CFP last date
20 December 2024
Reseach Article

An Iterative Search based Technique to Find or Predict Older Face Images of a Child

by Rustam Ali Ahmed, Bhogeswar Borah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 6
Year of Publication: 2016
Authors: Rustam Ali Ahmed, Bhogeswar Borah
10.5120/ijca2016911832

Rustam Ali Ahmed, Bhogeswar Borah . An Iterative Search based Technique to Find or Predict Older Face Images of a Child. International Journal of Computer Applications. 151, 6 ( Oct 2016), 1-6. DOI=10.5120/ijca2016911832

@article{ 10.5120/ijca2016911832,
author = { Rustam Ali Ahmed, Bhogeswar Borah },
title = { An Iterative Search based Technique to Find or Predict Older Face Images of a Child },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 6 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume151/number6/26234-2016911832/ },
doi = { 10.5120/ijca2016911832 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:56:20.772013+05:30
%A Rustam Ali Ahmed
%A Bhogeswar Borah
%T An Iterative Search based Technique to Find or Predict Older Face Images of a Child
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 6
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The major variations in the appearance of human faces is because of age changes. Due to many lifestyle issues, it is difficult to precisely predict how individuals may look in older years. This work aims to develop a technique for predicting older face images from a given childrens face image. This method requires only one input face image of a child and produces different age progressed images of the child at different target ages. This technique might be very helpful to find the missing children. In this method we have proposed a technique to find or construct a synthesize older face images from a given face image dataset. In the proposed work the FG-NET image dataset has been classified with different age groups of face images. Age groups are named by AgeGroup IDs 10-14, 15-19,. . . , 50-54. For a given child image we have applied an iterative approach to find the face images in higher age groups. At the first step an input of a child image of age that is below of the first age group has been taken and searched that image in the face dataset of higher age group. If the face is found, then the founded image is considered as the target image at that age group and that new face is searched in the next higher aged group data set. If it is not found, then a synthesized mean image is constructed with the input image and the founded nearest image. The same technique is repeated until the construction of the oldest (of age 50-60) synthesized image computation is completed. Here age group 50-60 has been considered as the oldest image in the experiment. In this way the older images of all the respective age groups can be found. Here PCA face recognition algorithm is used for searching an image from a given dataset.

References
  1. National center for mission and exploited children. http://www.missingkids.com. Technical report.
  2. S. Lai A. J. Lin and F. Cheng. Growth simulation of facial/ head model from childhood to adulthood. Computer- Aided Design and Applications, 7(5):777786, 2010.
  3. K. Ricanek A. K. Albert and E. Patterson. A review of the literature on the aging adult skull and face: Implications for forensic science research and applications. Forensic Science International, 172(1):1–9, 2007.
  4. C.J. Taylor A. Lanitis and T.F. Cootes. Automatic identification and coding of human faces using flexible models. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):743– 756, July 1997.
  5. C.J. Taylor A. Lanitis and T.F. Cootes. Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:442 – 455, April 2002.
  6. Rustam Ali Ahmed and Bhogeswar Borah. Triangle wise mapping technique to transform one face image into another face image. International Journal of Computer Applications, 87(6):1–8, February 2014.
  7. K. Ariyarathne and A. T. Dharmaratne. Age related morphing progression of young faces. in International Conference on Machine Vision, pages 28–30, 2010.
  8. C. J. Solomon C. M. Hill and S. J. Gibson. Aging the human face - a statistically rigorous approach. pages 89–94, 2005.
  9. et al Cheng-TA S. 3d age progression prediction in children’s faces with a small exemplar-image set. Journal of Information Science & Engineering, 30(4):1131–1148, 2014.
  10. L. G. Farkas. Anthropometry of the head and face, raven press. New York, 1994.
  11. Yun Fu, Guodong Guo, and Thomas S Huang. Age synthesis and estimation via faces: A survey. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(11):1955–1976, 2010.
  12. M. R. Gandhi. A method for automatic synthesis of aged human facial images. Masters thesis, McGill University, 2005.
  13. C.J. Taylor G.J. Edwards, A. Lanitis and T.F. Cootes. Statistical face models: lmproving specificity. Image and Vision Computing, 16(3):203–211, 1998.
  14. S. C. Zhu S. Shan J. L. Suo, F. Min and X. Chen. A multiresolution dynamic model for face aging simulation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1–8, 2007.
  15. S. Shan J. L. Suo, S. C. Zhu and X. Chen. A compositional and dynamic model for face aging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2):385401, 2010.
  16. S. Shan J. L. Suo, X. Chen and W. Gao. Learning long term face aging patterns from partially dense aging databases. Proceedings of International Conference on Computer Vision, page 622629, 2009.
  17. T. D. Bui K. Luu, C. Y. Suen and K. Ricanek Jr. Automatic child-face age-progression based on heritability factors of familial faces. IEEE International Conference on Biometrics, Identity and Security, 200:1–6.
  18. H. P. Seidel K. Scherbaum, M. Sunkel and V. Blanz. Prediction of individual non-linear aging trajectories of faces. Computer Graphics Forum In EUROGRAPHICS, 26:285294, 2007.
  19. Andreas Lanitis. Comparative evaluation of automatic ageprogression methodologies. EURASIP J. Adv. Signal Process, 2008:101:1–101:10, January 2008.
  20. Narayanan Ramanathan, Rama Chellappa, and Soma Biswas. Computational methods for modeling facial aging: A survey. J. Vis. Lang. Comput., 20(3):131–144, June 2009.
  21. M. G. Rhodes. Age estimation of faces: A review. Applied Cognitive Psychology, 23:1–12, 2009.
  22. Z. H. Zhou X. Geng and K. S. Miles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12):22342240, 2007.
  23. H. Yue Y. Liang, C. Li and Y. Luo. Age simulation in young face images. International Conference on Bioinformatics and Biomedical Engineering, page 494497, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Synthesize Image Age Progression Future Image Prediction Face Image Modeling Missing Children