By Andreas Koschan, Marc Pollefeys, Mongi Abidi
This publication offers the completely revised models of lectures given through best researchers in the course of the Workshop on complex 3D Imaging for security and safety along side the overseas convention on machine imaginative and prescient and development reputation CVPR 2005, held in San Diego, CA, united states in June 2005. It covers the present cutting-edge in 3D imaging for defense and security.
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J. Comput. Vision, 25(1):63-85, 1997. 3D Assisted Face Recognition: A Survey 21 10. D. Colbry, X. Lu, A. Jain, and G. Stockman. 3D face feature extraction for recognition. Technical Report MSU-CSE-04-39, Michigan State University, East Lansing, Michigan, 2004. 11. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active Appearance Models. In Proc. of European Conference of Computer Vision, 2:484-498, 1998. 12. D. DeCarlo, D. Metaxas, and M. Stone. An Anthropometric Face Model using Variational Techniques.
Passive methods serve as the only alternates in this case since sometimes 2D images could be the only available data, captured by 2D sensors or scanned from identity cards, for modeling 3D face of a person. Shape from space carving has not been used for 3D modeling so far; yet we briefly present this technique for its potential in 3D face modeling. Section 5 compares face modeling techniques in terms of their performances in 3D face recognition, and discusses their accuracy and feasibility. Section 6 reviews uses of 3D face models in 3D assisted face recognition and presents a list of publicly available 3D face databases.
Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Comput. , 35(4):399-458, 2003. Chapter 2 A SURVEY ON 3D MODELING OF HUMAN FACES FOR FACE RECOGNITION S. Huq, B. Abidi, S. G. Kong, and M. Abidi Imaging, Robotics, and Intelligent Systems, The University of Tennessee, Knoxville, TN 37996-2100, USA. edu Abstract: In its quest for more reliability and higher recognition rates the face recognition community has been focusing more and more on 3D based recognition. Depth information adds another dimension to facial features and provides ways to minimize the effects of pose and illumination variations for achieving greater recognition accuracy.