This paper presents a survey of the research carried out to date in the area of computer-based deformable modelling. Due to their cross-disciplinary nature, deformable modelling techniques have been the subject of vigorous research over the past three decades and have found numerous applications in the fields of machine vision (image analysis, image segmentation, image matching, and motion tracking), visualisation (shape representation and data fitting), and computer graphics (shape modelling, simulation, and animation). Previous review papers have been field/application specific and have therefore been limited in their coverage of techniques. This survey focuses on general deformable models for computer-based modelling, which can be used for computer graphics, visualisation, and various image processing applications. The paper organizes the various approaches by technique and provides a description, critique, and overview of applications for each. Finally, the state of the art of deformable modelling is discussed, and areas of importance for future research are suggested.

Abstract. This paper is concerned with capturing the dynamics of facial expression. The dynamics of facial expression can be described as the intensity and timing of a facial expression and its formation. To achieve this we developed a technique that can accurately classify and differentiate between subtle and similar expressions, involving the lower face. This is achieved by using Local Linear Embedding (LLE) to reduce the dimensionality of the dataset and applying Support Vector Machines (SVMs) to classify expressions. We then extended this technique to estimate the dynamics of facial expression formation in terms of intensity and timing.

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The speed and intensity of the appearance changes that occur during the formation of facial expressions provide important information about the underlying meaning of the expression itself. In the past we have demonstrated the effectiveness of using Locally Linear Embedding with facial shape information for estimating the dynamics of facial expression. This approach was only suitable for specific expressions, where the appearance change was principally due to a movement or distortion of the shape of facial features. However, for some facial expressions, the variation in the shape of the facial features is very subtle. These expressions are mainly characterised by the variation in the texture of the face. Hence such expressions are not amenable to the previous approach. In order to estimate the dynamics of these types of expressions it is necessary to develop nonlinear appearance models that incorporate texture information. In this paper we use LLE to estimate the manifold of texture variation due to facial expression. We show that the resulting manifold effectively captures the underlying dynamics of facial expression and that it provides a suitable representation for differentiation between posed and spontaneous expressions.

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