27 research outputs found
Discriminative multimodal biometric authentication based on quality measures
This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 38, 5, (2005) DOI: 10.1016/j.patcog.2004.11.012A novel score-level fusion strategy based on quality measures for multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted every time an authentication claim is performed based on the estimated quality of the sensed biometric signals at this time. Experimental results combining written signatures and quality-labelled fingerprints are reported. The proposed scheme is shown to outperform significantly the fusion approach without considering quality signals. In particular, a relative improvement of approximately 20% is obtained on the publicly available MCYT bimodal database.This work has been supported by MCYT project TIC2003-08382-C05-01
Extracting and Analysing of Heterogeneous Features for Robust FRS
Collecting, cleaning, combining and analysing of data are in demand in all the fields for acquiring accuracy in their task. In biometrics, this process is done for smart and secured life by means of extracting and analysing data for recognition task. Huge volume and variety of data are effectively extracted and analysed with Matlab2015 to identify the uniqueness of attributes for better accuracy in recognition process. Heterogeneous set of features that are extracted from ORL face dataset are analysed with Nearest Neighbour Rule in order to identify the unique facial features for robust FRS (Face Recognition System)
Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review
This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
Signal-quality awareness has been found to increase recognition rates and to
support decisions in multisensor environments significantly. Nevertheless,
automatic quality assessment is still an open issue. Here, we study the
orientation tensor of fingerprint images to quantify signal impairments, such
as noise, lack of structure, blur, with the help of symmetry descriptors. A
strongly reduced reference is especially favorable in biometrics, but less
information is not sufficient for the approach. This is also supported by
numerous experiments involving a simpler quality estimator, a trained method
(NFIQ), as well as the human perception of fingerprint quality on several
public databases. Furthermore, quality measurements are extensively reused to
adapt fusion parameters in a monomodal multialgorithm fingerprint recognition
environment. In this study, several trained and nontrained score-level fusion
schemes are investigated. A Bayes-based strategy for incorporating experts past
performances and current quality conditions, a novel cascaded scheme for
computational efficiency, besides simple fusion rules, is presented. The
quantitative results favor quality awareness under all aspects, boosting
recognition rates and fusing differently skilled experts efficiently as well as
effectively (by training).Comment: Published at IEEE Transactions on Information Forensics and Securit
Image-based Gender Estimation from Body and Face across Distances
International audienceGender estimation has received increased attention due to its use in a number of pertinent security and commercial applications. Automated gender estimation algorithms are mainly based on extracting representative features from face images. In this work we study gender estimation based on information deduced jointly from face and body, extracted from single-shot images. The approach addresses challenging settings such as low-resolution-images, as well as settings when faces are occluded. Specifically the face-based features include local binary patterns (LBP) and scale-invariant feature transform (SIFT) features, projected into a PCA space. The features of the novel body-based algorithm proposed in this work include continuous shape information extracted from body silhouettes and texture information retained by HOG descriptors. Support Vector Machines (SVMs) are used for classification for body and face features. We conduct experiments on images extracted from video-sequences of the Multi-Biometric Tunnel database, emphasizing on three distance-settings: close, medium and far, ranging from full body exposure (far setting) to head and shoulders exposure (close setting). The experiments suggest that while face-based gender estimation performs best in the close-distance-setting, body-based gender estimation performs best when a large part of the body is visible. Finally we present two score-level-fusion schemes of face and body-based features, outperforming the two individual modalities in most cases
Comparative Study And Analysis Of Quality Based Multibiometric Technique Using Fuzzy Inference System
Biometric is a science and technology of measuring and analyzing biological
data i.e. physical or behavioral traits which is able to uniquely recognize a person
from others. Prior studies of biometric verification systems with fusion of several
biometric sources have been proved to be outstanding over single biometric system.
However, fusion approach without considering the quality information of the data
used will affect the system performance where in some cases the performances of the
fusion system may become worse compared to the performances of either one of the
single systems. In order to overcome this limitation, this study proposes a quality
based fusion scheme by designing a fuzzy inference system (FIS) which is able to
determine the optimum weight to combine the parameter for fusion systems in
changing conditions. For this purpose, fusion systems which combine two modalities
i.e. speech and lip traits are experimented. For speech signal, Mel Frequency
Cepstral Coefficient (MFCC) is used as features while region of interest (ROI) of lip
image is employed as lip features. Support vector machine (SVM) is then executed
as classifier to the verification system. For validation, common fusion schemes i.e.
minimum rule, maximum rule, simple sum rule, weighted sum rule are compared to
the proposed quality based fusion scheme. From the experimental results at 35dB
SNR of speech and 0.8 quality density of lip, the EER percentages for speech, lip,
minimum rule, maximum rule, simple sum rule, weighted sum rule systems are
observed as 5.9210%, 37.2157%, 33.2676%, 31.1364%, 4.0112% and 14.9023%,
respectively compared to the performances of sugeno-type FIS and mamdani-type
FIS i.e. 1.9974% and 1.9745%
Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability
As biometric technology is increasingly deployed, it will be common to
replace parts of operational systems with newer designs. The cost and
inconvenience of reacquiring enrolled users when a new vendor solution is
incorporated makes this approach difficult and many applications will require
to deal with information from different sources regularly. These
interoperability problems can dramatically affect the performance of biometric
systems and thus, they need to be overcome. Here, we describe and evaluate the
ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007
BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion
algorithms when biometric signals were generated using several biometric
devices in mismatched conditions. Quality measures from the raw biometric data
are available to allow system adjustment to changing quality conditions due to
device changes. This system adjustment is referred to as quality-based
conditional processing. The proposed fusion approach is based on linear
logistic regression, in which fused scores tend to be log-likelihood-ratios.
This allows the easy and efficient combination of matching scores from
different devices assuming low dependence among modalities. In our system,
quality information is used to switch between different system modules
depending on the data source (the sensor in our case) and to reject channels
with low quality data during the fusion. We compare our fusion approach to a
set of rule-based fusion schemes over normalized scores. Results show that the
proposed approach outperforms all the rule-based fusion schemes. We also show
that with the quality-based channel rejection scheme, an overall improvement of
25% in the equal error rate is obtained.Comment: Published at IEEE Transactions on Systems, Man, and Cybernetics -
Part A: Systems and Human