A Hierarchical Face Recognition Algorithm

  • Remco R. Bouckaert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5828)


In this paper, we propose a hierarchical method for face recognition where base classifiers are defined to make predictions based on various different principles and classifications are combined into a single prediction. Some features are more relevant to particular face recognition tasks than others. The hierarchical algorithm is flexible in selecting features relevant for the face recognition task at hand. In this paper, we explore various features based on outline recognition, PCA classifiers applied to part of the face and exploitation of symmetry in faces. By combining the predictions of these features we obtain superior performance on benchmark datasets (99.25% accuracy on the ATT dataset) at reduced computation cost compared to full PCA.


Face Recognition Linear Discriminant Analysis Independent Component Analysis Principle Component Analysis Independent Component Analysis 
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.


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  1. 1.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997); Yale face database, CrossRefGoogle Scholar
  2. 2.
    Bouckaert, R.R., Goebel, M., Riddle, P.J.: Generalized Unified Decomposition of Ensemble Loss. In: Australian Conference on Artificial Intelligence 2006, pp. 1133–1139 (2006)Google Scholar
  3. 3.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Delac, K., Grgic, M., Grgic, S.: Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set. International Journal of Imaging Systems and Technology 15(5), 252–260Google Scholar
  5. 5.
    Guo, G., Li, S.Z., Chan, K.L.: Support vector machines for face recognition Image and Vision Computing  19(9-10), 631–638 (2001)Google Scholar
  6. 6.
    Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and infrared face recognition a review. Computer Vision and Image Understanding. Elsevier, Amsterdam (2005)Google Scholar
  7. 7.
    Kim, K.I., Jung, K., Kim, H.J.: Face recognition using kernel principal component analysis Signal Processing Letters. IEEE 9(2), 40–42 (2002)Google Scholar
  8. 8.
    Liu, C., Wechsler, H.: Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition. In: Proc. of the Second International Conference on Audio- and Video-based Biometric Person Authentication, AVBPA 1999, Washington D.C., USA, March 22-24, pp. 211–216 (1999)Google Scholar
  9. 9.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using LDA-Based Algorithms. IEEE Trans. on Neural Networks 14(1), 195–200 (2003)CrossRefGoogle Scholar
  10. 10.
    Moon, H., Phillips, P.J.: Computational and Performance aspects of PCA-based Face Recognition Algorithms. Perception 30, 303–321 (2001)CrossRefGoogle Scholar
  11. 11.
    Samaria, F., Harter, A.: Parameterisation of a Stochastic Model for Human Face Identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL (December 1994)Google Scholar
  12. 12.
    Yang, M.-H., Ahuja, N., Kriegman, D.: Face recognition using kernel eigenfaces Proceedings. In: 2000 International Conference on Image Processing, vol. 1, pp. 37–40 (2000)Google Scholar
  13. 13.
    Witten, I.H., Frank, E.: Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco (2000)Google Scholar
  14. 14.
    Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)CrossRefGoogle Scholar
  15. 15.
    Zheng, W., Zou, C., Zhao, L.: Face recognition using two novel nearest neighbor classifiers. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 725–728 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Remco R. Bouckaert
    • 1
  1. 1.Computer Science DepartmentUniversity of WaikatoNew Zealand

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