Introduction

In this challenge, we will release a largest cross-ethnicity dataset CASIA-SURF CeFA for face antispoofing (please refer to [1]) .  It includes four protocols. But for this challenge, we have two tracks and only use protocol 4  (Cross-ethnicity & PAI) .

Track 1 (Multi-modal data):   It uses the protocol 4  (Cross-ethnicity & PAI)  in [1].  Participates can use multiply modalities, such as RGB, depth and IR data. Any extra data or pretrained model cannot be used in track 1 (link).  

Track 2 (Single RGB modal data): It uses the protocol protocol 4 (Cross-ethnicity & PAI)  in [1].  But participates are only permitted to use the single RGB modality. Any extra data or pretrained model cannot be used in track 2 (link). 

Dataset Application [link]

Full Schedule of  Workshop@CVPR2020 (https://sites.google.com/view/wmediaforensics2020/home?authuser=0) 

Organizers

Jun Wan (万军), NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA), China,  jun.wan@ia.ac.cn 

Ajian Liu, Macau University of Science and Technology (M.U.S.T.),  Macau, China, 1809853nii30001@student.must.edu.mo 

Sergio Escalera, Computer Vision Center (UAB) and University of Barcelona, Spain, sergio@maia.ub.es 

Hugo Jair Escalante, INAOE, ChaLearn, Mexico, hugojair@inaoep.mx  

Isabelle Guyon, Université Paris-Saclay, France and ChaLearn, Berkeley, California, USA,  guyon@chalearn.org 

Guodong Guo, IDL, Baidu Research,  China, guoguodong01@baidu.com 

Shaopeng Tang, Beijing Surfing Technology Ltd, shaopeng@surfingtech.cn 

Sponsor

Baidu (the exclusive sponsorship)

Ref:

[1]Ajian Liu, Zichang Tan, Xuan Li, Jun Wan*, Sergio Escalera, Guodong Guo, Stan Z. Li, " Static and Dynamic Fusion for Multi-modal Cross-ethnicity Face Anti-spoofing ", arxiv, 2019.