A novel two-stage protection scheme for automatic iris recognition systems against masquerade attacks carried out with synthetically reconstructed iris images is presented. The method uses different characteristics of real iris images to differentiate them from the synthetic ones, thereby addressing important security flaws detected in state-of-the-art commercial systems. Experiments are carried out on the publicly available Biosecure Database and demonstrate the efficacy of the proposed security enhancing approach.
One of the emerging applications of the millimeter-wave imaging technology is its use in biometric recognition.
This is mainly due to some properties of the millimeter-waves such as their ability to penetrate through clothing
and other occlusions, their low obtrusiveness when collecting the image and the fact that they are harmless to
health. In this work we first describe the generation of a database comprising 1200 synthetic images at 94 GHz
obtained from the body of 50 people. Then we extract a small set of distance-based features from each image
and select the best feature subsets for person recognition using the SFFS feature selection algorithm. Finally
these features are used in body geometry authentication obtaining promising results.
This paper reports for the first time experiments on the fusion of footsteps and face on an unsupervised and
not controlled environment for person authentication. Footstep recognition is a relatively new biometric based
on signals extracted from people walking over floor sensors. The idea of the fusion between footsteps and face
starts from the premise that in an area where footstep sensors are installed it is very simple to place a camera to
capture also the face of the person that walks over the sensors. This setup may find application in scenarios like
ambient assisted living, smart homes, eldercare, or security access. The paper reports a comparative assessment
of both biometrics using the same database and experimental protocols. In the experimental work we consider
two different applications: smart homes (small group of users with a large set of training data) and security access
(larger group of users with a small set of training data) obtaining results of 0.9% and 5.8% EER respectively for
the fusion of both modalities. This is a significant performance improvement compared with the results obtained
by the individual systems.
A new method to generate synthetic online signatures is presented. The algorithm uses a parametrical model to
generate the synthetic Discrete Fourier Transform (DFT) of the trajectory signals, which are then refined in the
time domain and completed with a synthetic pressure function. Multiple samples of each signature are created
so that synthetic databases may be produced. Quantitative and qualitative results are reported, showing that,
in addition to presenting a very realistic appearance, the synthetically generated signatures have very similar
characteristics to those that enable the recognition of real signatures.
Multimodal biometric systems allow to overcome some of the problems presented in unimodal systems, such as non-universality, lack of distinctiveness of the unimodal trait, noise in the acquired data, etc. Integration at the matching score level is the most common approach used due to the ease in combining the scores generated by different unimodal systems. Unfortunately, scores usually lie in application-dependent domains. In this work, we use linear logistic regression fusion, in which fused scores tend to be calibrated log-likelihood-ratios and thus, independent of the application. We use for our experiments the development set of scores of the DS2 Evaluation (Access Control Scenario) of the BioSecure Multimodal Evaluation Campaign, whose objective is to compare the performance of fusion algorithms when query biometric signals are originated from heterogeneous biometric devices. We compare a fusion scheme that uses linear logistic regression with a set of simple fusion rules. It is observed that the proposed fusion scheme outperforms all the simple fusion rules, with the additional advantage of the application-independent nature of the resulting fused scores.
Although many image quality measures have been proposed
for fingerprints, few works have taken into account how differences
among capture devices impact the image quality. Several
representative measures for assessing the quality of fingerprint images
are compared using an optical and a capacitive sensor. We
implement and test a representative set of measures that rely on
different fingerprint image features for quality assessment. The capability
to discriminate between images of different quality and the
relationship with the verification performance are studied. For our
verification experiments, we use minutiae- and ridge-based matchers,
which are the most common approaches for fingerprint recognition.
We report differences depending on the sensor, and interesting
relationships between sensor technology and features used for
quality assessment are also pointed out.
An automatic classification scheme of on-line handwritten signatures is presented. A Multilayer Perceptron
(MLP) with a hidden layer is used as classifier, and two different signature classes are considered, namely:
legible and non-legible name. Signatures are represented considering different feature subsets obtained from
global information. Mahalanobis distance is used to rank the parameters and feature selection is then applied
based on the top ranked features. Experimental results are given on the MCYT signature database comprising
330 signers. It is shown experimentally that automatic on-line signature classification based on the name legibility
Based on recent works showing the feasibility of key generation using biometrics, we study the application of handwritten signature to cryptography. Our signature-based key generation scheme implements the cryptographic construction named fuzzy vault. The use of distinctive signature features suited for the fuzzy vault is discussed and evaluated. Experimental results are reported, including error rates to unlock the secret data by using both random and skilled forgeries from the MCYT database.
A novel kernel-based fusion strategy is presented. It is based on SVM classifiers, trade-off coefficients introduced in the standard SVM training and testing procedures, and quality measures of the input biometric signals. Experimental results on a prototype application based on voice and fingerprint traits are reported. The benefits
of using the two modalities as compared to only using one of them are revealed. This is achieved by using a novel experimental procedure in which multi-modal verification performance tests are compared with multi-probe tests of the individual subsystems. Appropriate selection of the parameters of the proposed quality-based scheme leads to a quality-based fusion scheme outperforming the raw fusion strategy without considering quality signals. In particular, a relative improvement of 18% is obtained for small SVM training set size by using only fingerprint quality labels.