509 research outputs found
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
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
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
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
3D Face Mask Presentation Attack Detection Based on Intrinsic Image Analysis
Face presentation attacks have become a major threat to face recognition
systems and many countermeasures have been proposed in the past decade.
However, most of them are devoted to 2D face presentation attacks, rather than
3D face masks. Unlike the real face, the 3D face mask is usually made of resin
materials and has a smooth surface, resulting in reflectance differences. So,
we propose a novel detection method for 3D face mask presentation attack by
modeling reflectance differences based on intrinsic image analysis. In the
proposed method, the face image is first processed with intrinsic image
decomposition to compute its reflectance image. Then, the intensity
distribution histograms are extracted from three orthogonal planes to represent
the intensity differences of reflectance images between the real face and 3D
face mask. After that, the 1D convolutional network is further used to capture
the information for describing different materials or surfaces react
differently to changes in illumination. Extensive experiments on the 3DMAD
database demonstrate the effectiveness of our proposed method in distinguishing
a face mask from the real one and show that the detection performance
outperforms other state-of-the-art methods
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Face anti-spoofing (a.k.a presentation attack detection) has drawn growing
attention due to the high-security demand in face authentication systems.
Existing CNN-based approaches usually well recognize the spoofing faces when
training and testing spoofing samples display similar patterns, but their
performance would drop drastically on testing spoofing faces of unseen scenes.
In this paper, we try to boost the generalizability and applicability of these
methods by designing a CNN model with two major novelties. First, we propose a
simple yet effective Total Pairwise Confusion (TPC) loss for CNN training,
which enhances the generalizability of the learned Presentation Attack (PA)
representations. Secondly, we incorporate a Fast Domain Adaptation (FDA)
component into the CNN model to alleviate negative effects brought by domain
changes. Besides, our proposed model, which is named Generalizable Face
Authentication CNN (GFA-CNN), works in a multi-task manner, performing face
anti-spoofing and face recognition simultaneously. Experimental results show
that GFA-CNN outperforms previous face anti-spoofing approaches and also well
preserves the identity information of input face images.Comment: 8 pages; 8 figures; 4 table
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
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
Learning Generalized Spoof Cues for Face Anti-spoofing
Many existing face anti-spoofing (FAS) methods focus on modeling the decision
boundaries for some predefined spoof types. However, the diversity of the spoof
samples including the unknown ones hinders the effective decision boundary
modeling and leads to weak generalization capability. In this paper, we
reformulate FAS in an anomaly detection perspective and propose a
residual-learning framework to learn the discriminative live-spoof differences
which are defined as the spoof cues. The proposed framework consists of a spoof
cue generator and an auxiliary classifier. The generator minimizes the spoof
cues of live samples while imposes no explicit constraint on those of spoof
samples to generalize well to unseen attacks. In this way, anomaly detection is
implicitly used to guide spoof cue generation, leading to discriminative
feature learning. The auxiliary classifier serves as a spoof cue amplifier and
makes the spoof cues more discriminative. We conduct extensive experiments and
the experimental results show the proposed method consistently outperforms the
state-of-the-art methods. The code will be publicly available at
https://github.com/vis-var/lgsc-for-fas.Comment: 16 page
Deep Anomaly Detection for Generalized Face Anti-Spoofing
Face recognition has achieved unprecedented results, surpassing human
capabilities in certain scenarios. However, these automatic solutions are not
ready for production because they can be easily fooled by simple identity
impersonation attacks. And although much effort has been devoted to develop
face anti-spoofing models, their generalization capacity still remains a
challenge in real scenarios. In this paper, we introduce a novel approach that
reformulates the Generalized Presentation Attack Detection (GPAD) problem from
an anomaly detection perspective. Technically, a deep metric learning model is
proposed, where a triplet focal loss is used as a regularization for a novel
loss coined "metric-softmax", which is in charge of guiding the learning
process towards more discriminative feature representations in an embedding
space. Finally, we demonstrate the benefits of our deep anomaly detection
architecture, by introducing a few-shot a posteriori probability estimation
that does not need any classifier to be trained on the learned features. We
conduct extensive experiments using the GRAD-GPAD framework that provides the
largest aggregated dataset for face GPAD. Results confirm that our approach is
able to outperform all the state-of-the-art methods by a considerable margin.Comment: To appear at CVPR19 (workshop
- …