Hybrid Template Update System for Unimodal Biometric Systems

Semi-supervised template update systems allow to automatically take into account the intra-class variability of the biometric data over time. Such systems can be inefficient by including too many impostor's samples or skipping too many genuine's samples. In the first case, the biometric reference drifts from the real biometric data and attracts more often impostors. In the second case, the biometric reference does not evolve quickly enough and also progressively drifts from the real biometric data. We propose a hybrid system using several biometric sub-references in order to increase per- formance of self-update systems by reducing the previously cited errors. The proposition is validated for a keystroke- dynamics authentication system (this modality suffers of high variability over time) on two consequent datasets from the state of the art.Comment: IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2012), Washington, District of Columbia, USA : France (2012

Performance Evaluation of Biometric Template Update

Template update allows to modify the biometric reference of a user while he uses the biometric system. With such kind of mechanism we expect the biometric system uses always an up to date representation of the user, by capturing his intra-class (temporary or permanent) variability. Although several studies exist in the literature, there is no commonly adopted evaluation scheme. This does not ease the comparison of the different systems of the literature. In this paper, we show that using different evaluation procedures can lead in different, and contradictory, interpretations of the results. We use a keystroke dynamics (which is a modality suffering of template ageing quickly) template update system on a dataset consisting of height different sessions to illustrate this point. Even if we do not answer to this problematic, it shows that it is necessary to normalize the template update evaluation procedures.Comment: International Biometric Performance Testing Conference 2012, Gaithersburg, MD, USA : United States (2012

Computation of forces exerted on a microparticle by a laser beam

A mathematical description of the electromagnetic fields of non-paraxial laser beams is derived and used to calculate the trapping forces on spherical particles. The fields are exact solutions to the wave equation. A set of closed-form expressions for the scalar field of such a beam is presented first. The solution for the order 00 is equivalent to the wave of a combined complex-point source and sink. In the far field the two lowest order solutions, 00 and 01, closely match the energy density produced by a high-numerical aperture lens illuminated by a paraxial Gaussian beam. At the large beam waist limit these two solutions reduce to the paraxial beam form. However, it is found that only the 01 order solution is physically realizable, since the total energy flux through the transverse section of the 00 order beam is infinite. The scalar solutions of arbitrary order are then used to derive solutions to the vector wave equation. Next, the electric and magnetic fields that closely fit the far-field boundary conditions for a focusing lens are constructed from the solutions for the orders 00 and 01. These fields are in general elliptically polarized at the beam waist. However at the large beam waist (paraxial) limit and in the far field limit the fields become linearly polarized. The electromagnetic field due to order 01 is used to calculate the Maxwell stress tensor, and hence the trapping forces exerted on a dielectric microsphere in a single beam laser tweezers setup. It is demonstrated that the electromagnetic theory model based on the 5th order Gaussian beam approximation due to Barton is accurate for almost paraxial beams (numerical aperture NA<0.25), when compared to the model derived here. However, for strongly focused beams (NA>l) the 5th order approximation breaks down. Trapping forces on water droplets suspended in air and on polystyrene spheres suspended in water, exerted by a Gaussian laser beam focused with lenses of various numerical apertures are calculated. It is established that a model accurate for a strongly focused beam is vital, since in order to trap a particle effectively a focusing lens with NA>1 is required

Neural Correlates of Learning in the Prefrontal Cortex of the Monkey: A Predictive Model

The principles underlying the organization and operation of the prefrontal cortex have been addressed by neural network modeling. The involvement of the prefrontal cortex in the temporal organization of behavior can be defined by processing units that switch between two stable states of activity (bistable behavior) in response to synaptic inputs. Long-term representation of programs requiring short-term memory can result from activity-dependent modifications of the synaptic transmission controlling the bistable behavior. After learning, the sustained activity of a given neuron represents the selective memorization of a past event the selective anticipation of a future event, and the predictability of reinforcement A simulated neural network illustrates the abilities of the model (1) to learn, via a natural step-by-step training protocol, the paradigmatic task (delayed response) used for testing prefrontal neurons in primates, (2) to display the same categories of neuronal activities, and (3) to predict how they change during learning. In agreement with experimental data, two main types of activity contribute to the adaptive properties of the network. The first is transient activity time-locked to events of the task and its profile remains constant during successive training stages. The second is sustained activity that undergoes nonmonotonic changes with changes in reward contingency that occur during the transition between stage

How much and for how long does the neonatal myocardium suffer from mild perinatal asphyxia?

Cardiac troponins can be useful in monitoring cardiac injury following perinatal distress. We report here an increase of cardiac troponin I (cTnI) to 2.84 microg/l at 3 weeks (age-related median: 0.07 microg/l) followed by normalization in a newborn with an uneventful clinical course after resuscitation at birth. Serial echocardiographs showed normal cardiac function. Such a time course of cTnI, not previously reported, could be due to either a greater sensitivity of biochemical markers than of instrumental tools or birth asphyxia. Larger studies are neede

Techniques neuronales et fusion de données appliquées à un système de détection de passage de véhicules "en-ligne"

Nous présentons ici un système temps réel de détection de passage de véhicules au dessus de capteurs magnétiques. Le système est indépendant du positionnement initial des capteurs et insensible aux perturbations magnétiques fortes éventuellement induites par les charges des véhicules. Ce système est fondé sur la coopération d'agents de détection neuronaux, avec une mesure de fiabilité de leurs réponses, et une fusion des informations de chaque agent par des règles logiques. Le système est robuste à de fortes perturbations magnétiques (y compris non-périodiques), il utilise les 3 composantes du champ magnétique, il est insensible aux rotations, et la modularité de sa conception permet une grande évolutivité

Réseaux prédictifs et fusion de données floue appliqués à un système de détection de passage de véhicules en ligne et temps réel

Dans cet article, nous présentons une architecture originale pour la fusion de données hétérogènes. Cette architecture a été appliquée à un système de détection magnétique. Elle est fondée sur la coopération d'agent neuronaux d'une part, et de règles expertes symboliques d'autre part. L'originalité de cette architecture repose, entre autres, sur l'utisation des réseaux de neurones prédictifs pour la caractérisation de signaux magnétiques. C'est ce dernier point qui est mis le plus en valeur dans cet article

Reconnaissance des sons de l'environnement dans un contexte domotique

Dans beaucoup de pays du monde, on observe une importante augmentation du nombre de personnes âgées vivant seules. Depuis quelques années, un nombre significatif de projets de recherche sur l assistance aux personnes âgées ont vu le jour. La plupart de ces projets utilisent plusieurs modalités (vidéo, son, détection de chute, etc.) pour surveiller l'activité de la personne et lui permettre de communiquer naturellement avec sa maison "intelligente", et, en cas de danger, lui venir en aide au plus vite. Ce travail a été réalisé dans le cadre du projet ANR VERSO de recherche industrielle, Sweet-Home. Les objectifs du projet sont de proposer un système domotique permettant une interaction naturelle (par commande vocale et tactile) avec la maison, et procurant plus de sécurité à l'habitant par la détection des situations de détresse. Dans ce cadre, l'objectif de ce travail est de proposer des solutions pour la reconnaissance des sons de la vie courante dans un contexte réaliste. La reconnaissance du son fonctionnera en amont d'un système de Reconnaissance Automatique de la Parole. Les performances de celui-ci dépendent donc de la fiabilité de la séparation entre la parole et les autres sons. Par ailleurs, une bonne reconnaissance de certains sons, complétée par d'autres sources informations (détection de présence, détection de chute, etc.) permettrait de bien suivre les activités de la personne et de détecter ainsi les situations de danger. Dans un premier temps, nous nous sommes intéressés aux méthodes en provenance de la Reconnaissance et Vérification du Locuteur. Dans cet esprit, nous avons testé des méthodes basées sur GMM et SVM. Nous avons, en particulier, testé le noyau SVM-GSL (SVM GMM Supervector Linear Kernel) utilisé pour la classification de séquences. SVM-GSL est une combinaison de SVM et GMM et consiste à transformer une séquence de vecteurs de longueur arbitraire en un seul vecteur de très grande taille, appelé Super Vecteur, et utilisé en entrée d'un SVM. Les expérimentations ont été menées en utilisant une base de données créée localement (18 classes de sons, plus de 1000 enregistrements), puis le corpus du projet Sweet-Home, en intégrant notre système dans un système plus complet incluant la détection multi-canaux du son et la reconnaissance de la parole. Ces premières expérimentations ont toutes été réalisées en utilisant un seul type de coefficients acoustiques, les MFCC. Par la suite, nous nous sommes penchés sur l'étude d'autres familles de coefficients en vue d'en évaluer l'utilisabilité en reconnaissance des sons de l'environnement. Notre motivation fut de trouver des représentations plus simples et/ou plus efficaces que les MFCC. En utilisant 15 familles différentes de coefficients, nous avons également expérimenté deux approches pour transformer une séquence de vecteurs en un seul vecteur, à utiliser avec un SVM linéaire. Dans le première approche, on calcule un nombre fixe de coefficients statistiques qui remplaceront toute la séquence de vecteurs. La seconde approche (une des contributions de ce travail) utilise une méthode de discrétisation pour trouver, pour chaque caractéristique d'un vecteur acoustique, les meilleurs points de découpage permettant d'associer une classe donnée à un ou plusieurs intervalles de valeurs. La probabilité de la séquence est estimée par rapport à chaque intervalle. Les probabilités obtenues ainsi sont utilisées pour construire un seul vecteur qui remplacera la séquence de vecteurs acoustiques. Les résultats obtenus montrent que certaines familles de coefficients sont effectivement plus adaptées pour reconnaître certaines classes de sons. En effet, pour la plupart des classes, les meilleurs taux de reconnaissance ont été observés avec une ou plusieurs familles de coefficients différentes des MFCC. Certaines familles sont, de surcroît, moins complexes et comptent une seule caractéristique par fenêtre d'analyse contre 16 caractéristiques pour les MFCCIn many countries around the world, the number of elderly people living alone has been increasing. In the last few years, a significant number of research projects on elderly people monitoring have been launched. Most of them make use of several modalities such as video streams, sound, fall detection and so on, in order to monitor the activities of an elderly person, to supply them with a natural way to communicate with their smart-home , and to render assistance in case of an emergency. This work is part of the Industrial Research ANR VERSO project, Sweet-Home. The goals of the project are to propose a domotic system that enables a natural interaction (using touch and voice command) between an elderly person and their house and to provide them a higher safety level through the detection of distress situations. Thus, the goal of this work is to come up with solutions for sound recognition of daily life in a realistic context. Sound recognition will run prior to an Automatic Speech Recognition system. Therefore, the speech recognition s performances rely on the reliability of the speech/non-speech separation. Furthermore, a good recognition of a few kinds of sounds, complemented by other sources of information (presence detection, fall detection, etc.) could allow for a better monitoring of the person's activities that leads to a better detection of dangerous situations. We first had been interested in methods from the Speaker Recognition and Verification field. As part of this, we have experimented methods based on GMM and SVM. We had particularly tested a Sequence Discriminant SVM kernel called SVM-GSL (SVM GMM Super Vector Linear Kernel). SVM-GSL is a combination of GMM and SVM whose basic idea is to map a sequence of vectors of an arbitrary length into one high dimensional vector called a Super Vector and used as an input of an SVM. Experiments had been carried out using a locally created sound database (containing 18 sound classes for over 1000 records), then using the Sweet-Home project's corpus. Our daily sounds recognition system was integrated into a more complete system that also performs a multi-channel sound detection and speech recognition. These first experiments had all been performed using one kind of acoustical coefficients, MFCC coefficients. Thereafter, we focused on the study of other families of acoustical coefficients. The aim of this study was to assess the usability of other acoustical coefficients for environmental sounds recognition. Our motivation was to find a few representations that are simpler and/or more effective than the MFCC coefficients. Using 15 different acoustical coefficients families, we have also experimented two approaches to map a sequence of vectors into one vector, usable with a linear SVM. The first approach consists of computing a set of a fixed number of statistical coefficients and use them instead of the whole sequence. The second one, which is one of the novel contributions of this work, makes use of a discretization method to find, for each feature within an acoustical vector, the best cut points that associates a given class with one or many intervals of values. The likelihood of the sequence is estimated for each interval. The obtained likelihood values are used to build one single vector that replaces the sequence of acoustical vectors. The obtained results show that a few families of coefficients are actually more appropriate to the recognition of some sound classes. For most sound classes, we noticed that the best recognition performances were obtained with one or many families other than MFCC. Moreover, a number of these families are less complex than MFCC. They are actually a one-feature per frame acoustical families, whereas MFCC coefficients contain 16 features per frameEVRY-INT (912282302) / SudocSudocFranceF

Sound environment analysis in smart home

International audienceThis study aims at providing audio-based interaction technology that lets the users have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. The paper presents the sound and speech analysis system evaluated thanks to a corpus of data acquired in a real smart home environment. The 4 steps of analysis are signal detection, speech/sound discrimination, sound classification and speech recognition. The results are presented for each step and globally. The very first experiments show promising results be it for the modules evaluated independently or for the whole system