DOI QR코드

DOI QR Code

A study on the enhancement of emotion recognition through facial expression detection in user's tendency

사용자의 성향 기반의 얼굴 표정을 통한 감정 인식률 향상을 위한 연구

  • Lee, Jong-Sik (Department of interaction Science, Sungkyunkwan University) ;
  • Shin, Dong-Hee (Department of interaction Science, Sungkyunkwan University)
  • 이종식 (성균관대학교 인터렉션 사이언스학과) ;
  • 신동희 (성균관대학교 인터렉션 사이언스학과)
  • Received : 2013.12.18
  • Accepted : 2014.03.14
  • Published : 2014.03.30

Abstract

Despite the huge potential of the practical application of emotion recognition technologies, the enhancement of the technologies still remains a challenge mainly due to the difficulty of recognizing emotion. Although not perfect, human emotions can be recognized through human images and sounds. Emotion recognition technologies have been researched by extensive studies that include image-based recognition studies, sound-based studies, and both image and sound-based studies. Studies on emotion recognition through facial expression detection are especially effective as emotions are primarily expressed in human face. However, differences in user environment and their familiarity with the technologies may cause significant disparities and errors. In order to enhance the accuracy of real-time emotion recognition, it is crucial to note a mechanism of understanding and analyzing users' personality traits that contribute to the improvement of emotion recognition. This study focuses on analyzing users' personality traits and its application in the emotion recognition system to reduce errors in emotion recognition through facial expression detection and improve the accuracy of the results. In particular, the study offers a practical solution to users with subtle facial expressions or low degree of emotion expression by providing an enhanced emotion recognition function.

인간의 감정을 인식하는 기술은 많은 응용분야가 있음에도 불구하고 감정 인식의 어려움으로 인해 쉽게 해결되지 않는 문제로 남아 있다. 인간의 감정 은 크게 영상과 음성을 이용하여 인식이 가능하다. 감정 인식 기술은 영상을 기반으로 하는 방법과 음성을 이용하는 방법 그리고 두 가지를 모두 이용하는 방법으로 많은 연구가 진행 중에 있다. 이 중에 특히 인간의 감정을 가장 보편적으로 표현되는 방식이 얼굴 영상을 이용한 감정 인식 기법에 대한 연구가 활발히 진행 중이다. 그러나 지금까지 사용자의 환경과 이용자 적응에 따라 많은 차이와 오류를 접하게 된다. 본 논문에서는 감정인식률을 향상시키기 위해서는 이용자의 내면적 성향을 이해하고 분석하여 이에 따라 적절한 감정인식의 정확도에 도움을 주어서 감정인식률을 향상 시키는 메카니즘을 제안하였으며 본 연구는 이러한 이용자의 내면적 성향을 분석하여 감정 인식 시스템에 적용함으로 얼굴 표정에 따른 감정인식에 대한 오류를 줄이고 향상 시킬 수 있다. 특히 얼굴표정 미약한 이용자와 감정표현에 인색한 이용자에게 좀 더 향상된 감정인식률을 제공 할 수 있는 방법을 제안하였다.

Keywords

References

  1. Kim, M. H. & Park, J. B. (2005). Development of Emotion Recongition System Using Facial Image. Proceedings of Korea Fuzzy Logic and Intelligent Systems Vol. 15, No. 2, pp. 191-196
  2. Kang, D. H. & Lee, M. L. (2008). Face recognition using PCA and KNN for improving research. Proceedings of KIIS Fall Conference 2008 Vol. 18. No. 2
  3. Shin, D. I. (2007). Trends of Emotion Recognition Technology. Technology Trends Week Vol 1283 2007, pp. 1-9
  4. Shin, Y. S. (2006). Independent neutral facial expression recognition. Proceedings of Korea Computer Vol.33, No.1(B)
  5. Lee, D. J. & LEE, K. A. (2005). Face Emotion Recognition by Fusion Model based on Static and Dynamic Image. Proceedings of Korea Fuzzy Logic and Intelligent Systems Vol 15, No. 5, pp.573-580
  6. JOO, J. T. (2007). Emotion Recognition and Expression using Facial Expression. Proceedingsof KFIS Spring Conference Vol. 17, No.1 pp. 295-298
  7. Joo, Y. H. (2004). Facial Image Analysis Algorithm for Emotion Recognition. Proceedings of Korea Fuzzy Logic and Intelligent Systems Vol. 14, No. 7, pp. 801-806
  8. Shim, K. B. & Park, C. H. (2001). Analyzing the element of emotion recognition from speech. Proceedings of Korea Fuzzy Logic and Intelligent Systems Vol 11, No6, pp.510-515
  9. Ko, H. J. (2004). Emotion Recognition Method from Speech Signal Using the Wavelet Transform. Proceedings of Korea Fuzzy Logic and Intelligent Systems Vol. No 2, pp.150-155
  10. Vogt, T. & Elisabeth, A.(2006). Improving automatic emotion recognition from speech via gender differentiation. Proc. Language Resources and Evaluation Conference (LREC 2006), Genoa.
  11. Bos, D. O. (2006). EEG-based emotion recognition. The Influence of Visual and Auditory Stimuli pp 1-17.
  12. P. Ekman,(1992). An Argument for Basic Emotions. Basic Emotions, N. Stein and K.Oatley, eds. Lawrence Erlbaum, pp. 169-200.
  13. Hancock, P. A., & I. Vasmatzidis, I. (2003). Effects of Heat Stress on Cognitive Performance: The Current State of Knowledge. International Journal of Hyperthermia, 19(3), 355?372. https://doi.org/10.1080/0265673021000054630
  14. Terracciano, A. & Merritt, M. & Zonderman, A. B. & Evans, M. K. (2003). Personality traits and sex differences in emotion recognition among African Americans and Caucasians. Annals of the New York Academy of Sciences, 1000(1), 309-312.
  15. Matsumoto, D. & Kudoh, T. (1993). American-Japanese cultural differences in attributions of personality based on smiles. Journal of Nonverbal Behavior, 17(4), 231-243. https://doi.org/10.1007/BF00987239
  16. Frijda, N. H. (1986). The emotions. Cambridge University Press
  17. Lang, P. J. (1995). The emotion probe: Studies of motivation and attention. American psychologist, 50(5), 372. https://doi.org/10.1037/0003-066X.50.5.372
  18. Rotter, N. G. & Rotter, G. S. (1988). Sex differences in the encoding and decoding of negative facial emotions. Journal of Nonverbal Behavior, 12(2), 139-148. https://doi.org/10.1007/BF00986931
  19. Fivush, R. & Brotman, M. A. & Buckner, J. P. & Goodman, S. H. (2000). Gender differences in parent?child emotion narratives. Sex Roles, 42(3-4), 233-253. https://doi.org/10.1023/A:1007091207068
  20. Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4), 169-200. https://doi.org/10.1080/02699939208411068
  21. M. Deivamani, R.Baskaran, P. Dhavachelvan (2008). Improving Emotion Recognition with a Learning Multi-agent system. Department of Computer Science & Engineering, Anna University, Chennai, India
  22. Hancock, P. J. B & Burton, A. M. & Bruce, V.(1996). Face Processing : human perception and principal component analysis. Memory and Cognition, Vol.24, No.1, p26-40 https://doi.org/10.3758/BF03197270
  23. Lu, J. & Plataniotis, K. N. & Venetsanopoulos, A. N. (2003). Face recognition using LDA-based algorithms. Neural Networks, IEEE Transactions on, 14(1), 195-200. https://doi.org/10.1109/TNN.2002.806647
  24. Lee, T. W. (1998). Independent component analysis, theory and applications.
  25. Shim, K. B. (2008). Emotion Recognition and Expression System of User using Multi-Modal Sensor Fusion Algorithm. Korea Fuzzy Logic and Intelligent Systems, 18(1), 20-26 https://doi.org/10.5391/JKIIS.2008.18.1.020
  26. Picard, R. W. & Vyzas, E. & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175.1191. https://doi.org/10.1109/34.954607
  27. Keltner, D. (1996). Facial expressions of emotion and personality. Handbook of emotion, adult development, and aging, 385-401.
  28. Ball, G., & Breese, J. (2000). Emotion and personality in a conversational agent. Embodied conversational agents, 189-219.
  29. Fraunhofer IIS SHORETM http://www.iis.fraunhofer.de
  30. MBTI test (Demo Version) http://mbtitest.net/sub/-