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SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern
A. Agarwal, , , A. Noore
Published in Institute of Electrical and Electronics Engineers Inc.
2018
Volume: 2018-January
   
Pages: 659 - 665
Abstract
Advancements in smartphone applications have empowered even non-technical users to perform sophisticated operations such as morphing in faces as few tap operations. While such enablements have positive effects, as a negative side, now anyone can digitally attack face (biometric) recognition systems. For example, face swapping application of Snapchat can easily create 'swapped' identities and circumvent face recognition system. This research presents a novel database, termed as SWAPPED - Digital Attack Video Face Database, prepared using Snap chat's application which swaps/stitches two faces and creates videos. The database contains bonafide face videos and face swapped videos of multiple subjects. Baseline face recognition experiments using commercial system shows over 90% rank-1 accuracy when attack videos are used as probe. As a second contribution, this research also presents a novel Weighted Local Magnitude Pattern feature descriptor based presentation attack detection algorithm which outperforms several existing approaches. © 2017 IEEE.
About the journal
JournalData powered by TypesetIEEE International Joint Conference on Biometrics, IJCB 2017
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN2474-9699
Open AccessNo