DOI QR코드

DOI QR Code

Half-Against-Half Multi-class SVM Classify Physiological Response-based Emotion Recognition

  • ;
  • 고광은 (중앙대학교 대학원 전자전기공학부) ;
  • 박승민 (중앙대학교 대학원 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Vanny, Makara (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 투고 : 2013.03.10
  • 심사 : 2013.05.02
  • 발행 : 2013.06.25

초록

The recognition of human emotional state is one of the most important components for efficient human-human and human- computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.

키워드

참고문헌

  1. D. E. Kim, J. Kim, E. C. Lee, M. Whang and Y. Cho, "Interactive Emotional Content Communication System using Portable Wireless Biofeedback Device," IEEE Trans. on Consumer Electronics, vol. 54, no. 4, pp. 1929-1936, 2011.
  2. E. Vyzas, W. Picard and J. Healey, "Toward Machine Emotional Intelligence," Analysis of Affective Physiological State, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp. 1175-1191, Oct. 2001. https://doi.org/10.1109/34.954607
  3. J. Lang, M. Bradley and N. Cuthbert, "International Affective Picture System (IAPS)," Center for Research in Psychophysiology, University of Florida, 1999.
  4. J. Platt. How to Implement SVMs, IEEE Intelligent System, vol. 13, no. 4, pp. 26-28D, 1998.
  5. Vladimir N. Vapnik, "Statistical Learning Theory," New York, Wiley, 1998.
  6. Vladimir N. Vapnik, "The Nature of Statistical Learning Theory," Springer-Verlag, New York, 1995.
  7. R. Sangeetha and B. Kalpana, "Performance Evaluation of Kernels in Multiclass Support Vector Machines," Int. Journal of Soft Computing and Engineeing, vol. 1, no. 5, pp. 2231-2307, 2011.
  8. H. Lei and V. Govindaraju, "Half-against-half Multi-classs Support Vector Machines," Lecture Notes in Computer Science, 3541, Springer-Verlag, pp. 156-164, 2005.
  9. J. Henry, "Half-against-half multi-class support vector machines in classification of benthic macroinvertebrate images," IEEE Int. Conference on (ICCIS), 2012.
  10. L. Lei and Z. H. Zhu, " On-line static security assessment of power system based on a new half-Against-half multi-class support vector Machine," IEEE Int. Workshop on (ISA), pp. 1-5, 2011.
  11. A. Hassan and I. Damper, "Classification of Emotional Speech using 3DEC Hierarchical Classification," Elsevier Speech Communication, vol. 54, pp. 903-916, 2012. https://doi.org/10.1016/j.specom.2012.03.003
  12. Bernhard Scholkopf and Alex Smola, "Learning with kernels." MIT Press, Cambridge, MA, 2002.
  13. K. Crammer and Y. Singer, "On the algorithmic implementation of multi-class kernel-based vector machines," Journal of Machine Learning Research, vol. 2, pp. 265-292, 2001.
  14. Q. Chang, Q. Chen and X. Wang, "Scaling Gaussian RBF kernel width to improve SVM classification," IEEE Int. Conference on Neural Networks and Brain, vol. 1, pp. 19-22, Oct. 2005.
  15. C. Cortes and V. Vapnik, "Support vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
  16. B. Liu, Z. Hao and E. C. C. Tsang, "Nesting One-Against-One Algorithm Based on SVMs for Pattern Classification," IEEE. Trans. on Neural Network, vol. 19, no. 12, pp. 2044-2052, Dec. 2008. https://doi.org/10.1109/TNN.2008.2003298
  17. Y. Liu and Y. F. Zheng, "One-Against-All Multi-Class SVM Classification Using Reliability Measures," IEEE. Int. Joint Conf. on Neural Networks, vol. 2, pp. 849-854, 2005.
  18. P. Ranganathan, A. Ramanan and M. Niranjan, "An Efficient and Speed-Up Tree for Multi-class Classification," IEEE. 6th Int. Conf. on (ICIAfS), pp. 190-193, 2012.
  19. H. Yi, X. Song and B. Hiang, "Structure Selection for DAG-SVM based on Misclassification Cost Minimization," Int. Journal of (ICIC), vol. 7, no. 9, pp. 5133-5143, Sept. 2011.