Expression Detector System based on Facial Images

José G. Hernández-Travieso, Carlos M. Travieso, Marcos del Pozo-Baños, Jesús B. Alonso

Abstract

This paper proposes a emotion detector, applied for facial images, based on the analysis of facial segmentation. The parameterizations have been developed on spatial and transform domains, and the classification has been done by Support Vector Machines. A public database has been used in experiments, The Radboud Faces Database (RAFD), with eight possible emotions: anger, disgust, fear, happiness, sadness, surprise, neutral and contempt. Our best approach has been reached with decision fusion, using transform domains, reaching an accurate up to 96.62%.

References

  1. An, K. H., Chung, M. J., 2009. Cognitive face analysis system for future interactive TV. In IEEE Transactions on Consumer Electronics. Vol. 55, no. 4, pp. 2271-2279.
  2. Arima, M., Ikeda, K., Hosoda, R., 2004. Analyses of Facial Expressions for the Evaluation of Seasickness. In Oceans 7804. MTTS/ IEEE Techno-Ocean 7804. Vol.2, pp. 1129-1132.
  3. Burges, C. J. C., 1998. A tutorial on Support Vector Machines for Pattern Recognition. In Data Mining and Knowledge Discovery, Vol. 2, pp.121-167.
  4. Chin, K. L., Chang, E., Atkinson, D., 2008. A Digital Ecosystem for ICT Educators, ICT Industry and ICT Students. In Second IEEE International Conference on Digital Ecosystems and Technologies. pp. 660-673.
  5. Dahmane, M., Meunier, J., 2011. Emotion Recognition using Dynamic Grid-based HoG Features. In IEEE International Conference on Automatic Face & Gesture Recognition and Workshops, pp. 884-888.
  6. Ekman, P., Friesen, W., 1978. Facial Action Coding System: A Technique for the Measurement of Facial Movements. Consulting Psychologist Press, Palo Alto, CA.
  7. Eshete, B., Mattioli, A., Villafiorita, A., Weldemariam, K., 2010. ICT for Good: Opportunities, Challenges and the Way Forward. In Fourth International Conference on Digital Society. pp. 14-19.
  8. Fu, M. H., Kuo, Y. H., Lee, K. R., 2009. Fusing Remote Control Usage and Facial Expression for Emotion Recognition. In Fourth International Conference on Innovative Computing, Information and Control. pp. 132-135.
  9. Fuertes, J. J., Travieso, C. M., Naranjo, V., 2012. 2-D Discrete Wavelet Transform for Hand Palm Texture Biometric Identification and Verification. Wavelet Transforms and Their Recent Applications in Biology and Geoscience, Ed. InTech.
  10. González, R. C., Woods, R. E., 2002. Digital Image Processing. Prentice Hall, Upper Saddle River, New Jersey.
  11. Gouizi, K., Reguig, F. B., Maaoui, C., 2011. Analysis Physiological Signals for Emotion Recognition. In 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA). pp. 147-150.
  12. Jakkula, V., Tutorial on Support Vector Machine (SVM). School of EECS, Washington State University, Pullman 99164.
  13. Joachims, T., 1999. Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning. B. Schölkopf and C. Burges and A. Smola (ed.), MIT-Press.
  14. Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D. H. J., Hawk, S. T., & van Knippenberg, A., 2010. Presentation and validation of the Radboud Faces Database. In Cognition & Emotion, Vol. 24, nº 8, pp. 1377-1388.
  15. Mallat, S., 2009. A Wavelet Tour of Signal Processing. Third Edition: The Sparse Way.
  16. Otsu, N., 1979. A Threshold Selection Method from Gray_Level Histograms. In IEEE Transactions on Systems, Man and Cybernetics. Vol. 9, no. 1, pp. 62- 66.
  17. Pantic, M., Patras, I., 2006. Dynamics of Facial Expression: Recognition of Facial Actions and Their Temporal Segments From Face Profile Image Sequences. In IEEE Transactions on System, Man and Cybernetics-Part B: Cybernetics. Vol. 36, no.2, pp. 443- 449.
  18. Petrantonakis, P. C., Hadjileontiadis, L. J., 2010. Emotion Recognition from EEG Using High Order Crossing. In IEEE Transactions on Information Technology in Biomedicine. Vol. 14, no. 2, pp. 186-197.
  19. Siriak, S., Islam, N., 2010. Relationship between Information and Communication Technology (ICT) Adoption and Hotel Productivity: An Empirical Study of the Hotels in Phuket, Thailand. In Proceedings of PICMET'10: Technology Management for Global Economic Growth, pp. 1-9.
  20. Vapnik, N. V., 1998. Statistical Learning Theory. Wiley Interscience Publication, John Wiley & Sons Inc., Vargas, J. F., Travieso, C. M., Alonso, J. B., Ferrer. M. A., 2010. Off-line signature Verification Based on Gray Level Information Using Wavelet Transform and Texture Features. In 12th International Conference on Frontiers in Handwriting Recognition (ICFHR). pp. 587-592.
  21. Viola, P., Jones, M. J., 2004. Robust Real-Time Face Detection. In International Journal of Computer Vision. Vol. 57, nº 2, pp. 137-154.
  22. Wang, P., Kohler, C., Barrett, F., Gur, R., Verma, R., 2007. Quantifying Facial Expression Abnormality in Schizophrenia by Combining 2D and 3D Features. In IEEE Conference on Computer Vision and Pattern Recognition. pp. 1-8.
  23. Wang, P., Kohler, C., Martin E., Stolar, N., Verma, R., 2008. Learning-based Analysis of Emotional Impairments in Schizophrenia. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. pp. 1-8.
  24. Wong, J. J., Cho, S. Y., 2006. Recognizing Human Emotion from Partial Facial Features. In International International Joint Conference on Neural Networks. Vol. 1, pp. 166-173.
  25. Yu, H., Yang, J., Han, J., 2003. Classifying Large Data Sets Using SVMs with Hierarchical Clusters In The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 306-315.
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Paper Citation


in Harvard Style

Hernández-Travieso J., Travieso C., del Pozo-Baños M. and Alonso J. (2013). Expression Detector System based on Facial Images . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 411-418. DOI: 10.5220/0004322504110418


in Bibtex Style

@conference{mpbs13,
author={José G. Hernández-Travieso and Carlos M. Travieso and Marcos del Pozo-Baños and Jesús B. Alonso},
title={Expression Detector System based on Facial Images},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2013)},
year={2013},
pages={411-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004322504110418},
isbn={978-989-8565-36-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2013)
TI - Expression Detector System based on Facial Images
SN - 978-989-8565-36-5
AU - Hernández-Travieso J.
AU - Travieso C.
AU - del Pozo-Baños M.
AU - Alonso J.
PY - 2013
SP - 411
EP - 418
DO - 10.5220/0004322504110418