Face spoofing detection from single images using texture and local shape analysis

2012 ◽  
Vol 1 (1) ◽  
pp. 3 ◽  
Author(s):  
J. Määttä ◽  
A. Hadid ◽  
M. Pietikäinen
Keyword(s):  
Shape Analysis ◽  
Local Shape ◽  
Spoofing Detection ◽  
Face Spoofing

Face Spoofing Detection Based on Local Binary Descriptors

2017 ◽  
Vol 2017 (13) ◽  
pp. 105-108
Author(s):  
Yao-Hong Tsai ◽  
Yu-Jung Lin
Keyword(s):  
Binary Descriptors ◽  
Spoofing Detection ◽  
Face Spoofing

Multispectral face spoofing detection using VIS–NIR imaging correlation

2018 ◽  
Vol 16 (02) ◽  
pp. 1840003 ◽  
Author(s):  
Xudong Sun ◽  
Lei Huang ◽  
Changping Liu
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Experimental Results ◽  
Security Systems ◽  
Local Descriptors ◽  
Confidence Factor ◽  
Nir Imaging ◽  
Spoofing Detection ◽  
Factor Map ◽  
Face Spoofing

With the wide applications of face recognition techniques, spoofing detection is playing an important role in the security systems and has drawn much attention. This research presents a multispectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging, which exploits VIS–NIR image consistency for spoofing detection. First, we use part-based methods to extract illumination robust local descriptors, and then the consistency is calculated to perform spoofing detection. In order to further exploit multispectral correlation in local patches and to be free from manually chosen regions, we learn a confidence factor map for all the patches, which is used in final classifier. Experimental results of self-collected datasets, public Msspoof and PolyU-HSFD datasets show that the proposed approach gains promising results for both intra-dataset and cross-dataset testing scenarios, and that our method can deal with different illumination and both photo and screen spoofing.

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