Mouth region localization method based on Gaussian mixture model

Abstract

This paper presents a new mouth region localization method which uses the Gaussian mixture model (GMM) of feature vectors extracted from mouth region images. The discrete cosine transformation (DCT) and principle component analysis (PCA) based feature vectors are evaluated in mouth localization experiments. The new method is suitable for audio-visual speech recognition. This paper also introduces a new database which is available for audio visual processing. The experimental results show that the proposed system has high accuracy for mouth region localization (more than 95 %) even if the tracking results of preceding frames are unavailable. © Springer-Verlag Berlin Heidelberg 2006.