Lip reading based on sampled active contour model

Abstract

This paper describes a model-based method for detecting lip region from image sequences. Our approach is by Sampled Active Contour Model (S-ACM). The original S-ACM has the problem which can't expand. To overcome this problem, we propose the elastic S-ACM. Moreover, based on the extracted lip contour, the effective delta radius features are led to the word HMM. We recorded ten words that uses for the wheelchair control, and obtained a recognition rate of 89% with twelve features. © Springer-Verlag Berlin Heidelberg 2005.