Review of Face Presentation Attack Detection Competitions

TL;DR

This paper reviews recent face presentation attack detection competitions, highlighting advancements, challenges, and lessons learned in unimodal and multimodal face anti-spoofing research from 2019 to 2021.

Contribution

It provides a comprehensive analysis of recent competitions, emphasizing new modalities and generalization challenges in face PAD, and discusses future research directions.

Findings

Multi-modal setups improve PAD effectiveness

Domain and attack type generalization remain challenging

Competitions reveal key research gaps and progress

Abstract

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.