Beside the optimization of biometric error rates the overall security system performance in respect to intentional security
attacks plays an important role for biometric enabled authentication schemes. As traditionally most user authentication
schemes are knowledge and/or possession based, firstly in this paper we present a methodology for a security analysis of
Internet-based biometric authentication systems by enhancing known methodologies such as the CERT attack-taxonomy
with a more detailed view on the OSI-Model. Secondly as proof of concept, the guidelines extracted from this
methodology are strictly applied to an open source Internet-based biometric authentication system (BioWebAuth). As
case studies, two exemplary attacks, based on the found security leaks, are investigated and the attack performance is
presented to show that during the biometric authentication schemes beside biometric error performance tuning also
security issues need to be addressed. Finally, some design recommendations are given in order to ensure a minimum
security level.
We describe a framework aimed at performing facebased
biometric user authentication for Web resources through
client–server secured sessions. A novel front end for face video sequences
processing is developed in which face detection and shot
selection are performed at the client side while statistical multishot
pose-corrected face verification is performed at the server side. We
explain all the image processing steps, from acquisition through decision,
paying special attention to a PDM-based pose correction
subsystem and a GMM-based sequence decision test. The pose
correction relies on projecting a face-shape mesh onto the set of
PDM eigenvectors and back-projecting it after changing the coefficients
associated with pose variation. The aligned texture features
compose the observation vectors ready to be plugged into a GMMbased
likelihood ratio for statistical decision. The pose correction
strategy has been previously validated over the XM2VTS and CMUPIE
data sets, the GMM-based video sequence decision test has
been compared to other video-based systems using the BANCA
data set, and the complete proposed system has been tested on a
new video data set from the European Network of Excellence
BIOSECURE.
We aim at the discrimination of varieties within a single plant species (Vitis vinifera) by means of airborne
hyperspectral imagery collected using a CASI-2 sensor and supervised classification, both under constant and
varying within-scene illumination conditions. Varying illumination due to atmospheric conditions (such as clouds)
and shadows cause different pixels belonging to the same class to present different spectral vectors, increasing
the within class variability and hindering classification. This is specially serious in precision applications such
as variety discrimination in precision agriculture, which depends on subtle spectral differences. In this study,
we use machine learning techniques for supervised classification, and we also analyze the variability within and
among plots and within and among sites, in order to address the generalizability of the results.
In this article we present an hybrid SOM+PCA approach for face identification that is based on separating shape and texture information. Shape will be processed by a modified Hausdorff distance SOM and texture processing relies on a modular PCA. In most successfully view-based recognition systems, shape and texture are jointly used to statistically model a linear or piecewise linear subspace that optimally explains the face space for a specific database. Our work is aimed to separate the influence that variance in face shape stamps on the set of eigenfaces in the classical PCA decomposition. In this sense we search for a more efficiently coded face-vector for identification. The ultimate goal consist of finding a non-linear transformation invariant to gesture changes and, in a larger extent, to pose changes. The first part of this paper is dedicated to the shape processor of the system, that is based on a novel shape-based Self Organizing Map, and the second part deals with the subspace PCA decomposition that relies on the SOM clustering. Results are reported by comparing face identification between PCA and the SOM-PCA approach.
In this paper we introduce an adaptive image segmentation neural network based on a Gaussian mixture classifier that is able to accommodate unlabeled data in the training process to improve generalization when labeled data is insufficient. The classifier is trained by maximizing the joint-likelihood of features and labels over all the data set (labeled and unlabeled). The classifier builds grey- level images with estimation of class-posteriors (as many images as classes) that feed the segmentation algorithm. The paper is focused on the adaptive classification part of the algorithm. The classification tests are performed over Landsat TM mini-scenes. We assess the efficiency of the adaptive classifier depending on the model complexity and the proportion of labeled/unlabeled data.
In this paper we introduce a new technique to build a Gaussian mixture classifier. It is based on the selection of the number and location of nodes dedicated to every class by means of discriminative rules. This feature allows us to make a fair comparison with MLP networks for terrain classification in remote sense applications, a field where non-parametric techniques usually outperform classical ML Gaussian classifiers. The main characteristic of the architecture proposed is the ability to select the proper number of Gaussian nodes per class attending to discriminative rules. The growth control is imposed by the use of an information theoretic criterion that prevents the network from becoming extremely complex, thus loosing generalization capabilities. After the growing phase is finished, a mutual information criterion is maximized to bias the parameters to a more discriminative configuration. We report a comparative study on terrain classification over a Landsat-TM image, using this technique and MLP classifiers with one hidden layer.
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