1,035 research outputs found
Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study
Biometric presentation attack detection is gaining increasing attention.
Users of mobile devices find it more convenient to unlock their smart
applications with finger, face or iris recognition instead of passwords. In
this paper, we survey the approaches presented in the recent literature to
detect face and iris presentation attacks. Specifically, we investigate the
effectiveness of fine tuning very deep convolutional neural networks to the
task of face and iris antispoofing. We compare two different fine tuning
approaches on six publicly available benchmark datasets. Results show the
effectiveness of these deep models in learning discriminative features that can
tell apart real from fake biometric images with very low error rate.
Cross-dataset evaluation on face PAD showed better generalization than state of
the art. We also performed cross-dataset testing on iris PAD datasets in terms
of equal error rate which was not reported in literature before. Additionally,
we propose the use of a single deep network trained to detect both face and
iris attacks. We have not noticed accuracy degradation compared to networks
trained for only one biometric separately. Finally, we analyzed the learned
features by the network, in correlation with the image frequency components, to
justify its prediction decision.Comment: A preprint of a paper accepted by IET Biometrics journal and is
subject to Institution of Engineering and Technology Copyrigh
On the Learning of Deep Local Features for Robust Face Spoofing Detection
Biometrics emerged as a robust solution for security systems. However, given
the dissemination of biometric applications, criminals are developing
techniques to circumvent them by simulating physical or behavioral traits of
legal users (spoofing attacks). Despite face being a promising characteristic
due to its universality, acceptability and presence of cameras almost
everywhere, face recognition systems are extremely vulnerable to such frauds
since they can be easily fooled with common printed facial photographs.
State-of-the-art approaches, based on Convolutional Neural Networks (CNNs),
present good results in face spoofing detection. However, these methods do not
consider the importance of learning deep local features from each facial
region, even though it is known from face recognition that each facial region
presents different visual aspects, which can also be exploited for face
spoofing detection. In this work we propose a novel CNN architecture trained in
two steps for such task. Initially, each part of the neural network learns
features from a given facial region. Afterwards, the whole model is fine-tuned
on the whole facial images. Results show that such pre-training step allows the
CNN to learn different local spoofing cues, improving the performance and the
convergence speed of the final model, outperforming the state-of-the-art
approaches
IriTrack: Liveness Detection Using Irises Tracking for Preventing Face Spoofing Attacks
Face liveness detection has become a widely used technique with a growing
importance in various authentication scenarios to withstand spoofing attacks.
Existing methods that perform liveness detection generally focus on designing
intelligent classifiers or customized hardware to differentiate between the
image or video samples of a real legitimate user and the imitated ones.
Although effective, they can be resource-consuming and detection results may be
sensitive to environmental changes. In this paper, we take iris movement as a
significant liveness sign and propose a simple and efficient liveness detection
system named IriTrack. Users are required to move their eyes along with a
randomly generated poly-line, and trajectories of irises are then used as
evidences for liveness detection. IriTrack allows checking liveness by using
data collected during user-device interactions. We implemented a prototype and
conducted extensive experiments to evaluate the performance of the proposed
system. The results show that IriTrack can fend against spoofing attacks with a
moderate and adjustable time overhead
How far did we get in face spoofing detection?
The growing use of control access systems based on face recognition shed
light over the need for even more accurate systems to detect face spoofing
attacks. In this paper, an extensive analysis on face spoofing detection works
published in the last decade is presented. The analyzed works are categorized
by their fundamental parts, i.e., descriptors and classifiers. This structured
survey also brings the temporal evolution of the face spoofing detection field,
as well as a comparative analysis of the works considering the most important
public data sets in the field. The methodology followed in this work is
particularly relevant to observe trends in the existing approaches, to discuss
still opened issues, and to propose new perspectives for the future of face
spoofing detection
FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning
Nowadays, the adoption of face recognition for biometric authentication
systems is usual, mainly because this is one of the most accessible biometric
modalities. Techniques that rely on trespassing these kind of systems by using
a forged biometric sample, such as a printed paper or a recorded video of a
genuine access, are known as presentation attacks, but may be also referred in
the literature as face spoofing. Presentation attack detection is a crucial
step for preventing this kind of unauthorized accesses into restricted areas
and/or devices. In this paper, we propose a novel approach which relies in a
combination between intrinsic image properties and deep neural networks to
detect presentation attack attempts. Our method explores depth, salience and
illumination maps, associated with a pre-trained Convolutional Neural Network
in order to produce robust and discriminant features. Each one of these
properties are individually classified and, in the end of the process, they are
combined by a meta learning classifier, which achieves outstanding results on
the most popular datasets for PAD. Results show that proposed method is able to
overpass state-of-the-art results in an inter-dataset protocol, which is
defined as the most challenging in the literature.Comment: 7 pages, 1 figure, 7 table
PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses
For applications such as airport border control, biometric technologies that
can process many capture subjects quickly, efficiently, with weak supervision,
and with minimal discomfort are desirable. Facial recognition is particularly
appealing because it is minimally invasive yet offers relatively good
recognition performance. Unfortunately, the combination of weak supervision and
minimal invasiveness makes even highly accurate facial recognition systems
susceptible to spoofing via presentation attacks. Thus, there is great demand
for an effective and low cost system capable of rejecting such attacks.To this
end we introduce PARAPH -- a novel hardware extension that exploits different
measurements of light polarization to yield an image space in which
presentation media are readily discernible from Bona Fide facial
characteristics. The PARAPH system is inexpensive with an added cost of less
than 10 US dollars. The system makes two polarization measurements in rapid
succession, allowing them to be approximately pixel-aligned, with a frame rate
limited by the camera, not the system. There are no moving parts above the
molecular level, due to the efficient use of twisted nematic liquid crystals.
We present evaluation images using three presentation attack media next to an
actual face -- high quality photos on glossy and matte paper and a video of the
face on an LCD. In each case, the actual face in the image generated by PARAPH
is structurally discernible from the presentations, which appear either as
noise (print attacks) or saturated images (replay attacks).Comment: Accepted to CVPR 2016 Biometrics Worksho
Discriminative Representation Combinations for Accurate Face Spoofing Detection
Three discriminative representations for face presentation attack detection
are introduced in this paper. Firstly we design a descriptor called spatial
pyramid coding micro-texture (SPMT) feature to characterize local appearance
information. Secondly we utilize the SSD, which is a deep learning framework
for detection, to excavate context cues and conduct end-to-end face
presentation attack detection. Finally we design a descriptor called template
face matched binocular depth (TFBD) feature to characterize stereo structures
of real and fake faces. For accurate presentation attack detection, we also
design two kinds of representation combinations. Firstly, we propose a
decision-level cascade strategy to combine SPMT with SSD. Secondly, we use a
simple score fusion strategy to combine face structure cues (TFBD) with local
micro-texture features (SPMT). To demonstrate the effectiveness of our design,
we evaluate the representation combination of SPMT and SSD on three public
datasets, which outperforms all other state-of-the-art methods. In addition, we
evaluate the representation combination of SPMT and TFBD on our dataset and
excellent performance is also achieved.Comment: To be published in Pattern Recognitio
An Overview of Face Liveness Detection
Face recognition is a widely used biometric approach. Face recognition
technology has developed rapidly in recent years and it is more direct, user
friendly and convenient compared to other methods. But face recognition systems
are vulnerable to spoof attacks made by non-real faces. It is an easy way to
spoof face recognition systems by facial pictures such as portrait photographs.
A secure system needs Liveness detection in order to guard against such
spoofing. In this work, face liveness detection approaches are categorized
based on the various types techniques used for liveness detection. This
categorization helps understanding different spoof attacks scenarios and their
relation to the developed solutions. A review of the latest works regarding
face liveness detection works is presented. The main aim is to provide a simple
path for the future development of novel and more secured face liveness
detection approach.Comment: International Journal on Information Theory (IJIT), Vol.3, No.2,
April 201
A Survey on Unknown Presentation Attack Detection for Fingerprint
Fingerprint recognition systems are widely deployed in various real-life
applications as they have achieved high accuracy. The widely used applications
include border control, automated teller machine (ATM), and attendance
monitoring systems. However, these critical systems are prone to spoofing
attacks (a.k.a presentation attacks (PA)). PA for fingerprint can be performed
by presenting gummy fingers made from different materials such as silicone,
gelatine, play-doh, ecoflex, 2D printed paper, 3D printed material, or latex.
Biometrics Researchers have developed Presentation Attack Detection (PAD)
methods as a countermeasure to PA. PAD is usually done by training a machine
learning classifier for known attacks for a given dataset, and they achieve
high accuracy in this task. However, generalizing to unknown attacks is an
essential problem from applicability to real-world systems, mainly because
attacks cannot be exhaustively listed in advance. In this survey paper, we
present a comprehensive survey on existing PAD algorithms for fingerprint
recognition systems, specifically from the standpoint of detecting unknown PAD.
We categorize PAD algorithms, point out their advantages/disadvantages, and
future directions for this area.Comment: Submitted to 3rd International Conference on Intelligent Technologies
and Applications INTAP 202
Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing
Using printed photograph and replaying videos of biometric modalities, such
as iris, fingerprint and face, are common attacks to fool the recognition
systems for granting access as the genuine user. With the growing online
person-to-person shopping (e.g., Ebay and Craigslist), such attacks also
threaten those services, where the online photo illustration might not be
captured from real items but from paper or digital screen. Thus, the study of
anti-spoofing should be extended from modality-specific solutions to
generic-object-based ones. In this work, we define and tackle the problem of
Generic Object Anti-Spoofing (GOAS) for the first time. One significant cue to
detect these attacks is the noise patterns introduced by the capture sensors
and spoof mediums. Different sensor/medium combinations can result in diverse
noise patterns. We propose a GAN-based architecture to synthesize and identify
the noise patterns from seen and unseen medium/sensor combinations. We show
that the procedure of synthesis and identification are mutually beneficial. We
further demonstrate the learned GOAS models can directly contribute to
modality-specific anti-spoofing without domain transfer. The code and GOSet
dataset are available at cvlab.cse.msu.edu/project-goas.html.Comment: In IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
202
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