iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/11840930_65
Human Facial Expression Recognition Using Hybrid Network of PCA and RBFN | SpringerLink
Skip to main content
Springer Nature Link
Log in

Human Facial Expression Recognition Using Hybrid Network of PCA and RBFN

  • Conference paper
Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

Included in the following conference series:

Abstract

In this paper, we propose a hybrid architecture combining radial basis function network (RBFN) and Principal Component Analysis (PCA) re-constructure model to perform facial expression recognition from static images. The resultant framework is a two stages coarse to fine discrimination model based on local features extracted from eyes and face images by applying PCA technique . It decomposes the acquired data into a small set of characteristic features. The objective of this research is to develop a more efficient approach to classify between seven prototypic facial expressions, such as neutral, joy, anger, surprise, fear, disgust, and sadness. A constructive procedure is detailed and the system performance is evaluated on a public database ”Japanese Females Facial Expression (JAFFE)”. As anticipated, the experimental results demonstrate the potential capabilities of the proposed approach.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

Similar content being viewed by others

References

  1. Ioannou, S., Raouzaiou, A., Tzouvaras, V., Mailis, T., Karpouzis, K., Kollias, S.: Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Networks 18, 423–435 (2005)

    Article  Google Scholar 

  2. Fragopanagos, N., Taylor, J.: Emotion recognition in human-computer interaction. Neural Networks 18, 389–405 (2005)

    Article  Google Scholar 

  3. Yacoob, Y., Lam, H.M., Davis, L.S.: Recognizing faces showing expressions. In: International Workshop on Automatic Face and Gesture Recognition, Zurich (1995)

    Google Scholar 

  4. Bartlett, M., Viola, P., Sejnowski, T., Larsen, L., Hager, J., Ekman, P.: Classifying facial action. In: Mozer, M., Touretzky, D.S., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol. 8. The MIT Press, Cambridge (1996)

    Google Scholar 

  5. Ekman, P., Friesen, W.: The facial action coding system. Consulting Psychologists Press, Palo Alto (1965)

    Google Scholar 

  6. Donato, G., Bartlett, M., Hager, J., Ekman, P., Sejnowski, T.: Classifying facial actions. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(10), 974–989 (1999)

    Article  Google Scholar 

  7. Anderson, K., McOwan, P.: A real-time automated system for the recognition of human facial expressions. IEEE Transactions on Systems, Mand, and CyberneticsXPart B 36(1), 96–105 (2006)

    Article  Google Scholar 

  8. Devillers, L., Vidrascu, L., Lamel, L.: Challenges in real-life emotion annotation and machine learning based detection. Neural Networks 18, 407–422 (2005)

    Article  Google Scholar 

  9. Chen, X.W., Huang, T.: Facial expression recognition: A clustering-based approach. Pattern Recognition Letters 24, 1295–1302 (2003)

    Article  MATH  Google Scholar 

  10. Cottrell, G., Metcalfe, J.: EMPATH: Face, gender, and emotion recognition using holons. In: Moddy, J., Lippman, R.P., Touretzky, D.S. (eds.) Advances in Neural Information Processing Systems, vol. 3, pp. 564–571 (1991)

    Google Scholar 

  11. Ma, L., Khorasani, K.: Facial expression recognition using constructive feedforward neural network. IEEE Trans. on Systems, Man, and Cybernetics – Part B: Cybernetics 34(3), 1559–1588 (2004)

    Google Scholar 

  12. Wang, Y., Ai, H., Wu, B., Huang, C.: Real time facial expression recognition with adaboost. In: Proceedings of the 17th International Confeence on Pattern Recognition, vol. 3, pp. 926–929 (2004)

    Google Scholar 

  13. Buciu, I., Pitas, I.: Application of non-negative and local non negative matrix factorization to facial expression recognition. In: Proceedings of the 17th International Confeence on Pattern Recognition, August 2004, vol. 1, pp. 288–291 (2004)

    Google Scholar 

  14. Shinohara, Y., Otsu, N.: Facial expression recognition using fisher weight maps. In: Proceedings of the Sixth IEEE International Confeence on Automatic Face And Gesture Recognition, pp. 499–504 (2004)

    Google Scholar 

  15. Abboud, B., Davoine, F., Dang, M.: Facial expression recognition and synthesis based on an appearance model. Signal Processing: Image Communication 19(8), 723–740 (2004)

    Article  Google Scholar 

  16. Feng, X., Pietikainen, M., Hadid, A.: Facial expression recognition with local binary patterns and linear programming. Pattern Recognition and Image Analysis 15(2), 546–548 (2005)

    Google Scholar 

  17. Nakayama, M., Kumakura, T.: Face identification performance using facial expressions as perturbation. In: Proceedings of the International Confeence on Artificial Neural Networks, vol. 1, pp. 557–562 (2005)

    Google Scholar 

  18. Amin, M., Afzulpurkar, N., Dailey, M., Esichaikul, V.: Fuzzy-C-mean determines the principle component pairs to estimate the degree of emotion from facial. In: Proceedings of the 2nd International Confeence on Fuzzy Systems and Knowledge Discovery, vol. 1, p. 484 (2005)

    Google Scholar 

  19. Datcu, D., Rothkrantz, L.: Facial expression recognition with relevance vector machines. In: Proceedings of the IEEE International Conference on Multimedia and Expo (July 2005)

    Google Scholar 

  20. Lyons, M., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(12), 1357–1362 (1999)

    Article  Google Scholar 

  21. Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, New York (1994)

    MATH  Google Scholar 

  22. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

    Article  Google Scholar 

  23. Duric, Z., Gray, W., Heishman, R., Li, F., Rosenfeld, A., Schoelles, M., Schunn, C., Wechsler, H.: Integrating perceptual and cognitive modeling for adaptive and intelligent human - computer interaction. Proceedings of the IEEE 90(7), 1272–1289 (2002)

    Article  Google Scholar 

  24. Tian, Y.-L., Kanade, T., Cohn, J.: Recognitizing action units for facial expression analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(2), 97–115 (2001)

    Article  Google Scholar 

  25. He, L., Zhou, J., Hu, D., Zou, C., Zhao, L.: Boosted independent features for face expression recognition. In: Proceedings of the Second International Symposium on Neural Networks, May 2005, pp. 137–145 (2005)

    Google Scholar 

  26. Advanced Multimedia Processing Lab. Carnegie Mellon University, http://amp.ece.cmu.edu/projects/faceauthentication/download.htm

  27. Ekman, P., Friesen, W.V.: Pictures of facial affect. Human Interaction Laboratory, Univ. of California Medical Center

    Google Scholar 

  28. Cowie, R., Douglas-Cowie, E., Cox, C.: Beyong emotion archetypes: Databases for emotion modelling using neural networks. Neural Networks 18, 371–388 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taipei University, Sanshia, Taipei County, Taiwan

    Daw-Tung Lin

Authors
  1. Daw-Tung Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, DT. (2006). Human Facial Expression Recognition Using Hybrid Network of PCA and RBFN. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_65

Download citation

  • DOI: https://doi.org/10.1007/11840930_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation