Abstract
The performance of human face recognition algorithms is seriously affected by two important factors: head pose and lighting condition. The effective processing of the pose and illumination variations is a vital key for improving the recognition rate. This paper proposes a novel method that can synthesize images with different head poses and lighting conditions by using a modified 3D CANDIDE model, linear vertex interpolation and NURBS curve surface fitting method, as well as a mixed illumination model. A specific Eigenface method is also proposed to perform face recognition based on a pre-estimated head pose method. Experimental results show that the quality of the synthesized images and the recognition performance are good.
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References
Chellappa, R., Wilson, C.L., Sirohey, S., SiroheyHuman, S.: Machine recognition of faces: a survey. Proceedings of the IEEE 83(5), 705–740 (1995)
Liu, D.H., Shen, L.S., Lam, K.M.: Face Recognition: A Survey. Chinese Journal of Circuits and Systems 9(2), 85–94 (2004)
Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: IEEE Computer Society Conference on CVPR, pp. 84–91 (1994)
Yan, J., Zhang, H.J.: Synthesized virtual view-based eigenspace for face recognition. In: Fifth IEEE Workshop on Applications of Computer Vision, pp. 85–90 (2000)
Georghiades, S., Belhumeur, P.N., David, J.K.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. on PAMI 23(2), 643–660 (2001)
Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Trans. on PAMI 25(9), 1–12 (2003)
Tang, L., Huang, T.S.: Automatic construction of 3D human face models based on 2D images. In: Proceedings of IEEE ICIP, vol. 10, pp. 467–470 (1996)
Chung, J.K., Huang, R.S., Lin, T.G.: 3-D Facial model estimation from single Front-view Facial Image. IEEE Trans. on CSVT 12(3), 183–192 (2002)
Siu, M., Chan, Y.H., Siu, W.C.: A robust model generation technology for model-based video coding. IEEE Trans. on CSVT 11(11), 1188–1192 (2001)
Ahlberg, J.: CANDIDE-3—An updated parameterized face, http://www.icg.isy.liu.se
Wu, L.F.: Researches on Image Retrieval Based on Face Object, PHD Thesis, Beijing University of Technology (2003)
Li, M.D., Ruan, Q.Q.: An interactive adaptation method of 3-D facial wireframe model. Chinese Journal of Image and Graphic 7A(8), 818–823 (2002)
Hearn, D., Baker, M.P.: Computer Graphic. Prentice Hall Press, Englewood Cliffs (2000)
Yan, J.: Two Methods of Displaying Realistic Three Dimensional Synthesized Human Face Graphics. Chinese Computer Engineering 24(1), 49–52 (1998)
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© 2005 Springer-Verlag Berlin Heidelberg
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Liu, Dh., Shen, Ls., Lam, Km. (2005). Image Synthesis and Face Recognition Based on 3D Face Model and Illumination Model. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_2
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DOI: https://doi.org/10.1007/11539117_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
eBook Packages: Computer ScienceComputer Science (R0)