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Multiple Neural Networks for Facial Feature Localization in Orientation-Free Face Images

  • Lionel Prevost
  • Rachid Belaroussi
  • Maurice Milgram
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4087)

Abstract

We present in this paper a new facial feature localizer. It uses a kind of auto-associative neural network trained to localize specific facial features (like eyes and mouth corners) in orientation-free faces. One possible extension is presented where several specialized detectors are trained to deal with each face orientation. To select the best localization hypothesis, we combine radiometric and probabilistic information. The method is quite fast and accurate. The mean localization error (estimated on more than 700 test images) is lower than 9%.

Keywords

Face Image Facial Feature Facial Emotion Recognition Face Orientation Specific Face 
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.

References

  1. 1.
    Belaroussi, R., Prevost, L., Milgram, M.: Classifier combination for face localization in color images. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 1043–1050. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  3. 3.
    Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: a survey. Proceedings of IEEE 83(5), 705–740 (1995)CrossRefGoogle Scholar
  4. 4.
    Cristinacce, D., Cootes, T.: A comparison of shape constrained facial feature detectors. In: International Conference on Automatic Face and Gesture Recognition, pp. 375–380 (2004)Google Scholar
  5. 5.
    DeMers, D., Cottrell, G.: Non-linear dimensionality reduction. Neural Information Processing Systems 5, 580–587 (1993)Google Scholar
  6. 6.
    Duffner, S., Garcia, C.: A Connexionist Approach for Robust and Precise Facial Feature Detection in Complex Scenes. In: IEEE International Symposium on Image and Signal Processing and Analysis, pp. 316–321 (2005)Google Scholar
  7. 7.
    Feng, G.C., Yuen, P.C.: Multi-cues eye detection on gray intensity image. Pattern Recognition 34, 1033–1046 (2001)MATHCrossRefGoogle Scholar
  8. 8.
    Féraud, R., Bernier, O., Viallet, J., Collobert, M.: A fast and accurate face detector based on neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(1), 42–53 (2002)CrossRefGoogle Scholar
  9. 9.
    Fumera, G., Roli, F.: A theoretical and experimental analysis of linear combiners for multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 942–956 (2005)CrossRefGoogle Scholar
  10. 10.
    Ioannou, S., Wallace, M., Karpouzis, K., Raouzaiou, A., Kollias, S.: Combination of Multiple Extraction Algorithms in the Detection of Facial Features. In: International Conference on Image Processing (2005)Google Scholar
  11. 11.
    Milgram, M., Belaroussi, R., Prevost, L.: Multi-stage combination of geometric and colorimetric detectors for eyes localization. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 1010–1017. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)CrossRefGoogle Scholar
  13. 13.
    Peng, P., Chen, L., Ruan, S., Kukharev, G.: A Robust and Efficient Algorithm for Eye Detection on Gray Intensity Face. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 302–308. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Schwenk, H., Milgram, M.: Transformation invariant auto-association with application to handwritten character recognition. Neural Information Processing Systems 7, 991–998 (1995)Google Scholar
  15. 15.
    Yuille, A., Hallinan, P., Cohen, D.: Feature extraction from faces using deformable templates. International Journal of Computer Vision 8(2), 99–111 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lionel Prevost
    • 1
  • Rachid Belaroussi
    • 1
  • Maurice Milgram
    • 1
  1. 1.Université Pierre et Marie Curie-Paris 6, EA2385 PRC, BC252 4Place JussieuFrance

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