Advertisement

Face Recognition System Using Accurate and Rapid Estimation of Facial Position and Scale

  • Takatsugu Hirayama
  • Yoshio Iwai
  • Masahiko Yachida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

Face recognition technology needs to be robust for arbitrary facial appearances. In this paper, we propose a face recognition system that is efficient and robust for facial scale variations. In the process of face recognition, facial position detection incurs the highest computational cost. To estimate both facial position and scale, there needs to be a trade-off between accuracy and efficiency. To resolve the trade-off, we propose a method that estimates facial position in parallel with facial scale. We apply the method to our proposed system and demonstrate the advantages of the proposed system through face recognition experiments. The proposed system is more efficient than any other system and can maintain high face recognition accuracy for facial scale variations.

Keywords

Face Recognition Gabor Wavelet Beam Search Face Recognition System Facial Scale 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    X. Song, C. Lee, G. Xu and S. Tsuji: Extracting facial features with partial feature template, Proceedings of the Asian Conference on Computer Vision, pp. 751–754 (1994).Google Scholar
  2. [2]
    M. Turk and A. Pentland: Eigenface for recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71–86(1991).CrossRefGoogle Scholar
  3. [3]
    P. Belhumeur, J. Hespanha and D. Kriegman: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711–720 (1997).CrossRefGoogle Scholar
  4. [4]
    O. Ayinde and Y.-H. Yang: Face Recognition Approach Based on Rank Correlation of Gabor-Filtered Images, Pattern Recognition, Vol. 35, pp. 1275–1289 (2002).zbMATHCrossRefGoogle Scholar
  5. [5]
    L. Wiskott, J. M. Fellous, N. Krüger and C. von der Malsburg: Face recognition and gender determination, Proceedings of the International Workshop on Automatic Face and Gesture Recognition, pp. 92–97 (1995).Google Scholar
  6. [6]
    L. Wiskott, J. M. Fellous, N. Krüger and C. von der Malsburg: Face recognition by Elastic Bunch Graph Matching, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 775–779 (1997).CrossRefGoogle Scholar
  7. [7]
    N. Krüger, M. Pötzsch and C. von der Malsburg: Determination of face position and pose with a learned representation based on labelled graphs, Image and Vision Computing, Vol. 15, pp. 665–673 (1997).CrossRefGoogle Scholar
  8. [8]
    D. Pramadihanto, Y. Iwai and M. Yachida: A flexible feature matching for automatic face and facial points detection, Proceedings of the 14th International Conference on Pattern Recognition, pp. 324–329 (1998).Google Scholar
  9. [9]
    D. Pramadihanto, Y. Iwai and M. Yachida: Integrated Person Identification and Expression Recognition from Facial Images, IEICE Trans. on Information and System, Vol. E84-D, No. 7, pp. 856–866 (2001).Google Scholar
  10. [10]
    A. Lanitis, C. J. Taylor and T. F. Cootes: Automatic Interpretation and Coding of Face Images Using Flexible Models, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 743–756 (1997).CrossRefGoogle Scholar
  11. [11]
    T. Hirayama, Y. Iwai and M. Yachida: Face Recognition Based on Efficient Facial Scale Estimation, Proceedings of the Second International Workshop on Articu-lated Motion and Deformable Objects (AMDO 2002), pp. 201–212 (2002).Google Scholar
  12. [12]
    D. Pramadihanto, H. Wu and M. Yachida: Face Identification under Varying Pose Using a Single Example View, The Transactions of the Institute of Electronics, Information and Communication Engineers D-II, Vol. J80-D-II, No. 8, pp. 2232–2238 (1997).Google Scholar
  13. [13]
    W. Konen, T. Maurer and C. von der Malsburg: A fast dynamic link matching algorithm for invariant pattern recognition, Neural Network, Vol. 7, pp. 1019–1030 (1994).zbMATHCrossRefGoogle Scholar
  14. [14]
    R.P. Würtz: Object Recognition Robust Under Translation, Deformation, and Changes in Background, IEEE Trans. on on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 769–774 (1997).CrossRefGoogle Scholar
  15. [15]
    A.M. Martinez and R. Benavente: The AR face database, CVC Technical Report 24 (1998).Google Scholar
  16. [16]
    J. G. Daugman: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, Journal of the Optical Society of America A, Vol. 2, pp. 1160–1169 (1985).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Takatsugu Hirayama
    • 1
  • Yoshio Iwai
    • 1
  • Masahiko Yachida
  1. 1.Graduate School of Engineering Science, Osaka UniversityToyonakaJapan

Personalised recommendations