Adaptive mouth segmentation using chromatic features

Authors:
Simon Lucey;Sridha Sridharan;Vinod Chandran
Affiliations:
Speech Research Laboratory, RCSAVT, School of Electrical and Electronic Systems Engineering, Queensland University of Technology, GPO Box 2434, Brisbane, Qld 4001, Australia;Speech Research Laboratory, RCSAVT, School of Electrical and Electronic Systems Engineering, Queensland University of Technology, GPO Box 2434, Brisbane, Qld 4001, Australia;Speech Research Laboratory, RCSAVT, School of Electrical and Electronic Systems Engineering, Queensland University of Technology, GPO Box 2434, Brisbane, Qld 4001, Australia
Venue:
Pattern Recognition Letters
Year:
2002

Citing 7
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Abstract

The automatic segmentation of the mouth from its facial background is a very difficult computer vision problem due to the low grayscale distinction between classes. Recently chromatic based segmentation has enjoyed some popularity for the purposes of mouth tracking due to its ability to distinguish between the two classes. Such systems have to be highly adaptive due to problems with colour constancy. In this paper, a technique for adaptive segmentation is investigated using an unsupervised clustering technique incorporating the expectation maximisation (EM) algorithm across a variety of chromatic features. Results are presented from the M2VTS database across a number of subjects.