1,001 research outputs found
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
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
Audio-replay attack detection countermeasures
This paper presents the Speech Technology Center (STC) replay attack
detection systems proposed for Automatic Speaker Verification Spoofing and
Countermeasures Challenge 2017. In this study we focused on comparison of
different spoofing detection approaches. These were GMM based methods, high
level features extraction with simple classifier and deep learning frameworks.
Experiments performed on the development and evaluation parts of the challenge
dataset demonstrated stable efficiency of deep learning approaches in case of
changing acoustic conditions. At the same time SVM classifier with high level
features provided a substantial input in the efficiency of the resulting STC
systems according to the fusion systems results.Comment: 11 pages, 3 figures, accepted for Specom 201
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
Deep Transfer Across Domains for Face Anti-spoofing
A practical face recognition system demands not only high recognition
performance, but also the capability of detecting spoofing attacks. While
emerging approaches of face anti-spoofing have been proposed in recent years,
most of them do not generalize well to new database. The generalization ability
of face anti-spoofing needs to be significantly improved before they can be
adopted by practical application systems. The main reason for the poor
generalization of current approaches is the variety of materials among the
spoofing devices. As the attacks are produced by putting a spoofing display
(e.t., paper, electronic screen, forged mask) in front of a camera, the variety
of spoofing materials can make the spoofing attacks quite different.
Furthermore, the background/lighting condition of a new environment can make
both the real accesses and spoofing attacks different. Another reason for the
poor generalization is that limited labeled data is available for training in
face anti-spoofing. In this paper, we focus on improving the generalization
ability across different kinds of datasets. We propose a CNN framework using
sparsely labeled data from the target domain to learn features that are
invariant across domains for face anti-spoofing. Experiments on public-domain
face spoofing databases show that the proposed method significantly improve the
cross-dataset testing performance only with a small number of labeled samples
from the target domain.Comment: 8 pages; 3 figures; 2 table
Face Presentation Attack Detection in Learned Color-liked Space
Face presentation attack detection (PAD) has become a thorny problem for
biometric systems and numerous countermeasures have been proposed to address
it. However, majority of them directly extract feature descriptors and
distinguish fake faces from the real ones in existing color spaces (e.g. RGB,
HSV and YCbCr). Unfortunately, it is unknown for us which color space is the
best or how to combine different spaces together. To make matters worse, the
real and fake faces are overlapped in existing color spaces. So, in this paper,
a learned distinguishable color-liked space is generated to deal with the
problem of face PAD. More specifically, we present an end-to-end deep learning
network that can map existing color spaces to a new learned color-liked space.
Inspired by the generator of generative adversarial network (GAN), the proposed
network consists of a space generator and a feature extractor. When training
the color-liked space, a new triplet combination mechanism of points-to-center
is explored to maximize interclass distance and minimize intraclass distance,
and also keep a safe margin between the real and presented fake faces.
Extensive experiments on two standard face PAD databases, i.e., Relay-Attack
and OULU-NPU, indicate that our proposed color-liked space analysis based
countermeasure significantly outperforms the state-of-the-art methods and show
excellent generalization capability
Using Deep Learning for Detecting Spoofing Attacks on Speech Signals
It is well known that speaker verification systems are subject to spoofing
attacks. The Automatic Speaker Verification Spoofing and Countermeasures
Challenge -- ASVSpoof2015 -- provides a standard spoofing database, containing
attacks based on synthetic speech, along with a protocol for experiments. This
paper describes CPqD's systems submitted to the ASVSpoof2015 Challenge, based
on deep neural networks, working both as a classifier and as a feature
extraction module for a GMM and a SVM classifier. Results show the validity of
this approach, achieving less than 0.5\% EER for known attacks
Improving Face Anti-Spoofing by 3D Virtual Synthesis
Face anti-spoofing is crucial for the security of face recognition systems.
Learning based methods especially deep learning based methods need large-scale
training samples to reduce overfitting. However, acquiring spoof data is very
expensive since the live faces should be re-printed and re-captured in many
views. In this paper, we present a method to synthesize virtual spoof data in
3D space to alleviate this problem. Specifically, we consider a printed photo
as a flat surface and mesh it into a 3D object, which is then randomly bent and
rotated in 3D space. Afterward, the transformed 3D photo is rendered through
perspective projection as a virtual sample. The synthetic virtual samples can
significantly boost the anti-spoofing performance when combined with a proposed
data balancing strategy. Our promising results open up new possibilities for
advancing face anti-spoofing using cheap and large-scale synthetic data.Comment: Accepted to ICB 201
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Face Anti-spoofing gains increased attentions recently in both academic and
industrial fields. With the emergence of various CNN based solutions, the
multi-modal(RGB, depth and IR) methods based CNN showed better performance than
single modal classifiers. However, there is a need for improving the
performance and reducing the complexity. Therefore, an extreme light network
architecture(FeatherNet A/B) is proposed with a streaming module which fixes
the weakness of Global Average Pooling and uses less parameters. Our single
FeatherNet trained by depth image only, provides a higher baseline with 0.00168
ACER, 0.35M parameters and 83M FLOPS. Furthermore, a novel fusion procedure
with ``ensemble + cascade'' structure is presented to satisfy the performance
preferred use cases. Meanwhile, the MMFD dataset is collected to provide more
attacks and diversity to gain better generalization. We use the fusion method
in the Face Anti-spoofing Attack Detection Challenge@CVPR2019 and got the
result of 0.0013(ACER), 0.999(TPR@FPR=10e-2), 0.998(TPR@FPR=10e-3) and
0.9814(TPR@FPR=10e-4).Comment: 10 pages;6 figure
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
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