Review of Face Presentation Attack Detection Competitions
This work addresses the problem of face spoofing vulnerabilities for biometric security systems, but it is incremental as it reviews existing competitions rather than proposing new methods.
The paper reviews face presentation attack detection competitions from 2019 to 2021, assessing state-of-the-art methods for detecting spoofing attacks using multi-modal and color data, with results showing advancements in generalization abilities.
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.