A Pruning Approach Improving Face Identification Systems

International audienceWe propose, in this paper, a new biometric identification approach which aims to improve recognition performances in identification systems. We aim to split the identity database into well separated partitions in order to simplify the identification task. In this paper we develop a face identification system and we use the reference algorithms of eigenfaces and fisherfaces in order to extract different features describing each identity. These features, which describe faces, are generally optimized to establish the required identity in a classical identification process. In this work, we develop a novel criterion to extract features used to partition the identity database. We develop database partitioning with clustering methods which split the gallery by bringing together identities which have similar features and separating dissimilar features in different bins. Pruning the most dissimilar bins from the query identity features allows us to improve the identification performances. We report results from the XM2VTS database

SPECIFIC SITES OF BONE EXPANSION DEPEND ON THE LEVEL OF VOLLEYBALL PRACTICE IN PREPUBESCENT BOYS

The purpose of this study was to investigate the effect of 18 months of high and low levels of volleyball practice on bone acquisition. 130 prepubescent boys (mean age 11.4 ± 0.7) were divided into a high-level training group (HLG), low-level training group (LLG), and controls. Bone mineral content (BMC) and bone area at the whole body, lumbar spine L2-L4, femoral neck of the dominant leg, and right and left radius were measured using dual-photon X-ray absorptiometry. Enhanced BMC resulted from high-training volleyball activity in all measured sites except the third left and right distal radius, which is not modified by low-level training in prepubescent players but it was accompanied by a bone area expansion in radius and weight-bearing sites for the HLG, and in legs, whole right and left radius for the LLG. Significant improvement of skeletal tissues is associated with the intensity and duration of volleyball training

Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

This article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.info:eu-repo/semantics/publishedVersio

Acinetobacter baumannii ventilator-associated pneumonia: epidemiology, clinical characteristics, and prognosis factors

SummaryObjectiveThe aim of this study was to describe the epidemiological characteristics of Acinetobacter baumannii ventilator-associated pneumonia (VAP) and to identify factors predictive of a poor outcome.MethodsA retrospective study was conducted over 16 months in a Tunisian intensive care unit (ICU). All adult patients with A. baumannii VAP were included.ResultsNinety-two patients were included in they study; 41 (44.6%) were admitted because of multiple trauma. The mean age of the patients was 44.5±19.5 years. All patients needed mechanical ventilation on admission. The mean SAPS II score was 39±15. The mean delay before VAP onset was 8.1±4.7 days. On VAP onset, 57 patients (62%) developed septic shock. Only 14.2% of isolated strains were susceptible to imipenem; none were resistant to colistin. The mean duration of mechanical ventilation was 20±11 days. The mean duration of ICU stay was 24.3±18.7 days. ICU mortality was 60.9%. In the multivariate analysis, factors predictive of a poor outcome were previously known hypertension (odds ratio 5.8, 95% confidence interval 1.4–24.9; p=0.018) and VAP-related septic shock (odds ratio 8.5, 95% confidence interval 3–23.7; p<0.001).ConclusionA. baumannii VAP is associated with a high mortality. Hemodynamic impairment is predictive of a poor outcome

Nouvelle approche d'identification dans les bases de données biométriques basée sur une classification non supervisée

The work done in the framework of this thesis deal with the automatic faces identification in databases of digital images. The goal is to simplify biometric identification process that is seeking the query identity among all identities enrolled in the database, also called gallery. Indeed, the classical identification scheme is complex and requires large computational time especially in the case of large biometric databases. The original process that we propose here aims to reduce the complexity and to improve the computing time and the identification rate performances. In this biometric context, we proposed an unsupervised classification or clustering of facial images in order to partition the enrolled database into several coherent and well discriminated subsets. In fact, the clustering algorithm aims to extract, for each face, a specific set of descriptors, called signature. Three facial representation techniques have been developed in order to extract different and complementary information which describe the human face: two factorial methods of multidimensional analysis and data projection (namely called "Eigenfaces" and "Fisherfaces") and a method of extracting geometric Zernike moments. On the basis of the different signatures obtained for each face, several clustering methods are used in competing way in order to achieve the optimal classification which leads to a greater reduction of the gallery. We used either "mobile centers" methods type such as the K-means algorithm of MacQueen and that of Forgy, and the "agglomerative" method of BIRCH. Based on the dependency of the generated partitions, these different classifying strategies are then combined using a parallel architecture in order to maximize the reduction of the search space to the smallest subset of the database. The retained clusters in fine are those which contain the query identity with an almost certain probability.Les travaux effectués dans le cadre de cette thèse portent sur l'identification automatique de visages dans des bases de données d'images numériques. L'objectif est de simplifier le déroulement de l'identification biométrique qui consiste à la recherche de l'identité requête parmi l'ensemble des individus de la base de données préenregistrée, appelée aussi galerie. En effet, le schéma d'identification classique est complexe et très coûteux notamment dans le cas de grandes bases de données biométriques. Le processus original que nous proposons ici a pour objectif de réduire la complexité et d'améliorer les performances en terme de temps de calcul et de taux d'identification. Dans ce cadre biométrique, nous avons proposé une étape de classification non supervisée ou clustering des visages pour partitionner la base d'images en plusieurs sous ensembles cohérents et bien discriminés. Pour ce faire, l'algorithme de clustering vise à extraire, pour chaque visage, un ensemble de descripteurs, appelé signature, qui lui soit spécifique. Trois techniques de représentation faciales ont été développées dans le but d'extraire des informations différentes et complémentaires décrivant le visage humain : deux méthodes factorielles d'analyse multidimensionnelle et de projection des données (méthodes dites « Eigenfaces » et « Fisherfaces ») ainsi qu'une méthode d'extraction des moments géométriques de Zernike. Sur la base des différentes classes de signatures extraites pour chaque visage plusieurs méthodes de clustering sont mises en compétition afin de dégager la classification optimale qui conduit à une meilleure réduction de la galerie. Les méthodes retenues sont, soit de type « centres mobiles » telles que les K-moyennes et les centres mobiles de Forgy, ou de type « agglomérative » telle que la méthode de BIRCH. Sur la base de la dépendance des partitions générées, ces différentes stratégies classificatoires sont ensuite combinées suivant une architecture parallèle de manière à optimiser la réduction de l'espace de recherche à un sous ensemble de la base de données. Les clusters retenus in fine étant ceux pour lesquels la probabilité d'appartenance de l'identité requête est quasi certaine

A new identification approach in biometric databases based on unsupervised classification

Les travaux effectués dans le cadre de cette thèse portent sur l’identification automatique de visages dans des bases de données d’images numériques. L’objectif est de simplifier le déroulement de l’identification biométrique qui consiste à la recherche de l’identité requête parmi l’ensemble des individus de la base de données préenregistrée, appelée aussi galerie. En effet, le schéma d’identification classique est complexe et très coûteux notamment dans le cas de grandes bases de données biométriques. Le processus original que nous proposons ici a pour objectif de réduire la complexité et d’améliorer les performances en terme de temps de calcul et de taux d’identification. Dans ce cadre biométrique, nous avons proposé une étape de classification non supervisée ou clustering des visages pour partitionner la base d’images en plusieurs sous ensembles cohérents et bien discriminés. Pour ce faire, l’algorithme de clustering vise à extraire, pour chaque visage, un ensemble de descripteurs, appelé signature, qui lui soit spécifique. Trois techniques de représentation faciales ont été développées dans le but d’extraire des informations différentes et complémentaires décrivant le visage humain : deux méthodes factorielles d’analyse multidimensionnelle et de projection des données (méthodes dites « Eigenfaces » et « Fisherfaces ») ainsi qu’une méthode d’extraction des moments géométriques de Zernike. Sur la base des différentes classes de signatures extraites pour chaque visage plusieurs méthodes de clustering sont mises en compétition afin de dégager la classification optimale qui conduit à une meilleure réduction de la galerie. Les méthodes retenues sont, soit de type « centres mobiles » telles que les K-moyennes et les centres mobiles de Forgy, ou de type « agglomérative » telle que la méthode de BIRCH. Sur la base de la dépendance des partitions générées, ces différentes stratégies classificatoires sont ensuite combinées suivant une architecture parallèle de manière à optimiser la réduction de l’espace de recherche à un sous ensemble de la base de données. Les clusters retenus in fine étant ceux pour lesquels la probabilité d’appartenance de l’identité requête est quasi certaine.The work done in the framework of this thesis deal with the automatic faces identification in databases of digital images. The goal is to simplify biometric identification process that is seeking the query identity among all identities enrolled in the database, also called gallery. Indeed, the classical identification scheme is complex and requires large computational time especially in the case of large biometric databases. The original process that we propose here aims to reduce the complexity and to improve the computing time and the identification rate performances. In this biometric context, we proposed an unsupervised classification or clustering of facial images in order to partition the enrolled database into several coherent and well discriminated subsets. In fact, the clustering algorithm aims to extract, for each face, a specific set of descriptors, called signature. Three facial representation techniques have been developed in order to extract different and complementary information which describe the human face: two factorial methods of multidimensional analysis and data projection (namely called "Eigenfaces" and "Fisherfaces") and a method of extracting geometric Zernike moments. On the basis of the different signatures obtained for each face, several clustering methods are used in competing way in order to achieve the optimal classification which leads to a greater reduction of the gallery. We used either "mobile centers" methods type such as the K-means algorithm of MacQueen and that of Forgy, and the "agglomerative" method of BIRCH. Based on the dependency of the generated partitions, these different classifying strategies are then combined using a parallel architecture in order to maximize the reduction of the search space to the smallest subset of the database. The retained clusters in fine are those which contain the query identity with an almost certain probability

A new clustering approach for face identification

International audienceWe propose in this paper a search approach which aim to improve identification in biometric databases. We work with face images and we develop appearance-based Eigenfaces and Fisherfaces methods to generate holistic and discriminant features and attributes. These features, which describe faces, are often used to establish the required identity in a classical identification process. In this work we introduce a clustering process upstream the identification process which divides faces into partitions according to their features similarities. Indeed, we aim to split biometric databases into partitions in order to simplify the recognition task within these databases. This paper describes the proposed clustering approach, the Eigenfaces and Fisherfaces representation methods and preliminary clustering results on the XM2VTS data corpus

Appearance based face identification and classification. A Combining approach

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Face classification for large biometric databases

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Face Classification Scheme Simplifying Identification in Biometric Databases

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