Performance Evaluation of Multimodal Biometric Systems based on Mathematical Models and Probabilistic Neural Networks

Multimodal biometrics overcome the technical limitations of unimodal biometrics, making them ideally suited for everyday life applications that require a reliable authentication system. However, for a successful adoption of multimodal biometrics, such systems would require large heterogeneous datasets with complex multimodal fusion and privacy schemes spanning various distributed environments. From experimental investigations of current multimodal systems, this paper reports the analysis of the multimodal biometric system performance based on the combination of voice, face and signature recognitions. The first part of the paper describes different methods used for the recognition of three biometric traits, established databases and relative performance obtained by using unimodal biometrics system. In the second part of the paper the multimodal biometric approach and the performance is described. The EER (Equal Error Rate) obtained with the multimodal approach by using a database of 50 individuals is about 0.4%, whereas most reallife biometric systems are affected with a variety of problems. Finally, the paper presents the implementation of a multimodal biometric system based on a probabilistic neural network in order to improve the recognition rate in a noisy scenario Keywords—Biometrics, neural networks, performances