Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Emotion Recognition Systems based on the Probabilistic Relational Model Between Facial Expressions and Physiological Responses

생리적 내재반응 및 얼굴표정 간 확률 관계 모델 기반의 감정인식 시스템에 관한 연구

  • Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 고광은 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Received : 2013.03.25
  • Accepted : 2013.04.29
  • Published : 2013.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

The current vision-based approaches for emotion recognition, such as facial expression analysis, have many technical limitations in real circumstances, and are not suitable for applications that use them solely in practical environments. In this paper, we propose an approach for emotion recognition by combining extrinsic representations and intrinsic activities among the natural responses of humans which are given specific imuli for inducing emotional states. The intrinsic activities can be used to compensate the uncertainty of extrinsic representations of emotional states. This combination is done by using PRMs (Probabilistic Relational Models) which are extent version of bayesian networks and are learned by greedy-search algorithms and expectation-maximization algorithms. Previous research of facial expression-related extrinsic emotion features and physiological signal-based intrinsic emotion features are combined into the attributes of the PRMs in the emotion recognition domain. The maximum likelihood estimation with the given dependency structure and estimated parameter set is used to classify the label of the target emotional states.

Keywords

References

  1. R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. G. Taylor, "Emotion recognition in human-computer interaction," Signal Processing Magazine, IEEE, vol. 18, no. 1, pp. 32-80, 2001. https://doi.org/10.1109/79.911197
  2. B. Fasel and J. Luettin, "Automatic facial expression analysis: a survey," Pattern Recognition, vol. 36, no. 1, pp. 259-275, 2003. https://doi.org/10.1016/S0031-3203(02)00052-3
  3. K. Fukunaga Introduction to Statistical Pattern Recognition, 2Ed., Academic Press, p. 2, 1990.
  4. J. N. Bailenson, E. D. Pontikakis, I. B. Mauss, J. J. Gross, M. E. Jabon, C. A. C. Hutcherson, C. Nass, and O. John, "Real-time classification of evoked emotions using facial feature tracking and physiological responses," International Journal of Human-Computer Studies, vol. 66, no. 5, pp. 303-317, 2008. https://doi.org/10.1016/j.ijhcs.2007.10.011
  5. K. H. Joo, C. M. Geun, and K. K. Paliwal, "Face recognition using emotional face images and fuzzy fisherface," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 15, no. 1, pp. 94-98, Jan. 2009. https://doi.org/10.5302/J.ICROS.2009.15.1.094
  6. Z. Zhang, "Feature-based facial expression recognition: Sensitivity analysis and experiments with a multilayer perceptron," International Journal of Pattern Recognition and Artificial Intelligence, vol. 13, no. 6, pp. 893-911, 1999. https://doi.org/10.1142/S0218001499000495
  7. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer, "Learning probabilistic relational models," pp. 1300-1309.
  8. A. Savran, B. Sankur, and M. Taha Bilge, "Regressionbased intensity estimation of facial action units," Image and Vision Computing, vol. 30, no. 10, pp. 774-784, 2012. https://doi.org/10.1016/j.imavis.2011.11.008
  9. A. Savran, B. Sankur, and M. Taha Bilge, "Comparative evaluation of 3D vs. 2D modality for automatic detection of facial action units," Pattern Recognition, vol. 45, no. 2, pp. 767-782, 2012. https://doi.org/10.1016/j.patcog.2011.07.022
  10. P. Ekman and W. V. Friesen, "Facial action coding system: a technique for the measurement of facial movement," Consulting Psychologists Press, 1978.
  11. K.-E. Ko and K.-B. Sim, "Study of Emotion Recognition based on Facial Image for Emotional Rehabilitation Biofeedback," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 16, no. 10, pp. 957-962, Oct. 2010. https://doi.org/10.5302/J.ICROS.2010.16.10.957
  12. M. Vanny, K.-E. Ko, S.-M. Park, and K.-B. Sim, "Physiological responses-based emotion recognition using multi-class SVM with RBF kernel," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 4, pp. 1-8, Apr. 2013. https://doi.org/10.5302/J.ICROS.2013.13.1879
  13. S.-J. Lee and S.-W. Kim, "Classifying scratch defects on billets using image processing and SVM," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 19, no. 3, pp. 177-280, Mar. 2013. https://doi.org/10.5302/J.ICROS.2013.12.1838