Face 3D Modeling Based on Projective Rectification

Article Preview

Abstract:

Face 3D modeling is the difficulty problem in the field of computer graphics, computer vision and artificial intelligence. In recent years, it has become the most active research focus both at home and broad. 3D modeling of face is the key step to realize face recognition, and the technique of face 3D modeling has obtained extensive applications in many fields, such as film, animation, interactive games, video conference, human-computer interaction, reverse engineering, medical and public safety. In this paper, the technology of face 3D modeling based on projective rectification is presented and the reconstruction of face 3D digital model can be achieved by it. Firstly, BP neural network is used to simulate the mapping relationship between the 3D object and its images. Then, the rectification on the left and right images acquired by stereo vision system is implemented according to the principle of epipolar line constraint. On the left and right rectified image planes, the match researching of corresponding points are reduced from 2D plane to the horizontal lines, so the image matching and face 3D modeling can be implemented efficiently.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

181-186

Citation:

Online since:

August 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] S. Okada, M. Imade and H. Miyauchi: Proceedings of the 24th Annual Conference of the IEEE, Vol. 3(1998), pp.1284-1289.

Google Scholar

[2] R. Rodella and G. Sansoni: 3-D Digital Imaging and Modeling, Proceedings, Second International Conference, 1999, pp.77-83.

Google Scholar

[3] D.R. Shan, Y.L. Ke and Y.F. Liu: China Mechanical Engineering, Vol. 14(2003) No. 1, pp.9-12. (In Chinese).

Google Scholar

[4] X. Guo, D.M. Peng and J.S. Zu: Aviation Precision Manufacturing Technology, Vol. 40(2004) No. 5, pp.10-13. (In Chinese).

Google Scholar

[5] H. Pan and G. Guo: Computer Measurement and Control, Vol. 12(2004) No. 12, pp.1121-1124. (In Chinese).

Google Scholar

[6] J.G. Wang and Y.F. Li: Instrumentation and Measurement Technology Conference, Proceedings of the 16th IEEE, Vol. 2(1999), pp.684-689.

Google Scholar

[7] S.D. Ma and Z.Y. Zhang: Computer Vision-Fundamentals of Computational Theory and Algorithms (Science Press, China, 1998), pp.78-80.

Google Scholar

[8] R I. Hartley: Int. J. Computer Vision, Vol. 35(1999) No. 2, pp.1-16.

Google Scholar

[9] C. Loop and Z.Y. Zhang: Proceedings of IEEE conf. on Computer Vision and Pattern Recognition, Vol. 1(1999), pp.125-131.

Google Scholar

[10] I. Francesco and E. Trucco: Proceedings of IEEE conf. on Computer Vision and Pattern Recognition, Vol. 1(1999), pp.94-99.

Google Scholar

[11] G.Q. Wei and S.D. Ma: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16(1994) No. 5, pp.469-480.

Google Scholar

[12] T. H Martin, B.D. Howard and H.B. Mark: Neural Network Design (Mechanical Industry Press, China, 2002), pp.197-222. (In Chinese).

Google Scholar