713 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
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
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
The adoption of large-scale iris recognition systems around the world has
brought to light the importance of detecting presentation attack images
(textured contact lenses and printouts). This work presents a new approach in
iris Presentation Attack Detection (PAD), by exploring combinations of
Convolutional Neural Networks (CNNs) and transformed input spaces through
binarized statistical image features (BSIF). Our method combines lightweight
CNNs to classify multiple BSIF views of the input image. Following explorations
on complementary input spaces leading to more discriminative features to detect
presentation attacks, we also propose an algorithm to select the best (and most
discriminative) predictors for the task at hand.An ensemble of predictors makes
use of their expected individual performances to aggregate their results into a
final prediction. Results show that this technique improves on the current
state of the art in iris PAD, outperforming the winner of LivDet-Iris2017
competition both for intra- and cross-dataset scenarios, and illustrating the
very difficult nature of the cross-dataset scenario.Comment: IEEE Transactions on Information Forensics and Security (Early
Access), 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
Synthetic Iris Presentation Attack using iDCGAN
Reliability and accuracy of iris biometric modality has prompted its
large-scale deployment for critical applications such as border control and
national ID projects. The extensive growth of iris recognition systems has
raised apprehensions about susceptibility of these systems to various attacks.
In the past, researchers have examined the impact of various iris presentation
attacks such as textured contact lenses and print attacks. In this research, we
present a novel presentation attack using deep learning based synthetic iris
generation. Utilizing the generative capability of deep convolutional
generative adversarial networks and iris quality metrics, we propose a new
framework, named as iDCGAN (iris deep convolutional generative adversarial
network) for generating realistic appearing synthetic iris images. We
demonstrate the effect of these synthetically generated iris images as
presentation attack on iris recognition by using a commercial system. The
state-of-the-art presentation attack detection framework, DESIST is utilized to
analyze if it can discriminate these synthetically generated iris images from
real images. The experimental results illustrate that mitigating the proposed
synthetic presentation attack is of paramount importance.Comment: International Joint Conference on Biometrics 201
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
Face De-Spoofing: Anti-Spoofing via Noise Modeling
Many prior face anti-spoofing works develop discriminative models for
recognizing the subtle differences between live and spoof faces. Those
approaches often regard the image as an indivisible unit, and process it
holistically, without explicit modeling of the spoofing process. In this work,
motivated by the noise modeling and denoising algorithms, we identify a new
problem of face de-spoofing, for the purpose of anti-spoofing: inversely
decomposing a spoof face into a spoof noise and a live face, and then utilizing
the spoof noise for classification. A CNN architecture with proper constraints
and supervisions is proposed to overcome the problem of having no ground truth
for the decomposition. We evaluate the proposed method on multiple face
anti-spoofing databases. The results show promising improvements due to our
spoof noise modeling. Moreover, the estimated spoof noise provides a
visualization which helps to understand the added spoof noise by each spoof
medium.Comment: To appear in ECCV 2018. The first two authors contributed equally to
this wor
Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal
Over the past few years, Presentation Attack Detection (PAD) has become a
fundamental part of facial recognition systems. Although much effort has been
devoted to anti-spoofing research, generalization in real scenarios remains a
challenge. In this paper we present a new open-source evaluation framework to
study the generalization capacity of face PAD methods, coined here as
face-GPAD. This framework facilitates the creation of new protocols focused on
the generalization problem establishing fair procedures of evaluation and
comparison between PAD solutions. We also introduce a large aggregated and
categorized dataset to address the problem of incompatibility between publicly
available datasets. Finally, we propose a benchmark adding two novel evaluation
protocols: one for measuring the effect introduced by the variations in face
resolution, and the second for evaluating the influence of adversarial
operating conditions.Comment: 8 pages, to appear at International Conference on Biometrics (ICB19
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