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Principles and Methods for Face Recognition and Face Modelling
Abstract
This chapter focuses on the principles behind methods currently used for face recognition, which have a wide variety of uses from biometrics, surveillance and forensics. After a brief description of how faces can be detected in images, the authors describe 2D feature extraction methods that operate on all the image pixels in the face detected region: Eigenfaces and Fisherfaces first proposed in the early 1990s. Although Eigenfaces can be made to work reasonably well for faces captured in controlled conditions, such as frontal faces under the same illumination, recognition rates are poor. The authors discuss how greater accuracy can be achieved by extracting features from the boundaries of the faces by using Active Shape Models and, the skin textures, using Active Appearance Models, originally proposed by Cootes and Talyor. The remainder of the chapter on face recognition is dedicated such shape models, their implementation and use and their extension to 3D. The authors show that if multiple cameras are used the 3D geometry of the captured faces can be recovered without the use of range scanning or structured light. 3D face models make recognition systems better at dealing with pose and lighting variation.
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