Juan Antonio Ortega

BiosecurID: a multimodal biometric database

A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol. The database includes eight unimodal biometric traits, namely: speech, iris, face (still images, videos of talking faces), handwritten signature and handwritten text (on-line dynamic signals, off-line scanned images), fingerprints (acquired with two different sensors), hand (palmprint, contour-geometry) and keystroking. The database comprises 400 subjects and presents features such as: realistic acquisition scenario, balanced gender and population distributions, availability of information about particular demographic groups (age, gender, handedness), acquisition of replay attacks for speech and keystroking, skilled forgeries for signatures, and compatibility with other existing databases. All these characteristics make it very useful in research and development of unimodal and multimodal biometric systems.

Ameva: An autonomous discretization algorithm

This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a potentially minimal number of discrete intervals. Its most important advantage, in contrast with several existing discretization algorithms, is that it does not need the user to indicate the number of intervals. We have compared Ameva with one of the most relevant discretization algorithms, CAIM. Tests performed comparing these two algorithms show that discrete attributes generated by the Ameva algorithm always have the lowest number of intervals, and even if the number of classes is high, the same computational complexity is maintained. A comparison between the Ameva and the genetic algorithm approaches has been also realized and there are very small differences between these iterative and combinatorial approaches, except when considering the execution time.

Online motion recognition using an accelerometer in a mobile device

Highlights? A statistical study and machine learning algorithms are used to interpret data. ? Different human activities can be effectively recognized. ? An approach to recognize motions either offline or online based on data in a mobile device. This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.

Multi-Classification by Using Tri-Class SVM

The standard form for dealing with multi-class classification problems when bi-classifiers are used is to consider a two-phase (decomposition, reconstruction) training scheme. The most popular decomposition procedures are pairwise coupling (one versus one, 1-v-1), which considers a learning machine for each Pair of classes, and the one-versus-all scheme (one versus all, 1-v-r), which takes into consideration each class versus the remaining classes. In this article a 1-v-1 tri-class Support Vector Machine (SVM) is presented. The expansion of the architecture of this machine into three categories specifically addresses the decomposition problem of how to prevent the loss of information which occurs in the usual 1-v-1 training procedure. The proposed machine, by means of a third class, allows all the information to be incorporated into the remaining training patterns when a multi-class problem is considered in the form of a 1-v-1 decomposition. Three general structures are presented where each improves some features from the precedent structure. In order to deal with multi-classification problems, it is demonstrated that the final machine proposed allows ordinal regression as a form of decomposition procedure. Examples and experimental results are presented which illustrate the performance of the new tri-class SV machine.

A Note on the Bias in SVMs for Multiclassification

During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance.

An efficient face verification method in a transformed domain

In this paper we propose a low-complexity face verification system based on the Walsh-Hadamard transform. This system can be easily implemented on a fixed point processor and offers a good compromise between computational burden and verification rates. We have evaluated that with 36 integer coefficients per face we achieve better Detection Cost Function (6.05%) than the classical eigenfaces approach (minimum value 6.99% with 126 coefficients), with a smaller number of coefficients.

A study on saving energy in artificial lighting by making smart use of wireless sensor networks and actuators

This article is focused on adapting lighting conditions to user lighting preferences. A theoretical analysis of lighting conditions is carried out, and a case study is shown by means of the setup of an experimental environment and an empirical analysis of lighting conditions. Finally, a methodology for saving energy, which adjusts luminance to user preferences, is presented, and a study of the consumption results is given.

Support vector machines for interval discriminant analysis

The use of data represented by intervals can be caused by imprecision in the input information, incompleteness in patterns, discretization procedures, prior knowledge insertion or speed-up learning. All the existing support vector machine (SVM) approaches working on interval data use local kernels based on a certain distance between intervals, either by combining the interval distance with a kernel or by explicitly defining an interval kernel. This article introduces a new procedure for the linearly separable case, derived from convex optimization theory, inserting information directly into the standard SVM in the form of intervals, without taking any particular distance into consideration.

A model for the qualitative description of images based on visual and spatial features

An approach that provides a qualitative description of any image is presented in this paper. The main visual features (shape and colour) and the main spatial features (fixed orientation, relative orientation and topology) of each object within the image are described. This approach has been tested in two real scenarios that involve agents and human interaction: (i) images captured by the webcam of a mobile robot while it navigates, and (ii) images of tile compositions captured by an industrial camera used to select tile pieces to be used in assembling tile mosaics. In both scenarios, promising results have been obtained.

Smart scheduling for saving energy in grid computing

Highlights? Within IT, grids and data centres represent the hungriest consumers of energy. ? Energy saving involves two direct benefits: sustainability and cost reduction. ? We model seven energy policies for Grid computing environments. ? This paper proves proved that 40policy is applied. ? Simulation software is available and results for real scenarios can be obtained. Energy saving involves two direct benefits: sustainability and cost reduction. Within the field of Information Technology, clusters, grids and data centres represent the hungriest consumers of energy and therefore energy (saving) policies for these infrastructures should be applied in order to maximize their resources. It is proved in this paper that approximately 40% of energy can be saved in a data centre if an adequate policy is applied. Furthermore, a software tool is presented where simulations can be run and results for real scenarios can be obtained.