Browse > Article

Building a 3D Morphable Face Model using Finding Semi-automatic Dense Correspondence  

Choi, In-Ho (포항공과대학교 컴퓨터공학과)
Cho, Sun-Young (KT 미래기술연구소)
Kim, Dai-Jin (포항공과대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.14, no.7, 2008 , pp. 723-727 More about this Journal
Abstract
2D face analysis has some limitations which are pose and illumination sensitive. For these reasons, even if many researchers try to study in the 3D face analysis and processing, because of the low computing performance and the absence of a high-speed 3D scanner then a lot of research is not being able to proceed. But, due to improving of the computing performance in these days, the advanced 3D face research was now underway. In this paper, we propose the method of building a 3D face model which deal successfully with dense correspondence problem.
Keywords
Face analysis; Face model; 3D face; Morphable model;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Romdhani and T. Vetter, 'Efficient, Robust and Accurate Fitting of a 3D Morphable Model,' Proc. of the IEEE International Conf. on Computer Vision, pp. 59-66, 2003
2 BJUT, 'BJUT-3D Face Database,' Beijing University of Technology
3 V. Blanz and T. VetterR, 'A Morphable Model For The Synthesis Of 3D Faces,' Proc. of the 26th annual Conf. Computer graphics and interactive techniques, pp. 187-194, 1999
4 P. N. Belhumeur and D. J. Kriegman, 'What is the set of Images of an Object Under All possible Lighting Conditions?,' Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 270-277, 1996
5 X. Tan, S. Chen, Z. Zhou and F. Zhang, 'Face recognition from a single image per person: A survey,' Pattern Recognition, Vol.39, No.9, pp. 1725-1745, 2006   DOI   ScienceOn
6 M. J. Jones and T. Poggio, 'Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes,' International Journal of Computer Vision, Vol.2, No.29, pp. 107-131, 1998
7 B. K. P. Horrn and B. G. Schunck, 'Determining Optical Flow,' Artificial Intelligence, Vol.17, pp. 185-203, 1981   DOI   ScienceOn
8 T. Vetter, M.J. Jones and T. Poggio, 'A bootstrapping algorithm for learning linear models of object classes,' Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 40-46, 1997
9 D. Beymer, 'Face recognition under varying pose,' Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 756-761, 1994