Face Alignment Using Boosting and Evolutionary Search

  • Hua Zhang
  • Duanduan Liu
  • Mannes Poel
  • Anton Nijholt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5995)


In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.


face alignment boosting appearance models granular features evolutionary search 


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  1. 1.
    Cootes, T.F., Cooper, D.H., Taylor, C.J., Graham, J.: Trainable method of parametric shape description. Image and Vision Computing 10, 289–294 (1992)CrossRefGoogle Scholar
  2. 2.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active apperance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 681–685 (2001)CrossRefGoogle Scholar
  3. 3.
    Zhou, Y., Gu, L., Zhang, H.J.: Bayesian tangent shape model: Estimating shape and pose parameters via Bayesian reference. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 109–116 (2003)Google Scholar
  4. 4.
    Lin, L., Fang, W., Ying-Qing, X., Xiaoou, T., Heung-Yeung, S.: Accurate face alignment using shape constrained Markov network. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1313–1319 (2006)Google Scholar
  5. 5.
    Liu, X.: Generic face alignment using boosted appearance model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1–8 (2007)Google Scholar
  6. 6.
    Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 671–686 (2007)CrossRefGoogle Scholar
  7. 7.
    Abramson, Y., Moutarde, F., Steux, B., Stanciulescu, B.: Combining adaboost with a hill-climbing evolutionary feature-search for efficient training of performant visual object detectors. In: Proceedings of the 7th International FLINS Conference on Applied Artificial Intelligence (2006)Google Scholar
  8. 8.
    Freund, Y., Schapire, R.: A decision-theoretic generaliation of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)Google Scholar
  9. 9.
    Fridman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28, 337–374 (2000)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  11. 11.
    Treptow, A., Zell, A.: Combining adaboost learning and evolutionary search to select features for real-time object detection. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 2107–2113 (2004)Google Scholar
  12. 12.
    Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascade for face detection. In: Proceedings of IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  13. 13.
    Howard, D., Roberts, S.C., Brankin, R.: Evolution of ship detectors for satellite sar imagery. In: Langdon, W.B., Fogarty, T.C., Nordin, P., Poli, R. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 135–148. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  14. 14.
    Martinez, A.R., Benavente, R.: The AR face database. CVC Technical Report, vol. 24 (1998)Google Scholar
  15. 15.
    Philips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)CrossRefGoogle Scholar
  16. 16.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1615–1618 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hua Zhang
    • 1
  • Duanduan Liu
    • 2
  • Mannes Poel
    • 3
  • Anton Nijholt
    • 3
  1. 1.College of Software EngineeringSoutheast UniversityNanjingChina
  2. 2.Lab of Science and TechnologySoutheast UniversityNanjingChina
  3. 3.Human Media InteractionUniversity of TwenteEnschedeThe Netherlands

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