532 research outputs found
Biometric presentation attack detection: beyond the visible spectrum
The increased need for unattended authentication in
multiple scenarios has motivated a wide deployment of biometric
systems in the last few years. This has in turn led to the
disclosure of security concerns specifically related to biometric
systems. Among them, presentation attacks (PAs, i.e., attempts
to log into the system with a fake biometric characteristic or
presentation attack instrument) pose a severe threat to the
security of the system: any person could eventually fabricate
or order a gummy finger or face mask to impersonate someone
else. In this context, we present a novel fingerprint presentation
attack detection (PAD) scheme based on i) a new capture device
able to acquire images within the short wave infrared (SWIR)
spectrum, and i i) an in-depth analysis of several state-of-theart
techniques based on both handcrafted and deep learning
features. The approach is evaluated on a database comprising
over 4700 samples, stemming from 562 different subjects and
35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a
detection equal error rate (D-EER) as low as 1.35% even in a
realistic scenario where five different PAI species are considered
only for testing purposes (i.e., unknown attacks
An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements
Spectral imaging has recently gained traction for face recognition in
biometric systems. We investigate the merits of spectral imaging for face
recognition and the current challenges that hamper the widespread deployment of
spectral sensors for face recognition. The reliability of conventional face
recognition systems operating in the visible range is compromised by
illumination changes, pose variations and spoof attacks. Recent works have
reaped the benefits of spectral imaging to counter these limitations in
surveillance activities (defence, airport security checks, etc.). However, the
implementation of this technology for biometrics, is still in its infancy due
to multiple reasons. We present an overview of the existing work in the domain
of spectral imaging for face recognition, different types of modalities and
their assessment, availability of public databases for sake of reproducible
research as well as evaluation of algorithms, and recent advancements in the
field, such as, the use of deep learning-based methods for recognizing faces
from spectral images
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks
This paper addresses the problem of face presentation attack detection using
different image modalities. In particular, the usage of short wave infrared
(SWIR) imaging is considered. Face presentation attack detection is performed
using recent models based on Convolutional Neural Networks using only carefully
selected SWIR image differences as input. Conducted experiments show superior
performance over similar models acting on either color images or on a
combination of different modalities (visible, NIR, thermal and depth), as well
as on a SVM-based classifier acting on SWIR image differences. Experiments have
been carried on a new public and freely available database, containing a wide
variety of attacks. Video sequences have been recorded thanks to several
sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal
spectra, as well as depth data. The best proposed approach is able to almost
perfectly detect all impersonation attacks while ensuring low bonafide
classification errors. On the other hand, obtained results show that
obfuscation attacks are more difficult to detect. We hope that the proposed
database will foster research on this challenging problem. Finally, all the
code and instructions to reproduce presented experiments is made available to
the research community
Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario
Face anti-spoofing is the key to preventing security breaches in biometric
recognition applications. Existing software-based and hardware-based face
liveness detection methods are effective in constrained environments or
designated datasets only. Deep learning method using RGB and infrared images
demands a large amount of training data for new attacks. In this paper, we
present a face anti-spoofing method in a real-world scenario by automatic
learning the physical characteristics in polarization images of a real face
compared to a deceptive attack. A computational framework is developed to
extract and classify the unique face features using convolutional neural
networks and SVM together. Our real-time polarized face anti-spoofing (PAAS)
detection method uses a on-chip integrated polarization imaging sensor with
optimized processing algorithms. Extensive experiments demonstrate the
advantages of the PAAS technique to counter diverse face spoofing attacks
(print, replay, mask) in uncontrolled indoor and outdoor conditions by learning
polarized face images of 33 people. A four-directional polarized face image
dataset is released to inspire future applications within biometric
anti-spoofing field.Comment: 14pages,8figure
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its
vital role in securing face recognition systems from presentation attacks
(PAs). As more and more realistic PAs with novel types spring up, traditional
FAS methods based on handcrafted features become unreliable due to their
limited representation capacity. With the emergence of large-scale academic
datasets in the recent decade, deep learning based FAS achieves remarkable
performance and dominates this area. However, existing reviews in this field
mainly focus on the handcrafted features, which are outdated and uninspiring
for the progress of FAS community. In this paper, to stimulate future research,
we present the first comprehensive review of recent advances in deep learning
based FAS. It covers several novel and insightful components: 1) besides
supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also
investigate recent methods with pixel-wise supervision (e.g., pseudo depth
map); 2) in addition to traditional intra-dataset evaluation, we collect and
analyze the latest methods specially designed for domain generalization and
open-set FAS; and 3) besides commercial RGB camera, we summarize the deep
learning applications under multi-modal (e.g., depth and infrared) or
specialized (e.g., light field and flash) sensors. We conclude this survey by
emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
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