3D face mask presentation attack detection based on intrinsic image analysis-Reference-Cited by-同舟云学术

3D face mask presentation attack detection based on intrinsic image analysis

Published:2020-03-11 Issue:3 Volume:9 Page:100-108
ISSN:2047-4938
Container-title:IET Biometrics
language:en
Short-container-title:IET biom.

Author:

Li Lei¹,Xia Zhaoqiang¹,Jiang Xiaoyue¹,Ma Yupeng¹,Roli Fabio²,Feng Xiaoyi¹

Affiliation:

1. School of Electronics and InformationNorthwestern Polytechnical UniversityXi'anShaanxiPeople's Republic of China

2. Department of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2019.0155

Reference47 articles.

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2. Chingovska I. Anjos A. Marcel S.: ‘On the effectiveness of local binary patterns in face anti‐spoofing’.Biometrics Special Interest Group Darmstadt Germany 2012 pp.1–7

3. Erdogmus N. Marcel S.: ‘Spoofing in 2d face recognition with 3d masks and anti‐spoofing with kinect’.IEEE Sixth Int. Conf. on Biometrics: Theory Applications and Systems Arlington USA 2013 pp.1–6

4. Määttä J. Hadid A. Pietikäinen M.: ‘Face spoofing detection from single images using micro‐texture analysis’.Int. Joint Conf. on Biometrics Washington D.C. USA 2011 pp.1–7

5. Boulkenafet Z. Komulainen J Hadid A.: ‘Face anti‐spoofing based on color texture analysis’.IEEE Int. Conf. on Image Processing Quebec Canada 2015 pp.2636–2640

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