SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern

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....

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Vydané v:2017 IEEE International Joint Conference on Biometrics (IJCB) s. 659 - 665
Hlavní autori: Agarwal, Akshay, Singh, Richa, Vatsa, Mayank, Noore, Afzel
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2017
Predmet:
Detection algorithms
Error analysis
Face
Face recognition
Feature extraction
Mobile handsets
ISSN:2474-9699
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Shrnutí: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.
ISSN:2474-9699
DOI:10.1109/BTAS.2017.8272754