%0 Book Section
%T Face Anti-spoofing Progress Driven by Academic Challenges
%A Jun Wan
%A Guodong Guo
%A Sergio Escalera
%A Hugo Jair Escalante
%A Stan Z Li
%B Advances in Face Presentation Attack Detection
%D 2023
%F Jun Wan2023
%O HUPBA
%O exported from refbase (http://158.109.8.37/show.php?record=3957), last updated on Fri, 26 Jan 2024 10:10:32 +0100
%X With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges
%U https://link.springer.com/chapter/10.1007/978-3-031-32906-7_1
%P 1–15