A Thermal Facial Emotion Database and Its Analysis

  • Hung Nguyen
  • Kazunori Kotani
  • Fan Chen
  • Bac Le
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

In recent years, thermal image has extensively been used in many fields such as military (e.g., target acquisition, surveillance, night vision, homing and tracking) and civilian purposes (e.g., medical diagnosis, thermal efficiency analysis, environmental monitoring). It may be a promising alternative for investigation of facial expression and emotion. Currently there are very few database to support the research in facial expression and emotion, however most of them either only include posed thermal expression images or lack thermal information. For these reasons, we propose and establish a natural visible and thermal facial emotion database. The database contains seven spontaneous emotions of 26 subjects. We also analyze a visible database, a thermal database to recognize expression and thermal information to recognize emotion.

Keywords

Facial expression analysis thermal image visible image spontaneous database facial emotion KTFE database 

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References

  1. 1.
    Darwin, C.: The expression of the emotions in man and animals. Oxford University, New York (1872)CrossRefGoogle Scholar
  2. 2.
    Tian, Y., Kanade, T., Cohn, J.: Facial Expression Analysis, Handbook of Face Recognition, pp. 247–275. Springer, New York (2005)CrossRefGoogle Scholar
  3. 3.
    Zeng, Z., Pantic, M., Roisman, G.T., Huang, T.S.: A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009)CrossRefGoogle Scholar
  4. 4.
    Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recognition 36, 259–275 (2003)CrossRefMATHGoogle Scholar
  5. 5.
    Khan, M.M., Ward, R.D., Ingleby, M.: Classifying pretended and evoked facial expression of positive and negative affective states using infrared measurement of skin temperature. Trans. Appl. Percept. 6(1), 1–22 (2009)CrossRefGoogle Scholar
  6. 6.
    Nakanishi, R., Matsumura, K.I.: Facial skin temperature decreases in infants with joyful expression. Infant Behavior and Development 31, 137–144 (2008)CrossRefGoogle Scholar
  7. 7.
    Kanade, T., Cohn, J., Tian, Y.: Comprehensive database for facial expression analysis. In: Proc. IEEE Intl. Conf. Face and Gesture Recognition, pp. 46–53 (2000)Google Scholar
  8. 8.
    Sebe, N., Lew, M.S., Cohen, I., Sun, Y., Gevers, T., Huang, T.S.: Authentic Facial Expression Analysis. In: Proc. IEEE Intl. Conf. Automatic Face and Gesture Recognition, AFGR (2004)Google Scholar
  9. 9.
    Pantic, M., Bartlett, M.S.: Machine Analysis of Facial Expressions, Face Recognition, pp. 377–416. I-Tech Education and Publishing, Vienna (2007)Google Scholar
  10. 10.
    O’Toole, A.J., Harms, J., Snow, S.L., Hurst, D.R., Pappas, M.R., Ayyad, J.H., Abdi, H.: A video database of moving faces and people. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 812–816 (2005)CrossRefGoogle Scholar
  11. 11.
    Douglas-Cowie, E., Cowie, R., Schroeder, M.: The description of naturally occurring emotional speech. In: Proc. 15th Int. Conf. Phonetic Sciences, Barcelona, Spain, pp. 2877–2880 (2003)Google Scholar
  12. 12.
    Roisman, G.I., Tsai, J.L., Chiang, K.S.: The emotional integration of childhood experience: Physiological, facial expressive, and self-reported emotional response during the adult attachment interview. Development Psychol. 40(5), 776–789 (2004)CrossRefGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
    Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Chen, F., Wang, X.: A Natural Visible and Infrared Facial Expression Database for Expression Recognition and Emotion Inference. IEEE Transactions on Multimedia 12(7), 682–691 (2010)CrossRefGoogle Scholar
  16. 16.
    Lin, D.T.: Facial Expression Classification Using PCA and Hierarchical Radial Basis Function Network. Journal of Information Science and Engineering 22, 1033–1046 (2006)Google Scholar
  17. 17.
    Kurozumi, T., Shinza, Y., Kenmochi, Y., Kotani, K.: Facial Individuality and Expression Analysis by Eigenspace Method Based on Class Features or Multiple Discriminant Analysis. In: ICIP (1999)Google Scholar
  18. 18.
    Yabui, T., Kenmochi, Y., Kotani, K.: Facial expression analysis from 3D range images; comparison with the analysis from 2D images and their integration. In: ICIP (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hung Nguyen
    • 1
  • Kazunori Kotani
    • 1
  • Fan Chen
    • 1
  • Bac Le
    • 2
  1. 1.Japan Advanced Institute of Science and TechnologyNomiJapan
  2. 2.University of Science, Ho Chi Minh cityHo Chi Minh cityVietnam

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