Timothy F. Cootes

Active shape models—their training and application

!, Model-based vision is firmly established as a robust approach to recognizing and locating known rigid objects in the presence of noise, clutter, and occlusion. It is more problematic to apply modelbased methods to images of objects whose appearance can vary, though a number of approaches based on the use of flexible templates have been proposed. The problem with existing methods is that they sacrifice model specificity in order to accommodate variability, thereby compromising robustness during image interpretation. We argue that a model should only be able to deform in ways characteristic of the class of objects it represents. We describe a method for building models by learning patterns of variability from a training set of correctly annotated images. These models can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes). The key difference is that our Active Shape Models can only deform to fit the data in ways consistent with the training set. We show several practical examples where we have built such models and used them to locate partially occluded objects in noisy, cluttered images. Q 199s A&& prrss, IN.

Active appearance models

We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.

Active Appearance Models

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.

Use of active shape models for locating structures in medical images

Abstract This paper describes a technique for building compact models of the shape and appearance of flexible objects (such as organs) seen in 2D images. The models are derived from the statistics of labelled images containing examples of the objects. Each model consists of a flexible shape template, describing how the relevant locations of important points on the objects can vary, and a statistical model of the expected grey levels in a region around each model point. We describe how the models can be used in local image search, and give examples of their application to medical images.

Automatic interpretation and coding of face images using flexible models

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above.

Toward automatic simulation of aging effects on face images

The process of aging causes significant alterations in the facial appearance of individuals. When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics. Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals. We describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images. We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle. Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past. The methodology presented has also been used for designing a face recognition system, robust to aging variation. In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated. Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved.

Active Shape Models — ‘Smart Snakes’

We describe ‘Active Shape Models’ which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples. These models, known as Point Distribution Models, represent objects as sets of labelled points. An initial estimate of the location of the model points in an image is improved by attempting to move each point to a better position nearby. Adjustments to the pose variables and shape parameters are calculated. Limits are placed on the shape parameters ensuring that the example can only deform into shapes conforming to global constraints imposed by the training set. An iterative procedure deforms the model example to find the best fit to the image object. Results of applying the method are described. The technique is shown to be a powerful method for refining estimates of object shape and location.

Feature Detection and Tracking with Constrained Local Models

We present an efficient and robust model matching method which uses a joint shape and texture appearance model to generate a set of region template detectors. The model is fitted to an unseen image in an iterative manner by generating templates using the joint model and the current parameter estimates, correlating the templates with the target image to generate response images and optimising the shape parameters so as to maximise the sum of responses. The appearance model is similar to that used in the Active Appearance Model due to Cootes et al. However in our approach the appearance model is used to generate likely feature templates, instead of trying to approximate the image pixels directly. We show that when applied to human faces, our constrained local model (CLM) algorithm is more robust and more accurate than the original AAM search method, which relies on the image reconstruction error to update the model parameters. We demonstrate improved localisation accuracy on two publicly available face data sets and improved tracking on a challenging set of in-car face sequences.

Training Models of Shape from Sets of Examples

A method for building flexible shape models is presented in which a shape is represented by a set of labelled points. The technique determines the statistics of the points over a collection of example shapes. The mean positions of the points give an average shape and a number of modes of variation are determined describing the main ways in which the example shapes tend to deform from the average. In this way allowed variation in shape can be included in the model. The method produces a compact flexible ‘Point Distribution Model’ with a small number of linearly independent parameters, which can be used during image search. We demonstrate the application of the Point Distribution Model in describing two classes of shapes.

Interpreting face images using active appearance models

We demonstrate a fast, robust method of interpreting face images using an Active Appearance Model (AAM). An AAM contains a statistical model of shape and grey level appearance which can generalise to almost any face. Matching to an image involves finding model parameters which minimise the difference between the image and a synthesised face. We observe that displacing each model parameter from the correct value induces a particular pattern in the residuals. In a training phase, the AAM learns a linear model of the correlation between parameter displacements and the induced residuals. During search it measures the residuals and uses this model to correct 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 show it matching to new face images.