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.

This paper describes a system using two complementary sorts of information issuing from a hidden Markov model (HMM) for online signature verification. At each point of the signature, 25 features are extracted. These features are normalized before training and testing in order to improve the performance of the system. This normalization is writer-dependent; it exploits only five genuine signatures used to train the writer HMM. A claimed identity is confirmed when the arithmetic mean of two similarity scores, obtained on an input signature, is higher than a threshold. The first score is related to the likelihood given by the HMM of the claimed identity; the second score is related to the segmentation given by such an HMM on the input signature. A personalized score normalization is also proposed before fusion. Our approach is evaluated on several online signature databases, such as BIOMET, PHILIPS, MCYT, and SVC2004, which were captured under different acquisition conditions. For the first time in signature verification, we show that the fusion of segmentation-based information generated by the HMM with likelihood-based information considerably improves the quality of the verification system. Finally, owing to our two-stage normalization (at the feature and score levels), we show that our system results in more stable client-score distributions across databases and in a better separation between the distributions of client and impostor scores.