671 research outputs found
Fingerprint liveness detection using local quality features
Fingerprint-based recognition has been widely deployed in various
applications. However, current recognition systems are vulnerable to spoofing
attacks which make use of an artificial replica of a fingerprint to deceive the
sensors. In such scenarios, fingerprint liveness detection ensures the actual
presence of a real legitimate fingerprint in contrast to a fake
self-manufactured synthetic sample. In this paper, we propose a static
software-based approach using quality features to detect the liveness in a
fingerprint. We have extracted features from a single fingerprint image to
overcome the issues faced in dynamic software-based approaches which require
longer computational time and user cooperation. The proposed system extracts 8
sensor independent quality features on a local level containing minute details
of the ridge-valley structure of real and fake fingerprints. These local
quality features constitutes a 13-dimensional feature vector. The system is
tested on a publically available dataset of LivDet 2009 competition. The
experimental results exhibit supremacy of the proposed method over current
state-of-the-art approaches providing least average classification error of
5.3% for LivDet 2009. Additionally, effectiveness of the best performing
features over LivDet 2009 is evaluated on the latest LivDet 2015 dataset which
contain fingerprints fabricated using unknown spoof materials. An average
classification error rate of 4.22% is achieved in comparison with 4.49%
obtained by the LivDet 2015 winner. Further, the proposed system utilizes a
single fingerprint image, which results in faster implications and makes it
more user-friendly.Comment: 21 pages, 11 figures, 7 Table
RaspiReader: Open Source Fingerprint Reader
We open source an easy to assemble, spoof resistant, high resolution, optical
fingerprint reader, called RaspiReader, using ubiquitous components. By using
our open source STL files and software, RaspiReader can be built in under one
hour for only US $175. As such, RaspiReader provides the fingerprint research
community a seamless and simple method for quickly prototyping new ideas
involving fingerprint reader hardware. In particular, we posit that this open
source fingerprint reader will facilitate the exploration of novel fingerprint
spoof detection techniques involving both hardware and software. We demonstrate
one such spoof detection technique by specially customizing RaspiReader with
two cameras for fingerprint image acquisition. One camera provides high
contrast, frustrated total internal reflection (FTIR) fingerprint images, and
the other outputs direct images of the finger in contact with the platen. Using
both of these image streams, we extract complementary information which, when
fused together and used for spoof detection, results in marked performance
improvement over previous methods relying only on grayscale FTIR images
provided by COTS optical readers. Finally, fingerprint matching experiments
between images acquired from the FTIR output of RaspiReader and images acquired
from a COTS reader verify the interoperability of the RaspiReader with existing
COTS optical readers.Comment: substantial text overlap with arXiv:1708.0788
RaspiReader: An Open Source Fingerprint Reader Facilitating Spoof Detection
We present the design and prototype of an open source, optical fingerprint
reader, called RaspiReader, using ubiquitous components. RaspiReader, a
low-cost and easy to assemble reader, provides the fingerprint research
community a seamless and simple method for gaining more control over the
sensing component of fingerprint recognition systems. In particular, we posit
that this versatile fingerprint reader will encourage researchers to explore
novel spoof detection methods that integrate both hardware and software.
RaspiReader's hardware is customized with two cameras for fingerprint
acquisition with one camera providing high contrast, frustrated total internal
reflection (FTIR) images, and the other camera outputting direct images. Using
both of these image streams, we extract complementary information which, when
fused together, results in highly discriminative features for fingerprint spoof
(presentation attack) detection. Our experimental results demonstrate a marked
improvement over previous spoof detection methods which rely only on FTIR
images provided by COTS optical readers. Finally, fingerprint matching
experiments between images acquired from the FTIR output of the RaspiReader and
images acquired from a COTS fingerprint reader verify the interoperability of
the RaspiReader with existing COTS optical readers.Comment: 14 pages, 14 figure
Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns
With the growing use of biometric authentication systems in the past years,
spoof fingerprint detection has become increasingly important. In this work, we
implement and evaluate two different feature extraction techniques for
software-based fingerprint liveness detection: Convolutional Networks with
random weights and Local Binary Patterns. Both techniques were used in
conjunction with a Support Vector Machine (SVM) classifier. Dataset
Augmentation was used to increase classifier's performance and a variety of
preprocessing operations were tested, such as frequency filtering, contrast
equalization, and region of interest filtering. The experiments were made on
the datasets used in The Liveness Detection Competition of years 2009, 2011 and
2013, which comprise almost 50,000 real and fake fingerprints' images. Our best
method achieves an overall rate of 95.2% of correctly classified samples - an
improvement of 35% in test error when compared with the best previously
published results.Comment: arXiv admin note: text overlap with arXiv:1301.3557 by other author
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
Fingerprint Spoof Buster
The primary purpose of a fingerprint recognition system is to ensure a
reliable and accurate user authentication, but the security of the recognition
system itself can be jeopardized by spoof attacks. This study addresses the
problem of developing accurate, generalizable, and efficient algorithms for
detecting fingerprint spoof attacks. Specifically, we propose a deep
convolutional neural network based approach utilizing local patches centered
and aligned using fingerprint minutiae. Experimental results on three
public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed
approach provides state-of-the-art accuracies in fingerprint spoof detection
for intra-sensor, cross-material, cross-sensor, as well as cross-dataset
testing scenarios. For example, in LivDet 2015, the proposed approach achieves
99.03% average accuracy over all sensors compared to 95.51% achieved by the
LivDet 2015 competition winners. Additionally, two new fingerprint presentation
attack datasets containing more than 20,000 images, using two different
fingerprint readers, and over 12 different spoof fabrication materials are
collected. We also present a graphical user interface, called Fingerprint Spoof
Buster, that allows the operator to visually examine the local regions of the
fingerprint highlighted as live or spoof, instead of relying on only a single
score as output by the traditional approaches.Comment: 13 page
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
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
Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models
Fingerprint recognition is widely used for verification and identification in
many commercial, governmental and forensic applications. The orientation field
(OF) plays an important role at various processing stages in fingerprint
recognition systems. OFs are used for image enhancement, fingerprint alignment,
for fingerprint liveness detection, fingerprint alteration detection and
fingerprint matching. In this paper, a novel approach is presented to globally
model an OF combined with locally adaptive methods. We show that this model
adapts perfectly to the 'true OF' in the limit. This perfect OF is described by
a small number of parameters with straightforward geometric interpretation.
Applications are manifold: Quick expert marking of very poor quality (for
instance latent) OFs, high fidelity low parameter OF compression and a direct
road to ground truth OFs markings for large databases, say. In this
contribution we describe an algorithm to perfectly estimate OF parameters
automatically or semi-automatically, depending on image quality, and we
establish the main underlying claim of high fidelity low parameter OF
compression
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
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