Facial Expression Recognition based on Facial Feature and Multi Library Wavelet Neural Network

Nawel Oussaifi, Wajdi Bellil, Chokri Ben Amar

2012

Abstract

In this paper, we propose a wavelet neural network-based system for automatically classifying facial expressions. This system is based on Multi Library Wavelet Neural Network (MLWNN) for emotions classification. Like other methods, our approach relies on facial deformation features. Eyes, mouth and eyebrows are identified as the critical features and their feature points are extracted to recognize the emotion. After feature extraction is performed a Multi Library Wavelet Neural Network approach is used to recognize the emotions contained within the face. This approach differs from existing work in that we define two classes of expressions: active emotions (smile, surprise and fear) and passive emotions (anger, disgust and sadness). In order to demonstrate the efficiency of the proposed system for the facial expression recognition, its performances are compared with other systems.

References

  1. Cohen P., Johnston M., McGee D., Oviatt S., Clow J., Smith I., The efficiency of multimodal interaction: A case study. in Proceedings of International Conference on Spoken Language Processing. ICSLP'98. Australia. 1998.
  2. Oviatt S., Ten myths of multimodal interaction. Communications of the ACM. Volume 42. Number 11 (1999). Pages 74-81.
  3. Oviatt S., DeAngeli. A, Kuhn K., (1997) Integration and synchronization of input modes during multimodal human-computer interaction. In Proceedings of Conf. Human Factors in Computing Systems CHI'97. ACM Press. NY. pp. 415 - 422.
  4. Jaimes A., and Sebe N., Multimodal Human Computer Interaction: A Survey. IEEE International Workshop on Human Computer Interaction. ICCV 2005. Beijing. China.
  5. Mehrabian A., Communication without words. Psychology Today. vol. 2. pp. 53-56. 1968.
  6. Zeng Z., Tu J., Liu M., Huang T. S., Pianfetti B., Roth D., Levinson S., Audio-Visual Affect Recognition. (2007) IEEE Trans. Multimedia. vol. 9. no. 2.
  7. Pantic M., and Rothkrantz L. J. M., Towards an affectsensitive multimodal human-computer interaction (2003). Proc. of the IEEE. vol. 91. no. 9. pp. 1370- 1390.
  8. Ioannou S., Raouzaiou A., Tzouvaras V., Mailis T., Karpouzis K., Kollias S., Emotion recognition through facial expression analysis based on a neurofuzzy network (2005). Special Issue on Emotion: Understanding & Recognition Neural Networks. Elsevier. Volume 18. Issue 4.pp. 423-435.
  9. De Silva L. C., and Ng P. C., Bimodal emotion recognition (2000). in Proc. Face and Gesture Recognition Conf. pp. 332-335.
  10. Gunes H., and Piccardi M., Fusing Face and Body Gesture for Machine Recognition of Emotions (2005). IEEE International Workshop on Robots and Human Interactive Communication. pp. 306 - 311.
  11. Karpouzis K., Caridakis G., Kessous L., Amir N., Raouzaiou A., Malatesta L., Kollias S., Modeling naturalistic affective states via facial vocal and bodily expressions recognition (2007). AI for Human Computing. LNAI Volume 4451/2007. Springer.
  12. Chihaoui M., Bellil W., and Amar C., (2010): Multimother wavelet neural network based on genetic algorithm for 1D and 2D functions approximation. In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation. pp 429-434.
  13. Bellil W., Othmani M., Ben Amar C., Alimi M. A., (2008) A new algorithm for initialization and training of beta multi-library wavelets neural network. Aramburo J. Ramirez Trevino A Advances in robotics. automation and control. I-Tech Education and Publishing. pp 199-220.
  14. Bellil.W, Othmani. M, Ben Amar. C., Initialization by Selection for multi library wavelet neural network training (2007). Proceeding of the 3rd international workshop on artificial neural networks and intelligent information processing ANNIIP07. in conjunction with 4th international conference on informatics in control. automation and robotics ICINCO. pp.30-37.
  15. Martí J., et al. (Eds.): IbPRIA (2007). Part II. LNCS 4478. pp. 40-47. 2007.© Springer-Verlag Berlin Heidelberg 2007.
  16. Othmani M., Bellil W., Ben Amar C., and Alimi M. A., A novel approach for high dimension 3D object representation using Multi-Mother Wavelet Network (2011). International Journal Multimedia Tools and Applications, MTAP, Springer Netherlands, ISSN 1380-7501, pp. 1-18.
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Paper Citation


in Harvard Style

Oussaifi N., Bellil W. and Ben Amar C. (2012). Facial Expression Recognition based on Facial Feature and Multi Library Wavelet Neural Network . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8565-22-8, pages 447-450. DOI: 10.5220/0004034004470450


in Bibtex Style

@conference{icinco12,
author={Nawel Oussaifi and Wajdi Bellil and Chokri Ben Amar},
title={Facial Expression Recognition based on Facial Feature and Multi Library Wavelet Neural Network},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2012},
pages={447-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004034004470450},
isbn={978-989-8565-22-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Facial Expression Recognition based on Facial Feature and Multi Library Wavelet Neural Network
SN - 978-989-8565-22-8
AU - Oussaifi N.
AU - Bellil W.
AU - Ben Amar C.
PY - 2012
SP - 447
EP - 450
DO - 10.5220/0004034004470450