Face recognition systems are susceptible to presentation
attacks such as printed photo attacks, replay attacks, and
3D mask attacks. These attacks, primarily studied in visible
spectrum, aim to obfuscate or impersonate a person’s identity.
This paper presents a unique multispectral video face
database for face presentation attack using latex and paper
masks. The proposed Multispectral Latex Mask based
Video Face Presentation Attack (MLFP) database contains
1350 videos in visible, near infrared, and thermal spectrums.
Since the database consists of videos of subjects
without any mask as well as wearing ten different masks,
the effect of identity concealment is analyzed in each spectrum
using face recognition algorithms. We also present the
performance of existing presentation attack detection algorithms
on the proposed MLFP database. It is observed that
the thermal imaging spectrum is most effective in detecting
face presentation attacks.
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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. 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 Snapchat’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.
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- NSF-PAR ID:
- 10053782
- Date Published:
- Journal Name:
- International Joint Conference on Biometrics (IJCB)
- Page Range / eLocation ID:
- 659 to 665
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BTAS.2017.8272754
