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Reseach Article

Survey on Various Techniques for Age Progression

by Patel Mira Y, Jaymit Pandya, Nirav M. Raja
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 13
Year of Publication: 2016
Authors: Patel Mira Y, Jaymit Pandya, Nirav M. Raja
10.5120/ijca2016907976

Patel Mira Y, Jaymit Pandya, Nirav M. Raja . Survey on Various Techniques for Age Progression. International Journal of Computer Applications. 136, 13 ( February 2016), 1-5. DOI=10.5120/ijca2016907976

@article{ 10.5120/ijca2016907976,
author = { Patel Mira Y, Jaymit Pandya, Nirav M. Raja },
title = { Survey on Various Techniques for Age Progression },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 13 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number13/24210-2016907976/ },
doi = { 10.5120/ijca2016907976 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:58.224575+05:30
%A Patel Mira Y
%A Jaymit Pandya
%A Nirav M. Raja
%T Survey on Various Techniques for Age Progression
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 13
%P 1-5
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. In this paper, different age variation methods with their prospects are reviewed. These are the selected methods and techniques that had been chosen for review: Anthropometric Model, Image morphing, Image Based Surface Detail Transfer (IBSDT), aging function (AGES), Gaussian Mixture Model (GMM).

References
  1. Alexandru Vlad FECIORESCU, “IMAGE MORPHING TECHNIQUES”, Volume 5 Issue 1 JIDEG, June 2010
  2. Aashmi, Sakshi Sahni, Sakshi Saxena, “SURVEY: Techniques for Aging Problems in Face Recognition”, MIT International Journal of Computer Science and Information Technology, Vol. 4, No. 2, August 2014
  3. Y. H. Kwon and N. da Vitoria Lobo, “Age classification from facial images,” Computer Vis. Image Understand., Vol. 74, No. 1, pp. 1–21,1999.
  4. X. Geng, Z.H. Zhou, and S.M. Kate, “Automatic age estimation based on facial aging patterns,” IEEE Trans. Pattern Anal. Machine Intell, Vol. 29, No.12, pp. 2234–2240, and 2007.
  5. X. Geng, Z.H. Zhou, Y. Zhang, G. Li, and H. Dai, “Learning from facial aging patterns for automatic age estimation,” in ACM Conf. Multimedia (ACM MM’06), 2006, pp. 307–316.
  6. Y. Shan, Z. Liu and Z. Zhang, "Image-Based Surface Detail Transfer," CVPR 2001, Hawaii, Vol. II, Page(s): 794-799, Dec. 2001.
  7. Gayathri Mahalingam & Chandra Kambhamettu "Age Invariant Face Recognition Using Graph Matching" IEEE, 2010
  8. X. Geng, Z.H. Zhou, Y. Zhang, G. Li, and H. Dai, “Learning from facial aging patterns for automatic age estimation,” in ACM Conf. Multimedia (ACM MM’06), 2006, pp. 307–316.
  9. K. Ricanek, T.Tesafaye, “MORPH: A Longitudinal Image Database of Normal Adult Age- progression,” in IEEE International Conferenceon Automatic Face and Gesture, 2006.
  10. FG-NET Database, http://www.fgnet.rsunit.com/
  11. M. Burt and D.I. Perrett, “Perception of age in adult Caucasian male faces: computer graphic manipulation of shape and color information,” Journal of Royal Society, Vol. 259, pp. 137–143, February 1995.
  12. Y. Fu and T.S. Huang, “Human age estimation with regression on discriminative aging manifold,” IEEE Trans. Multimedia, to be published.
  13. L. L. Gayani Kumari, and Anuja Dharmaratne, "Age Progression for Elderly People Using Image Morphing", The International Conference on Advances in ICT for Emerging Regions, IEEE 2011
  14. K.S. Ariyarathne & A.T. Dharrnaratne, "Age Related Morphing Progression of Young Faces", Int'l Conf. in Machine Vision (ICMN 2010), Hong Kong, Dec. 2010
  15. Udeni Jayasinghe & Anuja Dharrnarathne "Matching Facial Images Using Aging Related Morphing Changes" World Academy of Science, Engineering & Technology 60, 2009
  16. P. Penev and J. Atick,”Local feature analysis: A general statistical theory for object representation,” Network: Computation in Neural Systems, 7(30: 477-500, 1996.
  17. Ojala, T., Pietikainen, M., Harwood, D.,”A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition 51-59, 1996.
  18. Ojala, T., Pietikainen, M., Maenpaa, T.,”A generalized local binary pattern operator for multi-resolution gray scale and rotation invariant texture classification,” Second International Conference on Advances in Pattern Recognition, Rio de Janeiro, Brazil (2001) 397-406.
  19. E. Patterson, A Sethuram, M. Albert, K. Ricanek, and M. King, "Aspects of age variation in facial morphology affecting biometrics," in IEEE Int Conf. on Biometrics: Theory, Applications and Systems, Crystal City, VA, 2007.
  20. G.Guo, Y.Fu, T.Huang, C.Dyer. 2008. Locally Adjusted Robust Regression for Human Age Estimation. IEEE Workshop on Application of Computer Vision.
  21. Narayana Ramanathan, Rama Chellappa, "Modeling Age Progression in Young Faces", Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’06), IEEE, 2006
Index Terms

Computer Science
Information Sciences

Keywords

Age progression Age Variation Methods Anthropometric Model Ages Image Morphing Ibsdt Gmm