Face Alignment Models

In building models of facial appearance, we adopt a statistical approach that learns the ways in which the shape and texture of the face vary across a range of images. We rely on obtaining a suitably large and representative training set of images of faces, each of which is annotated with a set of feature points that define correspondences across the set. The positions of the feature points also define the shape of the face, and are analysed to learn the ways in which the shape can vary. The patterns of intensities are analysed in a similar way to learn how the texture can vary. The result is a model which is capable of synthesising any of the training images and generalising from them, but is specific enough that only face-like images are generated. To build a statistical appearance model, we require a set of training images that covers the types of variation we want the model to represent. For instance, if we are only interested in faces with neutral expressions, we need only include neutral expressions in the model. If, however, we want to synthesise and recognise a range of expressions, the training set should include images of people smiling, frowning, winking and so on. Ideally, the faces in the training set should be of at least as high a resolution as those in the images we wish to synthesise or interpret.