Spoken Handwriting Verification Using Statistical Models

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.