Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics is an important application in day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both template and query data. The response to the call of the campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms

The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1 over the Internet, 2 in an office environment with desktop PC, and 3 in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008

Assemblage de novo avec Spark

International audienceLes récentes avancées en biologie moléculaire et l’avènement des méthodes de séquençage à haut débit ont rendu possible la lecture, l’analyse et la réutilisation d’une très grande partie de l’information présente dans le génome. Malheureusement, notre capacité à analyser l’immense masse de données générée par ces nouvelles technologies de séquençage est aujourd’hui limitéepar nos moyens de calcul. Dans ces travaux, nous nous intéressons plus particulierèrement aux problématiques de l’assemblage de novo, dont l’objectif est de reconstruire une séquence ADN à partir d’un ensemble de fragments issus de cette même séquence. Nous mettons en évidence les avantages d’utiliser les outils (notamment méthodologiques) de la communauté “big data” pour résoudre ce problème sur des instances réelles de très grande taille. Nous discutons des résultats numériques prometteurs que nous avons pu obtenir en implémentant cette approche à l’aide du framework Apache Spark

Alternating optimization for lambertian photometric stereo model with unknown lighting directions

Conference of 15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013 ; Conference Date: 27 August 2013 Through 29 August 2013; Conference Code:99445International audiencePhotometric stereo is a technique of surface reconstruction using several object images made with a fixed camera position and varying illumination directions. Reconstructed surfaces can have complex reflecting properties which are unknown a priori and often simplified by Lambertian model (reflecting light uniformly in all directions). Such simplification leads to certain inaccuracy of reconstruction but in most cases is sufficient to obtain general object relief important for further recognition. Not only surface properties but also lighting sources utilized for each image acquisition can be very complex for modeling, or even unknown. Our work demonstrates how to find surface normals from Lambertian photometric stereo model using color images made with a priori unknown lighting directions. Evaluation of model components is based on an alternating optimization approach

A new protocol for multi-biometric systems' evaluation maintaining the dependencies between biometric scores

International audienceWe address the problem of measuring the dependency of multibiometric systems' scores, using Kolmogorov-Smirnov and Mutual Information criteria, and studying the validity of performance evaluation on chimeric persons. On the NIST-BSSR1 database, we formalize a common assumption in the literature: for independent scores, multibiometric systems can be evaluated on "random chimeric" persons. We show that this is not valid for dependent scores and propose a novel protocol for building "cluster-based chimeric" persons maintaining the level of dependency between scores. Finally, we show that performance evaluation for dependent modalities on such persons is equivalent to that obtained on "real" persons

Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics is an important application in day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both template and query data. The response to the call of the campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms

Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms

Automatically verifying the identity of a person by means of biometrics is an important application in day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both template and query data. The response to the call of the campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms