11,325 research outputs found
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
Genetic Programming for Multibiometrics
Biometric systems suffer from some drawbacks: a biometric system can provide
in general good performances except with some individuals as its performance
depends highly on the quality of the capture. One solution to solve some of
these problems is to use multibiometrics where different biometric systems are
combined together (multiple captures of the same biometric modality, multiple
feature extraction algorithms, multiple biometric modalities...). In this
paper, we are interested in score level fusion functions application (i.e., we
use a multibiometric authentication scheme which accept or deny the claimant
for using an application). In the state of the art, the weighted sum of scores
(which is a linear classifier) and the use of an SVM (which is a non linear
classifier) provided by different biometric systems provide one of the best
performances. We present a new method based on the use of genetic programming
giving similar or better performances (depending on the complexity of the
database). We derive a score fusion function by assembling some classical
primitives functions (+, *, -, ...). We have validated the proposed method on
three significant biometric benchmark datasets from the state of the art
Fast computation of the performance evaluation of biometric systems: application to multibiometric
The performance evaluation of biometric systems is a crucial step when
designing and evaluating such systems. The evaluation process uses the Equal
Error Rate (EER) metric proposed by the International Organization for
Standardization (ISO/IEC). The EER metric is a powerful metric which allows
easily comparing and evaluating biometric systems. However, the computation
time of the EER is, most of the time, very intensive. In this paper, we propose
a fast method which computes an approximated value of the EER. We illustrate
the benefit of the proposed method on two applications: the computing of non
parametric confidence intervals and the use of genetic algorithms to compute
the parameters of fusion functions. Experimental results show the superiority
of the proposed EER approximation method in term of computing time, and the
interest of its use to reduce the learning of parameters with genetic
algorithms. The proposed method opens new perspectives for the development of
secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012
Relations among Security Metrics for Template Protection Algorithms
Many biometric template protection algorithms have been proposed mainly in
two approaches: biometric feature transformation and biometric cryptosystem.
Security evaluation of the proposed algorithms are often conducted in various
inconsistent manner. Thus, it is strongly demanded to establish the common
evaluation metrics for easier comparison among many algorithms. Simoens et al.
and Nagar et al. proposed good metrics covering nearly all aspect of
requirements expected for biometric template protection algorithms. One
drawback of the two papers is that they are biased to experimental evaluation
of security of biometric template protection algorithms. Therefore, it was
still difficult mainly for algorithms in biometric cryptosystem to prove their
security according to the proposed metrics. This paper will give a formal
definitions for security metrics proposed by Simoens et al. and Nagar et al. so
that it can be used for the evaluation of both of the two approaches. Further,
this paper will discuss the relations among several notions of security
metrics
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
Recommended from our members
Privacy-Preserving iVector-Based Speaker Verification
This paper introduces an efficient algorithm to develop a privacy-preserving voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice print instead of alphanumeric passwords. Since a voice print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this paper proposes a novel technique based on randomization to carry out voice authentication, which allows the user to enrol and verify their voice in the randomized domain. To achieve this, the iVector-based voice verification technique has been redesigned to work on the randomized domain. The proposed algorithm is validated using a well-known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomization operations
- …