Age Classification from Facial Images: Is Frontalization Necessary?

  • A. Báez-SuárezEmail author
  • C. NikouEmail author
  • J. A. Nolazco-FloresEmail author
  • I. A. KakadiarisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)


In the majority of the methods proposed for age classification from facial images, the preprocessing steps consist of alignment and illumination correction followed by the extraction of features, which are forwarded to a classifier to estimate the age group of the person in the image. In this work, we argue that face frontalization, which is the correction of the pitch, yaw, and roll angles of the headpose in the 3D space, should be an integral part of any such algorithm as it unveils more discriminative features. Specifically, we propose a method for age classification which integrates a frontalization algorithm before feature extraction. Numerical experiments on the widely used FGnet Aging Database confirmed the importance of face frontalization achieving an average increment in accuracy of 4.43%.


Image Resolution Textural Feature Facial Image Local Binary Pattern Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been funded in part by the Mexican National Council for Science and Technology (CONACYT) scholarship 328083 and by the UH Hugh Roy and Lillie Cranz Cullen Endowment Fund. The authors acknowledge the use of the Maxwell/Opuntia Cluster and the support of the Center of Advanced Computing and Data Systems at the University of Houston to carry out the research presented herein. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the sponsors.


  1. 1.
    Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.: Face verification across age progression using discriminative methods. IEEE Trans. Inf. Forensics Secur. 5, 82–91 (2010)CrossRefGoogle Scholar
  2. 2.
    Geng, X., Yin, C., Zhou, Z.H.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2401–2412 (2013)CrossRefGoogle Scholar
  3. 3.
    Shu, X., Xie, G.S., Li, Z., Tang, J.: Age progression: current technologies and applications. Neurocomputing 208, 249–261 (2016)CrossRefGoogle Scholar
  4. 4.
    Kwon, Y., da Vitoria Lobo, N.: Age classification from facial images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, pp. 762–767 (1994)Google Scholar
  5. 5.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Proceedings of European Conference on Computer Vision, Freiburg, Germany, vol. 2, pp. 484–498 (1998)Google Scholar
  6. 6.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29, 51–59 (1996)CrossRefGoogle Scholar
  7. 7.
    Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.: A study of face recognition as people age. In: Proceedings of IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1–8 (2007)Google Scholar
  8. 8.
    Guo, G., Mu, G., Fu, Y., Huang, T.: Human age estimation using bio-inspired features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, pp. 112–119 (2009)Google Scholar
  9. 9.
    Geng, X., Zhou, Z.H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2234–2240 (2007)CrossRefGoogle Scholar
  10. 10.
    Kilinc, M., Akgul, Y.S.: Human age estimation via geometric and textural features. In: Proceedings of International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy, pp. 531–538 (2012)Google Scholar
  11. 11.
    Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, Massachusetts (2015)Google Scholar
  12. 12.
    Mirzaei, F., Toygar, O.: Facial age classification using subpattern-based approaches. In: Proceedings of International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, NV (2011)Google Scholar
  13. 13.
    Liu, K.H., Yan, S., Kuo, C.C.J.: Age group classification via structured fusion of uncertainty-driven shape features and selected surface features. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, pp. 445–452 (2014)Google Scholar
  14. 14.
    Lanitis, A.: FG-NET aging database face and gesture recognition (2012). Accessed 21 Oct 2016
  15. 15.
    Kakadiaris, I.A., Toderici, G., Evangelopoulos, G., Passalis, G., Chu, D., Zhao, X., Shah, S.K., Theoharis, T.: 3D–2D face recognition with pose and illumination normalization. Computer Vision and Image Understanding (2016, in press).
  16. 16.
    Armstrong, T.P.: The Human Odyssey: Navigating the Twelve Stages of Life. Sterling, New York (2008)Google Scholar
  17. 17.
    Liu, K.H., Yan, S., Kuo, C.C.: Age estimation via grouping and decision fusion. IEEE Trans. Inf. Forensics Secur. 10, 2408–2423 (2015)CrossRefGoogle Scholar
  18. 18.
    Kakadiaris, I.A., Passalis, G., Toderici, G., Murtuza, M.N., Lu, Y., Karampatziakis, N., Theoharis, T.: Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Trans. Pattern Anal. Mach. Intell. 29, 640–649 (2007)CrossRefGoogle Scholar
  19. 19.
    Toderici, G., Passalis, G., Zafeiriou, S., Tzimiropoulos, G., Petrou, M., Theoharis, T., Kakadiaris, I.: Bidirectional relighting for 3D-aided 2D face recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 2721–2728 (2010)Google Scholar
  20. 20.
    Wang, H.L., Wang, J.G., Yau, W.Y., Chua, X.L., Tan, Y.P.: Effects of facial alignment for age estimation. In: Proceedings of International Conference on Control Automation Robotics Vision, Singapore, pp. 644–647 (2010)Google Scholar
  21. 21.
    Han, H., Otto, C., Liu, X., Jain, A.: Demographic estimation from face images: human vs. machine performance. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1148–1161 (2014)CrossRefGoogle Scholar
  22. 22.
    Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019–1025 (1999)CrossRefGoogle Scholar
  23. 23.
    Guo, G., Wang, X.: A study on human age estimation under facial expression changes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 2547–2553 (2012)Google Scholar
  24. 24.
    Zhang, C., Guo, G.: Age estimation with expression changes using multiple aging subspaces. In: Proceedings of IEEE International Conference on Biometrics: Theory Applications and Systems, Washington DC, pp. 1–6 (2013)Google Scholar
  25. 25.
    Zhang, C., Guo, G.: Exploiting unlabeled ages for aging pattern analysis on a large database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, pp. 458–464 (2013)Google Scholar
  26. 26.
    Webb, A.: Statistical Pattern Recognition, 2nd edn. Wiley, New York (2002)CrossRefzbMATHGoogle Scholar
  27. 27.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of Annual Workshop on Computational Learning Theory, Pittsburgh, PA, pp. 144–152 (1992)Google Scholar
  28. 28.
    Chang, C.C., Lin, C.J.: LibSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computational Biomedicine Lab, Department of Computer ScienceUniversity of HoustonHoustonUSA
  2. 2.Department of Computer ScienceITESM Campus MonterreyMonterreyMexico

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