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This paper describes a new approach for speaker identification based on lipreading. Visual features are extracted from image sequences of the talking face and consist of shape parameters which describe the lip boundary and intensity parameters which describe the grey-level distribution of the mouth area. Intensity information is based on principal component analysis using eigenspaces which deform with the shape model. The extracted parameters account for both, speech dependent and speaker dependent information. We built spatio-temporal speaker models based on these features, using HMMs with mixtures of Gaussians. Promising results were obtained for text dependent and text independent speaker identification tests performed on a small video database.