Skip to main content
SpringerLink
Log in

A Hierarchical Face Recognition Algorithm

  • Conference paper
Advances in Machine Learning (ACML 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5828))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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, http://cvc.yale.edu/

    Article  Google Scholar 

  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. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  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), 252–260

    Google Scholar 

  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. 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. 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. 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. Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using LDA-Based Algorithms. IEEE Trans. on Neural Networks 14(1), 195–200 (2003)

    Article  Google Scholar 

  10. Moon, H., Phillips, P.J.: Computational and Performance aspects of PCA-based Face Recognition Algorithms. Perception 30, 303–321 (2001)

    Article  Google Scholar 

  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. 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. Witten, I.H., Frank, E.: Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  14. Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Waikato, New Zealand

    Remco R. Bouckaert

Authors
  1. Remco R. Bouckaert
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National Laboratory for Novel Software Technology, Nanjing University, 22 Hankou Road, 210093, Nanjing, China

    Zhi-Hua Zhou

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 567, Osaka, Ibaraki, Japan

    Takashi Washio

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bouckaert, R.R. (2009). A Hierarchical Face Recognition Algorithm. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-05224-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05223-1

  • Online ISBN: 978-3-642-05224-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation