Open AccessFace presentation attack detection. A comprehensive evaluation of the generalisation problem
Author(s) -
CostaPazo Artur,
PérezCabo Daniel,
JiménezCabello David,
AlbaCastro José Luis,
VazquezFernandez Esteban
Publication year - 2021
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/bme2.12049
Subject(s) - computer science , presentation (obstetrics) , robustness (evolution) , flexibility (engineering) , face (sociological concept) , modular design , key (lock) , set (abstract data type) , facial recognition system , artificial intelligence , machine learning , computer security , feature extraction , statistics , mathematics , radiology , sociology , gene , programming language , operating system , medicine , social science , biochemistry , chemistry
Abstract Face recognition technology is now mature enough to reach commercial products, such as smart phones or tablets. However, it still needs to increase robustness against imposter attacks. In this regard, face Presentation Attack Detection (face‐PAD) is a key component in providing trustable facial access to digital devices. Despite the success of several face‐PAD works in publicly available datasets, most of them fail to reach the market, revealing the lack of evaluation frameworks that represent realistic settings. Here, an extensive analysis of the generalisation problem in face‐PAD is provided, jointly with an evaluation strategy based on the aggregation of most publicly available datasets and a set of novel protocols to cover the most realistic settings, including a novel demographic bias analysis. Besides, a new fine‐grained categorisation of presentation attacks and instruments is provided, enabling higher flexibility in assessing the generalisation of different algorithms under a common framework. As a result, GRAD‐GPAD v2, a comprehensive and modular framework is presented to evaluate the performance of face‐PAD approaches in realistic settings, enabling accountability and fair comparison of most face‐PAD approaches in the literature.