A Nonlinear Appearance Model for Age Progression

Part of the Studies in Computational Intelligence book series (SCI, volume 730)


Recently, automatic age progression has gained popularity due to its numerous applications. Among these is the search for missing people, in the UK alone up to 300,000 people are reported missing every year. Although many algorithms have been proposed, most of the methods are affected by image noise, illumination variations, and most importantly facial expressions. To this end we propose to build an age progression framework that utilizes image de-noising and expression normalizing capabilities of kernel principal component analysis (Kernel PCA). Here, Kernel PCA a nonlinear form of PCA that explores higher order correlations between input variables is used to build a model that captures the shape and texture variations of the human face. The extracted facial features are then used to perform age progression via a regression procedure. To evaluate the performance of the framework, rigorous tests are conducted on the FGNET ageing database. Furthermore, the proposed algorithm is used to progress image of Mary Boyle; a 6-year-old that went missing over 39 years ago, she is considered Ireland’s youngest missing person. The algorithm presented in this paper could potentially aid, among other applications, the search for missing people worldwide.


Age progression Age synthesis Kernel appearance model Linear regression Kernel PCA Kernel preimage Mary Boyle 


  1. 1.
    Fu, Y., Guo, G., Huang, T.: Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955–1976 (2010)CrossRefGoogle Scholar
  2. 2.
    Zeng, Z., Pantic, M., Roisman, G., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. Pattern Anal. Mach. Intell. IEEE Trans. 31(1), 39–58 (2009)CrossRefGoogle Scholar
  3. 3.
    Zhao, Q., Rosenbaum, K., Okada, K., Zand, D.J., Sze, R., Summar, M., Linguraru, M.G.: Automated down syndrome detection using facial photographs. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 3670–3673 (2013)Google Scholar
  4. 4.
    Ramanathan, N., Chellappa, R.: Modeling age progression in young faces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 387–394 (2006)Google Scholar
  5. 5.
    Panis, G., Lanitis, A., Tsapatsoulis, N., Cootes, T.F.: Overview of research on facial ageing using the FG-NET ageing database. IET Biometrics 5(2), 37–46 (2016)CrossRefGoogle Scholar
  6. 6.
    Fyfe, N.R., Stevenson, O., Woolnough, P.: Missing persons: the processes and challenges of police investigation. Polic. Soc. 25(4), 409–425 (2015)CrossRefGoogle Scholar
  7. 7.
    Machado, H., Santos, F.: The disappearance of Madeleine McCann: Public drama and trial by media in the Portuguese press. Crime Media Cult. 5(2), 146–167 (2009)CrossRefGoogle Scholar
  8. 8.
    Needham, K.: Ben. Ebury Publishing, London (2013)Google Scholar
  9. 9.
    Haber, J., Terzopoulos, D.: Facial modeling and animation. In: ACM SIGGRAPH 2004 Course Notes, p. 6 (2004)Google Scholar
  10. 10.
    Lanitis, A., Taylor, C., Cootes, T.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)CrossRefGoogle Scholar
  11. 11.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. 23(6), 681–685 (2001)Google Scholar
  12. 12.
    Patterson, E., Sethuram, A., Albert, M., Ricanek, K.: Comparison of synthetic face aging to age progression by forensic sketch artist. In: International Conference on Visualization, Imaging, and Image Processing, pp. 247–252 (2007)Google Scholar
  13. 13.
    Ramanathan, N., Chellappa, R., Biswas, S.: Age progression in human faces: a survey. J. Vis. Lang. Comput. 15(1), 3349–3361 (2009)Google Scholar
  14. 14.
    Kono, H., Genda, E.: Wrinkle generation model for 3d facial expression. In: ACM SIGGRAPH 2003 Sketches & Applications, p. 1 (2003)Google Scholar
  15. 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. pp. 137–143 (1995)Google Scholar
  16. 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, June 2014Google Scholar
  17. 17.
    Geng, X., Zhou, Z.-H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2234–2240 (2007)CrossRefGoogle Scholar
  18. 18.
    Bukar, A.M., Ugail, H., Connah, D.: Individualised model of facial age synthesis based on constrained regression. In: 2015 5th International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 285–290 (2015)Google Scholar
  19. 19.
    Wang, W., Cui, Z., Yan, Y., Feng, J., Yan, S., Shu, X., Sebe, N.: Recurrent face aging. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  20. 20.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: In Computer Vision—ECCV’98, pp. 484–498 (1998)Google Scholar
  21. 21.
    Lanitis, A.: Evaluating the performance of face-aging algorithms. In: IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–6 (2008)Google Scholar
  22. 22.
    Christoudias, C.M., Darrell, T.: On modelling nonlinear shape-and-texture appearance manifolds. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1067–1074 (2005)Google Scholar
  23. 23.
    Gao, X., Su, Y., Li, X., Tao, D.: A review of active appearance models. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(2), 145–158 (2010)Google Scholar
  24. 24.
    Aizerman, M.A., Braverman, E.M., Rozonoer, L.I.: Theoretical foundations of the potential function method in pattern recognition. Autom. Remote Control 25, 917–936 (1964)MATHGoogle Scholar
  25. 25.
    Honeine, P., Richard, C.: Preimage problem in kernel-based machine learning. IEEE Signal Proces. Mag. 28(2), 77–88 (2011)CrossRefGoogle Scholar
  26. 26.
    Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1996)CrossRefGoogle Scholar
  27. 27.
    Li, J.-B., Chu, S.-C., Pan, J.-S.: Kernel Learning Algorithms for Face Recognition. Springer, New York (2014)CrossRefMATHGoogle Scholar
  28. 28.
    Honeine, P., Richard, C.: A closed-form solution for the pre-image problem in kernel-based machines. J. Signal Proces. Syst. 65(3), 289–299 (2011)CrossRefGoogle Scholar
  29. 29.
    Mika, S., Schölkopf, B., Smola, A., Müller, K., Scholz, M., Rätsch, G.: Kernel PCA and de-noising in feature spaces. Adv. Neural. Inf. Proces. Syst. 11, 536–542 (1999)Google Scholar
  30. 30.
    Zheng, W., Lai, J., Xie, X., Liang, Y., Yuen, P.C., Zou, Y.: Kernel methods for facial image preprocessing. In: Pattern Recognition, Machine Intelligence and Biometrics, pp. 389–409. Springer (2011)Google Scholar
  31. 31.
    Kabanikhin, S.I.: Definitions and examples of inverse and ill-posed problems. J. Inverse Ill-Posed Probl. 16(4), 317–357 (2008)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Kwok, J.T., Tsang, I.W.: The pre-image problem in kernel methods. IEEE Trans. Neural Netw. 15(6), 1517–1525 (2004)CrossRefGoogle Scholar
  33. 33.
    Abrahamsen, T.J., Hansen, L.K.: Input space regularization stabilizes pre-images for kernel pca de-noising. In: IEEE International Workshop on Machine Learning and Signal Processing. MLSP (2009)Google Scholar
  34. 34.
    Leitner, C., Pernkopf, F.: The Pre-image Problem And Kernel PCA For Speech Enhancement. Springer, Berlin Heidelberg (2011)CrossRefGoogle Scholar
  35. 35.
    Glasbey, C.A., Mardia, K.V.: A review of image-warping methods. J. Appl. Stat. 25(2), 155–171 (1998)Google Scholar
  36. 36.
    FG-NET: The Fg-Net Aging Database (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Centre for Visual Computing, University of BradfordBradfordUK

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