Age Estimation Using Active Appearance Models and Ensemble of Classifiers with Dissimilarity-Based Classification

  • Sharad Kohli
  • Surya Prakash
  • Phalguni Gupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6838)


This paper proposes a novel technique that uses Active Appearance Models (AAMs) and Ensemble of classifiers for age estimation. In this technique, features are extracted from face images by AAMs and a global classifier is then used to obtain an idea about the age by distinguishing between child/teen-hood and adulthood, before age estimation. This is done by an ensemble containing various classifiers trained on multiple dissimilarities and thereby which reduces misclassification error. Different aging functions are considered for the classified images to estimate age more accurately. Experiments are performed on the publicly available FG-NET database. The method is found to be a good age estimator.


Support Vector Regression Mean Absolute Error Facial Expression Recognition Aging Function Cumulative Score 
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  1. 1.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 442–455 (2002)CrossRefGoogle Scholar
  2. 2.
    Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: a survey. Proceedings of the IEEE 83(5), 705–741 (1995)CrossRefGoogle Scholar
  3. 3.
    Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recognition 36(1), 259–275 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    O’toole, A.J., Deffenbacher, K.A., Valentin, D., Mckee, K., Huff, D., Abdi, H.: The Perception of Face Gender: The Role of Stimulus Structure in Recognition and Classification. Memory and Cognition 26, 146–160 (1997)CrossRefGoogle Scholar
  5. 5.
    Albert, M., Ricanek, K., Patterson, E.: 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)CrossRefGoogle Scholar
  6. 6.
    Magnus, C., Forsberg: Facial morphology and ageing: a longitudinal cephalometric investigation of young adults. European Journal of Orthodontics 1(1), 15–23 (1979)CrossRefGoogle Scholar
  7. 7.
    Luu, K., Ricanek, K., Bui, T.D., Suen, C.Y.: Age estimation using active appearance models and support vector machine regression. In: Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Applications and systems, pp. 314–318 (2010)Google Scholar
  8. 8.
    Edwards, G.J., Lanitis, A., Taylor, C.J., Cootes, T.F.: Statistical models of face images – improving specificity. Image and Vision Computing 16(3), 203–211 (1998)CrossRefGoogle Scholar
  9. 9.
    Ramanathan, N., Chellappa, R.: Face Verification across Age Progression. In: Proceeding of IEEE Conference of Computer Vision and Pattern Recognition, pp. 462–469 (2005)Google Scholar
  10. 10.
    Fu, Y., Huang, T.S.: Human Age Estimation With Regression on Discriminative Aging Manifold. IEEE Transactions on Multimedia 10(4), 578–584 (2008)CrossRefGoogle Scholar
  11. 11.
    Kwon, Y.H., da Vitoria Lobo, N.: Age Classification from Facial Images. Computer Vision and Image Understanding 74(1), 1–21 (1999)CrossRefGoogle Scholar
  12. 12.
    Geng, X., Zhou, Z.-H., Zhang, Y., Li, G., Dai, H.: Learning from facial aging patterns for automatic age estimation. ACM Multimedia, 307–316 (2006)Google Scholar
  13. 13.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)CrossRefGoogle Scholar
  14. 14.
    Kang, H., Cootes, T.F., Taylor, C.J.: A Comparison of Face Verification Algorithms using Appearance Models. In: British Machine Vision Conference, vol. 2, pp. 477–486 (2002)Google Scholar
  15. 15.
    Tang, F., Deng, B.: Facial Expression Recognition using AAM and Local Facial Features. In: Proceedings of the Third International Conference on Natural Computation ICNC 2007, vol. 02, pp. 632–635 (2007)Google Scholar
  16. 16.
    Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51(2), 181–207 (2003)CrossRefzbMATHGoogle Scholar
  17. 17.
    Pekalska, E., Paclík, P., Duin, R.P.W.: A Generalized Kernel Approach to Dissimilarity-based Classification. Journal of Machine Learning Research 2, 175–211 (2001)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Blanco, Á., Martín-Merino, M., De Las Rivas, J.: Ensemble of Support Vector Machines to Improve the Cancer Class Prediction Based on the Gene Expression Profiles. Innovations in Hybrid Intelligent Systems 44, 393–400 (2008)CrossRefGoogle Scholar
  19. 19.
    Chen, Chun-houh, Wolfgang, Unwin, A., Cox, M.A.A., Cox, T.F.: Multidimensional Scaling Handbook of Data Visualization, pp. 315–347 (2008)Google Scholar
  20. 20.
    The FG-NET Aging Database,
  21. 21.
    Smola, A.J., Scholkopf, B.: A tutorial on support vector regression Statistics and Computing, vol. 14(3), pp. 199–222 (2004)Google Scholar
  22. 22.
    Wold, S., Eriksson, L., Kettaneh, N.: PLS in Data Mining and Data Integration. Handbook of Partial Least Squares, pp. 327–357 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sharad Kohli
    • 1
  • Surya Prakash
    • 1
  • Phalguni Gupta
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KanpurKanpurIndia

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