Lip-reading technique using spatio-temporal templates and support vector machines

Abstract

This paper presents a lip-reading technique to identify the unspoken phones using support vector machines. The proposed system is based on temporal integration of the video data to generate spatio-temporal templates (STT). 64 Zernike moments (ZM) are extracted from each STT. This work proposes a novel feature selection algorithm to reduce the dimensionality of the 64 ZM to 12 features. The proposed technique uses the shape of probability curve as a goodness measure for optimal feature selection. The feature vectors are classified using non-linear support vector machines.Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords when in public spaces. © 2008 Springer-Verlag Berlin Heidelberg.