Image Morphing and Warping: Application to Speech Simulation Using a Single Image
Stephen Karungaru, Takuya Akashi, Minoru Fukumi,
and Norio Akamatsu
Department of Information Science and Intelligent Systems, University of Tokushima,
2-1 Minami-Josanjima, Tokushima 770-8506, Japan
In this paper, we propose a fully automatic real time one image face gesture simulation using image morphing. Given a single image of a subject, we create several facial expressions of the face by morphing the image based on prior information stored in a data bank. The process involves the automatic detection of the control points both on the target image and the source data. The source data is a string of frames containing the desired facial expressions. A face detection neural network and a lips contour detector using edges and the SNAKES algorithm are employed to detect the face position and features. Five control points and the lips contour, for both the source and target images, are then extracted based on the facial features. Triangulation method is then used to match and warp the source image to the target image using the control points. In this experiment, using one expressionless face portrait, we create an animation to make it appear like the subject is pronouncing the five Japanese vowels. The final results shows the effectiveness of our method.
-  G. Worlberg, “Image morphing: a survey,” The Visual Computer, Vol.14, pp. 360-372, 1998.
-  A. Goshtasby, “Piecewise linear mapping functions for image registration,” Pattern Recognition, Vol.19, No.6, pp. 459-466, 1986.
-  M. Berg, M. Kreveld, M. Overmars, and O. Schwarzkorf, “Computation geometry- Algorithms and Applications,” Springer, 1997.
- T. Beier and S. Nelly, “Feature-based image metamorphosis,” Proc. of SIGGRAPH, pp. 35-42, 1992.
- D. Rurecht and H, Muller, “Free form deformation with scattered data interpolation methods,” Geometrical Modeling, Springer Verlag, pp. 267-281, 1993.
- I. Amidror, “Scattered data interpolation methods for electronics imaging systems: a survey,” Journal of Electronic Imaging, 11(2), pp. 157-176, 2002.
- S. Karungaru, M. Fukumi, and N. Akamatsu, “Genetic Algorithms Based On-line Size and Rotation Invariant Face Detection. Journal of Signal Processing,” Vol.9, No.6, pp. 497-503, 2005.
-  T. Akashi, Y. Wakasa, K. Tanaka, S. Karungaru, and M. Fukumi, “High Speed Genetic Lips Detection by Dynamic Search Domain Control,” IEEJ Transactions on Electronics, Information and Systems, Vol.127, No.6, pp. 854-866, 2007.
-  G. Nielson, H. Hager, and H. Muller, “Scientific Visualization,” IEEE, New York, p. 433, 1997.
-  Kass, A. Witkin, and D. Terzopoulos, “Snakes-Active contour model, Computer Vision,” Vol.1, No.3, pp. 321-331, 1988.
- T. McInerney and D. Terzopoulos, “Deformable models in medical image analysis: A survey,” Medical Image Analysis 1(2), pp. 91-108, 1996.
- A. K. Jain, Y. Zhong, and M.-P. Dubuisson-Jolly, “Deformable template models: A review,” Signal Processing 71(2), pp. 109-129, 1998.
- T. Sakaguchi, K. Oyama, “SNAKE with the area term,” Proc. of IEICE General conf., D-555, 1991 (in Japanese).
- M. Soriano, E. Marszalec, and M. Pietikainen, “Color correction of face images under different illuminants by RGB eigenfaces,” Proc. 2nd Audio- and Video-Based Biometric Person Authentication Conf., pp. 148-153, 1999.