Multimodal Biometrics of Lip Movements and Voice using Kernel Fisher Discriminant Analysis

We propose a method of text-prompted speaker recognition based on multimodal biometrics by using the kernel Fisher discriminant analysis. Algorithms for person authentication using multimodal biometrics have already been developed. When the decision boundary between genuine and imposter has a nonlinear structure, the identification accuracy will be degraded because the conventional recognition methods do not assume nonlinearity of decision boundary. We have investigated the score distribution. We also need to control the security level (FAR,FRR) by the threshold. To solve the problems, we have introduced a method of speaker recognition based on multimodal biometrics by using the kernel Fisher discriminant analysis. We experimentally demonstrate the proposed method´s effectiveness with simulation results and show that the method achieved high accuracy