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.

In this paper, we report on the development of an efficient user authentication system based on a combined acquisition of online pen and speech signals. The novelty of our approach is in the simultaneous recording of these two modalities, simply asking the user to utter what she/he is writing. The main benefit of this multimodal approach is a better accuracy at no extra costs in terms of access time or inconvenience. Another benefit comes from an increased difficulty for forgers willing to perform imitation attacks as two signals need to be reproduced. We are comparing here two potential scenarios of use. The first one is calledspokensignatureswhere the user signs and says the content of the signature. The second scenario is based onspokenhandwritingwhere the user is prompted to write and read the content of sentences randomly extracted from a text. Data according to these two scenarios have been recorded from a set of 70 users. In the first part of this paper, we describe the acquisition procedure, and we comment on the viability and usability of such simultaneous recordings. Our conclusions are supported by a short survey performed with the users. In the second part, we present the authentication systems that we have developed for both scenarios. More specifically, our strategy was to model independently both streams of data and to perform a fusion at the score level. Starting from a state-of-the-art-modeling algorithm based on Gaussian Mixture Models trained with an Expectation-Maximization procedure, we report on several significant improvements that are brought. As a general observation, the use of both modalities outperforms significantly the modalities used alone.

We report on our developments towards building a novel user authentication system using combined acquisition of online handwritten signature and speech modalities. In our approach, signatures are recorded by asking the user to say what she/he is writing, leading to the so-called spoken signatures. We have built a verification system composed of two Gaussian mixture models (GMMs) sub-systems that model independently the pen and voice signal. We report on results obtained with two algorithms used for training the GMMs, respectively expectation maximization and maximum a posteriori adaptation. Different algorithms are also compared for fusing the scores of each modality. The evaluations are conducted on spoken signatures taken from the MyIDea multimodal database, accordingly to the protocols provided with the database. Results are in favor of using MAP adaptation with a simple weighted sum fusion. Results show also clearly the impact of time variability and of skilled versus unskilled forgeries attacks.

We are proposing a novel and efficient user authentication system using combined acquisition of online handwriting and speech signals. In our approach, signals are recorded by asking the user to say what she or he is simultaneously writing. This methodology has the clear advantage of acquiring two sources of biometric information at no extra cost in terms of time or inconvenience. We have built a straightforward verification system to model these signals using statistical models. It is composed of two Gaussian mixture models (GMMs) sub-systems that takes as input features extracted from the pen and voice signals. The system is evaluated on Myldea, a realistic multimodal biometric database. Results show that the use of both speech and handwriting modalities outperforms significantly these modalities used alone. We also report on the evaluations of different training algorithms and fusion strategies.