DocumentCode
615122
Title
Countermeasure for the protection of face recognition systems against mask attacks
Author
Kose, Neslihan ; Dugelay, Jean-Luc
Author_Institution
Multimedia Dept., EURECOM, Sophia-Antipolis, France
fYear
2013
fDate
22-26 April 2013
Firstpage
1
Lastpage
6
Abstract
There are several types of spoofing attacks to face recognition systems such as photograph, video or mask attacks. Recent studies show that face recognition systems are vulnerable to these attacks. In this paper, a countermeasure technique is proposed to protect face recognition systems against mask attacks. To the best of our knowledge, this is the first time a countermeasure is proposed to detect mask attacks. The reason for this delay is mainly due to the unavailability of public mask attacks databases. In this study, a 2D+3D face mask attacks database is used which is prepared for a research project in which the authors are all involved. The performance of the countermeasure is evaluated on both the texture images and the depth maps, separately. The results show that the proposed countermeasure gives satisfactory results using both the texture images and the depth maps. The performance of the countermeasure is observed to be slight better when the technique is applied on texture images instead of depth maps, which proves that face texture provides more information than 3D face shape characteristics using the proposed approach.
Keywords
face recognition; image texture; security of data; visual databases; 2D-3D face mask attack; 3D face shape characteristics; countermeasure technique; depth maps; face recognition system protection; face texture; mask attack detection; public mask attacks database; spoofing attacks; texture images; Databases; Face; Face recognition; Histograms; Shape; Solid modeling; Training; countermeasure; face spoofing; mask attacks;
fLanguage
English
Publisher
ieee
Conference_Titel
Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on
Conference_Location
Shanghai
Print_ISBN
978-1-4673-5545-2
Electronic_ISBN
978-1-4673-5544-5
Type
conf
DOI
10.1109/FG.2013.6553761
Filename
6553761
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