The process or taking 2D and 3D images of faces and building a computer model of the faces. This may be a set of facial features and their geometry; the curves of the mouth, eyes, eyebrows, chin and cheeks; or a fully 3D model which incldues depth and colour information. Face modelling can be achieved from either 2D images (static or dynamic) or using 3D range scanning devices.
Published in Chapter:
Principles and Methods for Face Recognition and Face Modelling
Tim Rawlinson (Warwick Warp Ltd., UK), Abhir Bhalerao (University of Warwick, UK), and Li Wang (Warwick Warp Ltd., UK)
Source Title: Handbook of Research on Computational Forensics, Digital Crime, and Investigation: Methods and Solutions
Copyright: © 2010|Pages: 26
DOI: 10.4018/978-1-60566-836-9.ch003
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.
