Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Emotion Detection Model based on Sequential Neural Networks in Smart Exhibition Environment

스마트 전시환경에서 순차적 인공신경망에 기반한 감정인식 모델

  • Received : 2016.12.21
  • Accepted : 2017.02.02
  • Published : 2017.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In the various kinds of intelligent services, many studies for detecting emotion are in progress. Particularly, studies on emotion recognition at the particular time have been conducted in order to provide personalized experiences to the audience in the field of exhibition though facial expressions change as time passes. So, the aim of this paper is to build a model to predict the audience's emotion from the changes of facial expressions while watching an exhibit. The proposed model is based on both sequential neural network and the Valence-Arousal model. To validate the usefulness of the proposed model, we performed an experiment to compare the proposed model with the standard neural-network-based model to compare their performance. The results confirmed that the proposed model considering time sequence had better prediction accuracy.

최근 지능형 서비스를 제공하기 위해 감정을 인식하기 위한 많은 연구가 진행되고 있다. 특히, 전시 분야에서 관중에게 개인화된 서비스를 제공하기 위해 얼굴표정을 이용한 감정인식 연구가 수행되고 있다. 그러나 얼굴표정은 시간에 따라 변함에도 불구하고 기존연구는 특정시점의 얼굴표정 데이터를 이용한 문제점이 있다. 따라서 본 연구에서는 전시물을 관람하는 동안 관중의 얼굴표정의 변화로부터 감정을 인식하기 위한 예측 모델을 제안하였다. 이를 위하여 본 연구에서는 시계열 데이터를 이용하여 감정예측에 적합한 순차적 인공신경망 모델을 구축하였다. 제안된 모델의 유용성을 평가하기 위하여 일반적인 표준인공신경망 모델과 제안된 모델의 성능을 비교하였다. 시험결과 시계열성을 고려한 제안된 모델의 예측이 더 뛰어남으로 보였다.

Keywords

References

  1. Altun, H., and G. Polat, "Boosting Selection of Speech Related Features to Improve Performance of Multi-class SVMs in Emotion Detection," Expert Systems with Applications, Vol.36, No.4(2009), 8197-8203. https://doi.org/10.1016/j.eswa.2008.10.005
  2. Alvarado, N., "Arousal and Valence in the Direct Scaling of Emotional Response to Film Clips," Motivation and Emotion, Vol.21, No.4(1997), 323-348. https://doi.org/10.1023/A:1024484306654
  3. Eckman, P. "Universal and Cultural Differences in Facial Expression of Emotion." Nebraska symposium on motivation. Vol.19. University of Nebraska Press, Lincoln, 1972.
  4. Haykin, S., Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, 1999.
  5. Hsieh, K.-L., "Employing a Recommendation Expert System based on Mental Accounting and Artificial Neural Networks into Mining Business Intelligence for Study Abroad's P/S Recommendations," Expert Systems with Applications, Vol.38, No.12(2011), 14376-14381. https://doi.org/10.1016/j.eswa.2011.03.078
  6. Jang, N. S., Data Mining, Dae-chung Media, Seoul, Korea, 1999.
  7. Jung, M. K., and J. K. Kim, "An Intelligent Determination Model of Audience Emotion for Implementing Personalized Exhibition," Journal of Intelligence and Information Systems, Vol.18, No.1(2012), 39-57. https://doi.org/10.13088/JIIS.2012.18.1.039
  8. Kim, D. S. Neural Networks: Theory and Applications. Hitech Information, Seoul, Korea, 1993.
  9. Kim, Y. J., and S. B. Cho, "A Context-aware Recommender System for Ringtones in a Mobile Phone using an Emotion Model", Proceedings of Korea Computer Congress 2009 (KCC 2009), Vol.36, No.1(2009), 162-165.
  10. Ko, K.-E., D.-J. Shin, and K.-B. Sim, "Development of Context Awareness and Service Reasoning Technique for Handicapped People," Journal of Korean Institute of Intelligent Systems, Vol.18, No.4(2008), 512-517. https://doi.org/10.5391/JKIIS.2008.18.4.512
  11. Lane, R., and L. Nadel, Cognitive Neuroscience of Emotion, Oxford Univ. Press, London, UK, 2000..
  12. Lee, C. H., and S. R. Moon, "Detection of Face-element for Facial Analysis," Journal of The Institute of Electronics Engineers of Korea,-CI, Vol.41, No.2(2004), 131-133.
  13. Lewis, P. A., H. D. Critchley, P. Rotshtein, and R. J. Dolan, "Neural Correlates of Processing Valence and Arousal in Affective Words," Cerebral Cortex, Vol.17, No.3(2007), 742-748. https://doi.org/10.1093/cercor/bhk024
  14. Mehrabian, A., and J. Russell, An Approach to Environmental Psychology, MIT Press, Cambridge, MA, 1974.
  15. Nicolaou, M., H. Gunes, and M. Pantic, "Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-arousal Space," IEEE Transactions on Affective Computing, Vol.2, No.2(2011), 92-105. https://doi.org/10.1109/T-AFFC.2011.9
  16. Ohno-Machado, L. "Sequential Use of Neural Networks for Survival Prediction in AIDS," Proceedings of American Medical Informatics Association Annual Fall Symposium, (1996), 170-174.
  17. Ohno-Machado, L., and M. A. Musen, "Sequential versus Standard Neural Networks for Pattern Recognition: An Example using the Domain of Coronary Heart Disease," Computers in Biology and Medicine, Vol.27, No.4(1997), 267-281. https://doi.org/10.1016/S0010-4825(97)00008-5
  18. Oliveira, A. M., M. P. Teixeira, I. B. Fonseca, and M. Oliveira, "Joint Model-parameter Validation of Self-estimates of Valence and Arousal: Probing a Differential-weighting Model of Affective Intensity," Proceedings of the 22nd Annual Meeting of the International Society for Psychophysics, (2006), 245-250.
  19. Pantic, M., and L. J. M. Rothkrantz, "Expert System for Automatic Analysis of Facial Expressions," Image and Vision Computing, Vol.18, No.11(2000), 881-905. https://doi.org/10.1016/S0262-8856(00)00034-2
  20. Park, J., and D. W. Edington, "A sequential Neural Network Model for Diabetes Prediction," Artificial Intelligence in Medicine, Vol.23, No.3(2001), 277-293. https://doi.org/10.1016/S0933-3657(01)00086-0
  21. Park, M. H., H. D. Ko, And H. R. Byun, "Analysis and Synthesis of Facial Expression using Base Faces", Journal of KIISE: Software and Application, Vol.27, No.8 (2000), 827-828.
  22. Park, W. K., I. Y. Choi, H. C. Ahn, and J. K. Kim, "A Study on Intelligent Interactive System Considering Visitor's Response for Providing Personalized Exhibition Service," Journal of the Korea Society of IT Services, Vol.6, No.3(2012), 118-132.
  23. Russell, J. A., "A Circumplex Model of Affect," Journal of Personality and Social Psychology, Vol.39, No.6(1980), 1161-1178. https://doi.org/10.1037/h0077714
  24. Ryoo, E C., H. Ahn, and J. K. Kim, "The Audience Behavior-based Emotion Perdition Model for Personalized Service," Journal of Intelligence and Information Systems, Vol.19, No.2(20123, 73-85. https://doi.org/10.13088/jiis.2013.19.2.073
  25. Shim, H. G., and S. K. Kim, "The Study on the Forecasting of Operation Continuous Ability Applied the Artificial Neural Network in Battalion Defensive Operations," Proceedings of Korea Institute of Industrial Engineers/The Korean Operations Research and Management Science Society, (2008), 1-6.
  26. Shin, D. I. Emotion Recognition Technology Trends, Weekly Technology Trends, Vol. 1283, (2007), 1-2.
  27. Wong, J.-J. and S.-Y. Cho, "A Local Experts Organization Model with Application to Face Emotion Recognition," Expert Systems with Application, Vol.36, No.1(2009), 804-819. https://doi.org/10.1016/j.eswa.2007.10.030
  28. Yoo, H., M.-Y. Kim, and O. Kwon, "Emotional Index Measurement Method for Context-aware Service," Expert Systems with Applications, Vol.38, No.1(2011), 785-793. https://doi.org/10.1016/j.eswa.2010.07.034
  29. Yoo, M. J., H. J. Kim, and I. K. Lee, "Music Exploring Interface using Emotional Model," Proceedings of 2009 HCI Korea Conference, (2009), 707-710.