Handbook of Face Recognition
Containing a comprehensive account of face recognition research and technology, this handbook provides coverage of the concepts, methods, and algorithms for automated face detection and recognition.
TABLE OF CONTENTS Handbook of Face Recognition
Chapter 3: Modeling Facial Shape and Appearance
Tim Cootes, Chris Taylor, Haizhuang Kang, Vladimir Petrovi?
Imaging Science and Biomedical Engineering, University of Manchester, UK
To interpret images of faces, it is important to have a model of how the face can appear. Faces can vary widely, but the changes can be broken down into two parts: changes in shape and changes in the texture (patterns of pixel values) across the face. Both shape and texture can vary because of differences between individuals and due to changes in expression, viewpoint, and lighting conditions. In this chapter we will describe a powerful method of generating compact models of shape and texture variation and describe how such models can be used to interpret images of faces.
1 Statistical Models of Appearance
We wish to build models of facial appearance and its variation. We adopt a statistical approach, learning the ways in which the shape and texture of the face vary across a range of images. We rely on obtaining a suitably large, representative training set of facial images, each of which is annotated with a set of feature points defining correspondences across the set. The positions of the feature points are used to define the shape of the face and are analyzed to learn the ways in which the shape can vary. The patterns of intensities are then analyzed to learn the ways in which the texture can vary. The result is a model capable of synthesizing any of the training images and generalizing from them, but it is specific...
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