Review of Face Presentation Attack Detection Competitions
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
Saliency-based Video Summarization for Face Anti-spoofing
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
Surveillance Face Presentation Attack Detection Challenge
Face Presentation Attack Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.