有效性,此外还有彩色相机数据,而最近三次竞赛的重点是评估以传统彩色图像和视频操作的面部PAD算法的域和攻击型通用能力。我们还讨论了从竞争中吸取的经验教训和整个领域未来挑战。