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Reseach Article

Face Aging Simulation

by Pratik Waghela, Aadil Contractor, Jaya Chaudhary, Ruhina Karani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 11
Year of Publication: 2015
Authors: Pratik Waghela, Aadil Contractor, Jaya Chaudhary, Ruhina Karani
10.5120/ijca2015907474

Pratik Waghela, Aadil Contractor, Jaya Chaudhary, Ruhina Karani . Face Aging Simulation. International Journal of Computer Applications. 131, 11 ( December 2015), 32-35. DOI=10.5120/ijca2015907474

@article{ 10.5120/ijca2015907474,
author = { Pratik Waghela, Aadil Contractor, Jaya Chaudhary, Ruhina Karani },
title = { Face Aging Simulation },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 11 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number11/23496-2015907474/ },
doi = { 10.5120/ijca2015907474 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:27:04.568289+05:30
%A Pratik Waghela
%A Aadil Contractor
%A Jaya Chaudhary
%A Ruhina Karani
%T Face Aging Simulation
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 11
%P 32-35
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face aging is one of the most challenging task in image processing and is commonly used in many areas. This paper consists of compositional method which will represent faces of different age groups. The representation of faces in different age group is done hierarchically i.e. using And-Or graph, in which the And nodes will decompose the face into different components (e.g. wrinkle, hair) for crucial age perception and the Or node will represent the diversities of faces. The graph is then represented using Markov chain. The diversities or the uncertainties of the faces are learnt from the large database which consists of large number of images. There are two criteria for evaluating the result the aging simulation and one of them is the accuracy of the simulation i.e. whether the perceived image belongs to a particular age group, and second is the preservation of the identity i.e. whether the face, that is retrieved after simulation process is preserving the identity of the person or not. The statistical analysis of these two above mentioned criteria will decide the performance of the aging simulation.

References
  1. Wei Shen , Zhenjiang Miao “Face Aging Simulation Based On Image Warping”
  2. Mrs. G. Deepthi Research Scholar, B.Vijaya Babu Professor, Mrs. Vidyullatha Pellakuri, D.Rajeswara Rao “An Overview on Face Aging Graphical Models: Compositional Dynamic Model and Concatenational Graph Evolution Model”
  3. Junyan Wang, Yan Shang, Guangda Su, Xinggang Lin “Age simulation for face recognition”
  4. Jinli Suo , Song-Chun Zhu , Shiguang Shan and Xilin Chen “A Compositional and Dynamic Model for Face Aging”
  5. Jinli Suo , Song-Chun Zhu , Shiguang Shan and Xilin Chen “A Multi-Resolution Dynamic Model for Face Aging Simulation”
  6. Junyan Wang, Yan Shang, Guangda Su, Xinggang Lin “Age simulation for face recognition”
  7. M. Gandhi, ”A method for automatic synthesis of aged human facial images”, Master’s thesis, McGill University,2004
  8. Y. Bando, T. Kuratate, and T. Nishita, “A Simple Method for Modeling Wrinkles on Human Skin,” Proc. 10th Pacific Conf. Computer Graphics and Applications, pp. 166-175, 2002
  9. L. Boissieux, G. Kiss, N.M. Thalmann, and P. Kalra, “Simulation of Skin Aging and Wrinkles with Cosmetics Insight,” Proc. Eurographics Workshop Animation Computer Animation and Simulation, pp. 15-27, 2000.
  10. X. Geng, Z. Zhou, and K. Smith-Miles, “Automatic Age Estimation Based on Facial Aging Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2234-2240, Dec. 2007.
  11. C.M. Hill, C.J. Solomon, and S.J. Gibson, “Aging the Human Face—A Statistically Rigorous Approach,” Proc. IEE Symp. Imaging for Crime Detection and Prevention, pp. 89-94, June 2005.
  12. Y.H. Kwon and N.D.V. Lobo, “Age Classification from Facial Images,” Computer Vision and Image Understanding, vol. 74, no. 1,pp. 1-21, Apr. 1999.
  13. A. Lanitis, C.J. Taylor, and T.F. Cootes, “Toward Automatic Simulation of Aging Effects on Face Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 442-455, Apr. 2002.
  14. N. Ramanathan and R. Chellappa, ”Modeling shape and textural variations in aging faces,” Proc. Eighth Int’l Conf. Automatic Face and Gesture Recognition, 2008.
  15. D. DeCarlo, D. Metaxas and M. Stone, ”An anthropometric face model using variational techniques,” Proc. Twenty-fifth Int’l Conf. Computer Graphics and Interactive Techniques, pp. 67-74, 1998.
  16. A. Lanitis, ”Comparative evaluation of automatic age-progressionmethodologies,” EURASIP Journal on Advances in Signal Processing, vol. 8, no. 2, Jan. 2008.
  17. A. Lanitis, C. Dragonova and C. Christondoulou, ”Comparing different classifiers for automatic age estimation,” IEEE Trans Systems, Man and Cybernetics, Part B, vol. 34, no. 1, pp. 621-628,Feb. 2004.
  18. F. R. Leta, A. Conci, D. Pamplona and I. Itanguy, ”Manipulating facial appearance through age parameters,” Proc. Ninth Brazilian Symposium on Computer Graphics and Image Processing, pp. 167-172, 1996.
Index Terms

Computer Science
Information Sciences

Keywords

And-Or Graph Aging modeling ANOVA.