Skip to main content
SpringerLink
Log in
Book cover

Advances in Computational Intelligence Systems pp 465–479Cite as

On Facial Age Progression Based on Modified Active Appearance Models with Face Texture

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 513))

Abstract

Age progression that involves the reconstruction of facial appearance with a natural ageing effect has several applications. These include the search for missing people and identification of fugitives. The majority of age progression methods reported in the literature are data driven. Hence, such methods learn from training data and utilise statistical models such as 3D morphable models and active appearance models (AAM). Principal component analysis (PCA) which is a vital part of these models has an unfortunate drawback of averaging out texture details. Therefore, they work as a low pass filter and as such many of the face skin deformations and minor details become faded. Interestingly, recent work in 2D and 3D animation has shown that patches of the human face are somewhat similar when compared in isolation. Thus, researchers have proposed generating novel faces by compositing small face patches, usually from large image databases. Following these ideas, we propose a novel age progression model which synthesises aged faces using a hybrid of these two techniques. First, an invertible model of age synthesis is developed using AAM and sparse partial least squares regression (sPLS). Then the texture details of the face are enhanced using the patch-based synthesis approach. Our results show that the hybrid algorithm produces both unique and realistic images. Furthermore, our method demonstrates that the identity and ageing effects of subjects can be more emphasised.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Fu, Y., Guo, G., Huang, T.: Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1955–1976 (2010)

    Article  Google Scholar 

  2. Ramanathan, N., Chellappa, R.: Modeling age progression in young faces. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 387–394 (2006). doi:10.1109/CVPR.2006.187

  3. Suo, J., Zhu, S.-C., Shan, S., Chen, X.: A compositional and dynamic model for face aging. IEEE Trans. Pattern Anal. Mach. Intell. 32, 385–401 (2010). doi:10.1109/TPAMI.2009.39

    Article  Google Scholar 

  4. Bukar, A.M., Ugail, H., Connah, D.: Individualised model of facial age synthesis based on constrained regression. In: 2015 International Conference on Image Process. Theory, Tools Applications (IPTA), pp. 285–290. IEEE (2015)

    Google Scholar 

  5. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Computer Vision—ECCV’98, pp. 484–498 (1998). http://link.springer.com/chapter/10.1007/BFb0054760

  6. Wei, L., Turk, G.: State of the art in example-based texture synthesis (2009)

    Google Scholar 

  7. Dessein, A., Smith, W.A.P., Wilson, R.C., Hancock, E.R.: Example-based modeling of facial texture from deficient data, (n.d.), pp. 3898–3906. doi:10.1109/ICCV.2015.444

  8. Taylor, K.T.: Forensic Art and Illustration. CRC Press, Boca Raton, Florida (2001)

    Google Scholar 

  9. Thompson, D.W.: On Growth and Form (1942)

    Google Scholar 

  10. Benson, P.J., Perrett, D.I.: Synthesising continuous-tone caricatures. Image Vis. Comput. 9, 123–129 (1991)

    Article  Google Scholar 

  11. Hsu, R.-L., Jain, A.K.: Generating discriminating cartoon faces using interacting snakes. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1388–1398 (2003)

    Article  Google Scholar 

  12. Mukaida, S., Ando, H.: Extraction and manipulation of wrinkles and spots for facial image synthesis. In: Sixth IEEE International Conference on Automatic Face Gesture Recognition, 2004. Proceedings, pp. 749–754 (2004). doi:10.1109/AFGR.2004.1301624

  13. Shan, Y., Liu, Z., Zhang, Z.: Image-based surface detail transfer. IEEE Comput. Graph. Appl. 24, 30–35 (2004)

    Article  Google Scholar 

  14. Mehdi, A., Qahwaji, R., Ugail, H., Abdullah, M.: Construction of 3D facial wrinkles using splines. In: Fourth International Conference on Information Technology, Jordan (2009)

    Google Scholar 

  15. Burt, M., Perrett, D.: Perception of age in adult Caucasian male faces: computer graphic manipulation of shape and colour information. Proc. R. Soc. Lond. Ser. B Biol. Sci. 137–143 (1995)

    Google Scholar 

  16. Kemelmacher-Shlizerman, I., Suwajanakorn, S., Seitz, S.M.: Illumination-aware age progression. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3334–3341 (2014). doi:10.1109/CVPR.2014.426

  17. Lanitis, A., Taylor, C., Cootes, T.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24, 442–455 (2002)

    Article  Google Scholar 

  18. Scandrett, C.M., Solomon, C.J., Gibson, S.J.: A person-specific, rigorous aging model of the human face. Pattern Recognit. Lett. 27, 1776–1787 (2006)

    Article  Google Scholar 

  19. Patterson, E., Sethuram, A., Albert, M., Ricanek, K.: Comparison of synthetic face aging to age progression by forensic sketch artist. In: International Conference Vision Imaging, Image Process, Palma de Mallorca, Spain, pp. 247–252 (2007)

    Google Scholar 

  20. Mohammed, U., Prince, S.J., Kautz, J.: Visio-lization: generating novel facial images. ACM Trans. Graph. 28, 57 (2009)

    Article  Google Scholar 

  21. Gower, J.C.: Generalized procrustes analysis. Psychometrika 40, 33–51 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  22. Ohta, Y.-I., Kanade, T., Sakai, T.: Color information for region segmentation. Comput. Graph. Image Process. 13, 222–241 (1980)

    Article  Google Scholar 

  23. Panis, G., Lanitis, A, Tsapatsoulis, N., Cootes, T.F.: Overview of research on facial ageing using the FG-NET ageing database. IET Biometrics (2015)

    Google Scholar 

  24. Chun, H.: Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection with Applications to High Dimensional Genomic Data. ProQuest (2008)

    Google Scholar 

  25. Li, J., Lu, B.-L.: An adaptive image Euclidean distance. Pattern Recognit. 42, 349–357 (2009). doi:10.1016/j.patcog.2008.07.017

    Article  MATH  Google Scholar 

  26. Vieira, T.F., Bottino, A., Laurentini, A., De Simone, M.: Detecting siblings in image pairs. Vis. Comput. 30, 1333–1345 (2014)

    Article  Google Scholar 

  27. Dalrymple, K.A., Gomez, J., Duchaine, B.: The dartmouth database of children’s faces: acquisition and validation of a new face stimulus set. PLoS One. 8, 1–7 (2013). doi:10.1371/journal.pone.0079131

  28. Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. 22, 313 (2003). doi:10.1145/882262.882269

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Visual Computing, University of Bradford, Richmond Road, Bradford, BD7 1DP, UK

    Ali Maina Bukar, Hassan Ugail & Nosheen Hussain

Authors
  1. Ali Maina Bukar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hassan Ugail
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nosheen Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Maina Bukar .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg School of Computing and Communications, Lancaster, United Kingdom

    Plamen Angelov

  2. School of Computing, University of Portsmouth School of Computing, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  3. School of Comp. Sci. & Digital Media, Robert Gordon University School of Comp. Sci. & Digital Media, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth University Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth, United Kingdom

    Qiang Shen

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bukar, A.M., Ugail, H., Hussain, N. (2017). On Facial Age Progression Based on Modified Active Appearance Models with Face Texture. In: Angelov, P., Gegov, A., Jayne, C., Shen, Q. (eds) Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46562-3_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46561-6

  • Online ISBN: 978-3-319-46562-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation