AUTHENTIFICATION D’INDIVIDUS PAR RECONNAISSANCE DE VISAGES

La vérification de visage est un outil important pour l'authentification d'un individu. Elle peut être de valeur significative dansla sécurité et les applications de commerce. Le but assigné à ce travail est de faire l’authentification d’individus. Pour cela,nous avons utilisé un modèle Biométrique. La biométrie est la science qui étudie les méthodes de vérification d’identité(authentification), identification, ou même de chiffrement basées sur la reconnaissance de caractéristiques physiologiques del’individu. Pour être efficaces dans leur exploitation Ces caractéristiques doivent bien entendu posséder certaines qualitésintrinsèques pour permettre le développement de systèmes fiables et robustes. Les qualités indispensables pour chaquecaractéristique sont les suivantes : l’universalité, unicité, permanence, collectabilité et mesurabilité. Celles-ci assurent quechaque personne possède la caractéristique considérée, qu’elle est unique pour chaque individu, qu’elle ne change pas ou peudans le temps, qu’il est possible d’en récolter un échantillon et de l’analyser. Pour notre application, nous avons choisi pourl’extraction des caractéristiques la méthode de ACP (analyse en composantes principales) [1]. Une fois que le vecteurcaractéristique du visage est extrait, l’étape suivante consiste à le comparer avec le vecteur caractéristique de l’identitéproclamée. Ici, il s'agit de classer l'utilisateur comme un vrai utilisateur ou un imposteur. Par la suite le taux d’erreur est calculédans les deux ensembles, de validation et de test pour la base de données XM2VTS [2] selon le protocole de Lausanne [3]

An Efficient Human Activity Recognition Technique Based on Deep Learning

In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition

Chemometric Calibration of Infrared Spectrometers: Selection and Validation Of Variables by . . .

Data from spectrophotometers form spectra that are sets of a great number of exploitable variables in quantitative chemical analysis; calibration models using chemometric methods must be established to exploit these variables. In order to design these calibration models which are specific to each analyzed parameter, it is advisable to select a reduced number of spectral variables. This paper presents a new incremental method (step by step) for the selection of spectral variables, using linear regression or neural networks, and based on an objective validation (external) of the calibration model; this validation is carried out on data that are independent from those used during calibration. The advantages of the method are discussed and highlighted, in comparison to the current calibration methods used in quantitative chemical analysis by spectrophotometry

SVR active learning for product quality control

In this work, the active learning approach is adopted to address the problem of training sample collection for the estimation of chemical parameters for product quality control from spectroscopic data. In particular, two strategies for support vector regression (SVR) are proposed. The first method select samples distant in the kernel space from the current support vectors, while the second one uses a pool of regressors in order to choose the samples with the greater disagreements between the different regressors. The experimental results on two real data sets show the effectiveness of the proposed solutions. © 2012 IEEE

Qualitative approach for forward kinematic modeling of a Compact Bionic Handling Assistant trunk

International audienc

Neural Networks based approach for inverse kinematic modeling of a Compact Bionic Handling Assistant trunk

International audienc

Qualitative approach for inverse kinematic modeling of a Compact Bionic Handling Assistant trunk

International audienc

A functional approach to variable selection in spectrometric problems

In spectrometric problems, objects are characterized by high-resolution spectra that correspond to hundreds to thousands of variables. In this context, even fast variable selection methods lead to high computational load. However, spectra are generally smooth and can therefore be accurately approximated by splines. In this paper, we propose to use a B-spline expansion as a pre-processing step before variable selection, in which original variables are replaced by coefficients of the B-spline expansions. Using a simple leave-one-out procedure, the optimal number of B-spline coefficients can be found efficiently. As there is generally an order of magnitude less coefficients than original spectral variables, selecting optimal coefficients is faster than selecting variables. Moreover, a B-spline coefficient depends only on a limited range of original variables: this preserves interpretability of the selected variables. We demonstrate the interest of the proposed method on real-world data