3,395 research outputs found
Learn Convolutional Neural Network for Face Anti-Spoofing
Though having achieved some progresses, the hand-crafted texture features,
e.g., LBP [23], LBP-TOP [11] are still unable to capture the most
discriminative cues between genuine and fake faces. In this paper, instead of
designing feature by ourselves, we rely on the deep convolutional neural
network (CNN) to learn features of high discriminative ability in a supervised
manner. Combined with some data pre-processing, the face anti-spoofing
performance improves drastically. In the experiments, over 70% relative
decrease of Half Total Error Rate (HTER) is achieved on two challenging
datasets, CASIA [36] and REPLAY-ATTACK [7] compared with the state-of-the-art.
Meanwhile, the experimental results from inter-tests between two datasets
indicates CNN can obtain features with better generalization ability. Moreover,
the nets trained using combined data from two datasets have less biases between
two datasets.Comment: 8 pages, 9 figures, 7 table
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
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
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
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
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 Tree Learning for Zero-shot Face Anti-Spoofing
Face anti-spoofing is designed to keep face recognition systems from
recognizing fake faces as the genuine users. While advanced face anti-spoofing
methods are developed, new types of spoof attacks are also being created and
becoming a threat to all existing systems. We define the detection of unknown
spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous works of ZSFA
only study 1-2 types of spoof attacks, such as print/replay attacks, which
limits the insight of this problem. In this work, we expand the ZSFA problem to
a wide range of 13 types of spoof attacks, including print attack, replay
attack, 3D mask attacks, and so on. A novel Deep Tree Network (DTN) is proposed
to tackle the ZSFA. The tree is learned to partition the spoof samples into
semantic sub-groups in an unsupervised fashion. When a data sample arrives,
being know or unknown attacks, DTN routes it to the most similar spoof cluster,
and make the binary decision. In addition, to enable the study of ZSFA, we
introduce the first face anti-spoofing database that contains diverse types of
spoof attacks. Experiments show that our proposed method achieves the state of
the art on multiple testing protocols of ZSFA.Comment: To appear at CVPR 2019 as an oral presentatio
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
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
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