. We demonstrate a novel method of interpreting images using an Active Appearance Model (AAM). An AAM contains a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example. During a training phase we learn the relationship between model parameter displacements and the residual errors induced between a training image and a synthesised model example. To match to an image we measure the current residuals and use the model to predict changes to the current parameters, leading to a better fit. A good overall match is obtained in a few iterations, even from poor starting estimates. We describe the technique in detail and give results of quantitative performance tests. We anticipate that the AAM algorithm will be an important method for locating deformable objects in many applications. 1 Introduction Model-based approaches to the interpretation of images of variable objects are now attracting considerable interest [6][8...

Statistical models of the shape and appearance of image structures can be matched to new images using both the Active Shape Model [7] algorithm and the Active Appearance Model algorithm [2]. The former searches along profiles about the current model point positions to update the current estimate of the shape of the object. The latter samples the image data under the current instance and uses the difference between model and sample to update the appearance model parameters. In this paper we compare and contrast the two algorithms, giving the results of experiments testing their performance on two data sets, one of faces, the other of structures in MR brain sections. We find that the ASM is faster and achieves more accurate feature point location than the AAM, but the AAM gives a better match to the texture. 1 Introduction Interpretting images containing objects whose appearance can vary is difficult. A powerful approach has been to use deformable models, which can represent the variati...

Facial variation divides into a number of functional subspaces, and ensemble-specific variation. An improved method of measuring these is presented, within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by Principal Components Analysis on appropriate groups of faces. An expectation-maximization algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set.

Facial variation divides into a number of functional subspaces. An improved method of measuring these was designed within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity, expression) were obtained by Principal Components Analysis on appropriate groups of faces. An iterative algorithm was applied to image codings to maximise the probability of coding across these non-orthogonal subspaces before obtaining the projection on each sub-space and recalculating the spaces. This procedure enhances identity recognition, reduces overall sub-space variance and produces Principal Components with greater span and less contamination.

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.

This paper presents advances in the construction and use of Active Appearance Models (AAMs) for image interpretation. AAMs are photo-realistic generative models of object appearance that can be used to rapidly locate deformable objects in images. We extend the AAM method to include coloured texture and present an enhanced search algorithm with the ability to locate partially occluded objects. Previously, AAMs have been limited by the need for good manual initialisation. In this paper, we describe a hierarchical search algorithm that overcomes this drawback. The extended AAM method provides a complete, unified scheme for model based image interpretation. We demonstrate the application of the scheme to the task of locating faces in images.

An Active Appearance Model (AAM) allows complex models of shape and appearance to be matched to new images rapidly. An AAM contains a statistical model of the shape and grey-level appearance of an object of interest The associated search algorithm exploits the locally linear relationship between model parameter displacements and the residual errors between model instance and image. This relationship can be learnt during a training phase. To match to an image we measure the current residuals and use the model to predict changes to the current parameters. The algorithm converges in a few iterations. In this paper we describe variations of the basic algorithm aimed at improving the speed and robustness of search. These include subsampling and using image residuals to drive the shape rather than full appearance model. We show examples of search and give the results of experiments comparing the performance of the different algorithms. 1 Introduction Model based methods are now widely used ...

Model based approaches to the interpretation of face images have proved very successful. We have previously described statistically based models of face shape and grey-level appearance and shown how they can be used to perform various coding and interpretation tasks (Lanitis et al., 1995). In the paper we describe improved methods of modelling, which couple shape and grey-level information more directly than our existing methods, isolate the changes in appearance due to different sources of variability (person, expression, pose, lighting) and deal with nonlinear shape variation. We show that the new methods are better suited to interpretation and tracking tasks.