CFP last date
21 October 2024
Reseach Article

Triangle Wise Mapping Technique to Transform one Face Image into Another Face Image

by Rustam Ali Ahmed, Bhogeswar Borah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 6
Year of Publication: 2014
Authors: Rustam Ali Ahmed, Bhogeswar Borah
10.5120/15209-3714

Rustam Ali Ahmed, Bhogeswar Borah . Triangle Wise Mapping Technique to Transform one Face Image into Another Face Image. International Journal of Computer Applications. 87, 6 ( February 2014), 1-8. DOI=10.5120/15209-3714

@article{ 10.5120/15209-3714,
author = { Rustam Ali Ahmed, Bhogeswar Borah },
title = { Triangle Wise Mapping Technique to Transform one Face Image into Another Face Image },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 6 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number6/15209-3714/ },
doi = { 10.5120/15209-3714 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:10.837774+05:30
%A Rustam Ali Ahmed
%A Bhogeswar Borah
%T Triangle Wise Mapping Technique to Transform one Face Image into Another Face Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 6
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes a triangle based algorithm to transform a source image into a target image. In this paper, a digital source face image is mapped to a target face image to transform the shape of source into the target. This work is done with six steps. The initial step of the stated algorithm takes source and destination face images as input from the specified location or through the webcam. The second step deals with finding the 68 landmark points of input images for tracking the features such as eyes, mouth, nose, lips, ears and face. The third step generates proposed 116 nos of nonoverlapping triangles from 68 landmark points (which has been found in the second step) for both the input face images. In forth step one mapping link is established between the each pair of corresponding proposed 116 triangles of both the images. Then these pairs of triangles are divided on the basis of given threshold value of the in-radius of the triangles pairs. In this step a set of smallest subtriangles are found in the last label for the triangle pairs. In fifth step each pair of smallest sub-triangles are mapped with pixels from source face image to destination face image and then generate intermediate image with color interpolation. The sixth step is the process of assembling the 116 nos of triangles to generate the resultant face image in the shape of target face image. The results show that the proposed approach is simple and takes less time to transform the source image.

References
  1. Nur Arad and Daniel Reisfeld. Image warping using few anchor points and radial functions. Computer Graphics Forum, 14:35–46, 1995.
  2. Thaddeus Beier and Shawn Neely. Feature-based image metamorphosis. SIGGRAPH Comput. Graph. , 26(2):35–42, July 1992.
  3. Urvashi Bhushan, G. P. Saroha, and Disha Tiwari. An implementation of image morphing through mesh morphing algorithm. International Journal of Advanced Research in Computer Science and Software Engineering, 2(7), 2012.
  4. Martin Bichsel. Automatic interpolation and recognition of face images by morphing. In Proceedings of the International Conference on Automatic Face and Gesture Recognition, ICAFGR 96, pages 128–135, 1996.
  5. Timothy C. Faltemier, KevinW. Bowyer, and Patrick J. Flynn. Rotated profile signatures for robust 3d feature detection. In FG, pages 1–7. IEEE, 2008.
  6. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence, 23(6):643–660, 2001.
  7. Yanwen Guo, Guiping Zhang, Zili Lan, and Wenping Wang. Efficient view manipulation for cuboid-structured images. Computers Graphics, (0):–, 2013.
  8. Aaron W. F. Lee, David Dobkin, Wim Sweldens, and Peter Schrder. Multiresolution mesh morphing. In PROCEEDINGS OF SIGGRAPH 99, pages 343–350, 1999.
  9. Seung-Yong Lee, Kyung-Yong Chwa, James Hahn, and Sung Yong Shin. Image morphing using deformation techniques. 7(1):3–24, January 1996.
  10. Seung-Yong Lee, Kyung-Yong Chwa, and Sung Yong Shin. Image metamorphosis using snakes and free-form deformations. In Proceedings of the 22Nd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH '95, pages 439–448, New York, NY, USA, 1995. ACM.
  11. Seungyong Lee, George Wolberg, Kyung yong Chwa, and Sung Yong Shin. Image metamorphosis with scattered feature constraints. IEEE Transactions on Visualization and Computer Graphics, 2:337–354, 1996.
  12. T. H. Lin, W. P. Shih, W. C. Chen, and W. Y. Ho. 3d face authentication by mutual coupled 3d and 2d feature extraction. In Proceedings of the 44th Annual Southeast Regional Conference, ACM-SE 44, pages 423–427, New York, NY, USA, 2006. ACM.
  13. Peter Litwinowicz and Lance Williams. Animating images with drawings. In SIGGRAPH, pages 409–412. ACM, 1994.
  14. Ajmal S. Mian, Mohammed Bennamoun, and Robyn Owens. An efficient multimodal 2d-3d hybrid approach to automatic face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007:1584–1601, 2007.
  15. Prathap Nair and Andrea Cavallaro. 3-d face detection, landmark localization, and registration using a point distribution model. IEEE Transactions on Multimedia, 11(4):611–623, 2009.
  16. P. Perakis, G. Passalis, T. Theoharis, and I. A. Kakadiaris. 3d facial landmark detection under large yaw and expression variations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7):1552–1564, 2013.
  17. Yan Ren, Shuang Wang, Biao Hou, and Jingjing. Efficient view manipulation for cuboid-structured images. IEEE Transactions On Image Processing, 23(1), January 2014.
  18. Marcelo Romero and Nick Pears. Landmark localisation in 3d face data. In Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS '09, pages 73–78, Washington, DC, USA, 2009. IEEE Computer Society.
  19. Albert Ali Salah and Lale Akarun. 3d facial landmarking under expression, pose, and occlusion variations, 2008.
  20. Xiaozhou Wei, Peter Longo, and Lijun Yin. Automatic facial pose determination of 3d range data for face model and expression identification. In Seong-Whan Lee and Stan Z. Li, editors, ICB, volume 4642 of Lecture Notes in Computer Science, pages 144–153. Springer, 2007.
  21. GeorgeWolberg. Digital ImageWarping. IEEE Computer Society Press, Los Alamitos, CA, USA, 1st edition, 1994.
  22. George Wolberg. Recent advances in image morphing. In Proceedings of the 1996 Conference on Computer Graphics International, CGI '96, pages 64–, Washington, DC, USA, 1996. IEEE Computer Society.
  23. T. Yu and Y. Moon. A novel genetic algorithm for 3d facial landmark localization. Biometrics: Theory, Applications, and Systems, 2008.
  24. Vittorio Zanella and Olac Fuentes. An approach to automatic morphing of face images in frontal view. In Raul Monroy, Gustavo Arroyo-Figueroa, Luis Enrique Sucar, and Juan Humberto Sossa Azuela, editors, MICAI, volume 2972 of Lecture Notes in Computer Science, pages 679–687. Springer, 2004.
  25. Xiangxin Zhu and Deva Ramanan. Face detection, pose estimation, and landmark localization in the wild. In CVPR, pages 2879–2886. IEEE, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Image morphing warping triangle wise mapping mesh deformation triangular mesh deformation