Face verification system architecture using smart cards

A smart card based face verification system is pro-posed in which the feature extraction and decision mak-ing is performed on the card. Such an architecture has many privacy and security benefits. As smart cards are limited computational platforms, the face verifica-tion algorithms have to be adapted to limit the facial image representations. This minimises the information needed to be sent to the card and lessens the computa-tional load of the template matching. Studies performed on the BANCA and XM2VTS databases demonstrate that by limiting these representations the verification perfor-mance of the system is not degraded and that the pro-posed architecture is a viable one. 1

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 2008Comment: Published at IEEE Transactions on Pattern Analysis and Machine Intelligence journa

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

Designing a Smart Card Face Verification System

This thesis describes a face verification system that is smart-card-based. The objectives were to identify the key parameters that affect the design of such a system, to investigate the general optimisation problem and test its robustness when each key parameter is optimised. Some of these parameters have been coarsely investigated in the literature in the context of the general face recognition problem. However, the previous work only partially fulfilled the requirements of a smart-card-based system, in which the severe engineering constraints and limitations imposed by smart cards have to be taken into account in the overall design process. To address these problems on the proposed fully localised architecture of the smart card face verification system (SCFVS), the work starts with the selection of the client specific linear discriminant analysis (CS-LDA) algorithm, suitable to be ported to the target platform on which the biometric process can run. Then the main functional parts of the system are presented: face image geometric alignment, photometric normalisation, feature extraction, and on-card verification. Each part consists of a series of basic steps, where the role of each step is fixed. However, the algorithm is systematically varied in some steps to investigate the effect on system performance, and system complexity in terms of speed and memory management

Designing a smart card face verification system.

This thesis describes a face verification system that is smart-card-based. The objectives were to identify the key parameters that affect the design of such a system, to investigate die general optimisation problem and test its robustness when each key parameter is optimised. Some of these parameters have been coarsely investigated in the literature in the context of the general face recognition problem. However, the previous work only partially fulfilled the requirements of a smart-card-based system, in which the severe engineering constraints and limitations imposed by smart cards have to be taken into account in the overall design process. To address these problems on the proposed fully localised architecture of the smart card face verification system (SCFVS), the work starts with the selection of the client specific linear discriminant analysis (CS-LDA) algorithm, suitable to be ported to the target platform on which the biometric process can run. Then the main functional parts of the system are presented: face image geometric alignment, photometric normalisation, feature extraction, and on-card verification. Each part consists of a series of basic steps, where the role of each step is fixed. However, the algorithm is systematically varied in some steps to investigate the effect on system performance, and system complexity in terms of speed and memory management. Two major problems have been considered. The first problem are the restrictions that both face verification and smart card technology impose and the second is the extreme complexity of the system, in terms of the number of processing stages and system design parameters. In the simplified search procedure adopted, a number of parameters has been selected out of the complete parameter set involved in a generic SCFVS. This set was recommended by previous main-frame based studies, and deemed to provide acceptable performance. System optimisation in the context of smart card implementation has been conducted starting from those parameters involved in the pre-processing stage of the system, and then those involved in the remaining stages. A joint optimisation framework of the key parameters can also be adopted, assuming that then- effect is independent. Experimental results obtained on a number of publicly available face databases (used to evaluate the system performance) show the significant benefits of this design both in terms of performance and system speed. The different results achieved on different databases indicate that optimum parameters of the system are, to a certain extent, training database dependent

Database size effects on performance on a smart card face verification system

We study the effect of development set size on system performance, as measured by verification error. The study was performed using the FERET and FRGC2 databases to construct development training sets of varying size, while XM2VTS was used to test the system. Surprisingly, the achievable performance levels off relatively quickly. Increasing the size of the development set does not bring any benefit. On the contrary it may result in performance degradation. This finding appears to be development set independent. However, the choice of the development set size is protocol dependent. 1