Toward Computational Model of Emotion from Individual Difference in Perceiving Facial Expressions
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We propose a computational model for identifying emotional state of a facial expression from appraisal scores given by human observers utilizing their differences in perception. The appraisal model of human emotion is adopted as the basis of this evaluation process with appraisal variables as output. We investigated the performance for both categorical and continuous representation of the variables appraised by human observers. Analysis of the data exhibits higher degree of agreement between estimated Indian ratings and the available reference when these are rated through continuous domain. We also observed that emotional state with negative valence are influential in the perception of hybrid emotional state like ‘Surprise’, only when appraisal variables are labeled through categories of emotions. Thus, the proposed method has implications in developing software to detect emotion using appraisal variables in continuous domain, perceived from facial expression of an agent (or human subject). Further, this model can be customized to include cultural variability in recognizing emotions.
KeywordsAppraisal variable Computational model Emotion perception Facial expressions Kernel density estimation
The authors would like to thank the subjects participated in the experiments. This research work is self funded.
- Baldassarri, S., & Cerezo, E. (2012). Maxine: Embodied conversational agents for multimodal emotional communication. In N. Mukai (Ed.), Computer Graphics (pp. 195–212). Croatia: Intech.Google Scholar
- Barrett, L. F. (2011). Constructing emotion. Psychological Topics, 20(3), 359–380.Google Scholar
- Bowman, A. W., & Azzalini, A. (1997). Applied smoothing techniques for data analysis. Oxford: Clarendon Press.Google Scholar
- Dailey, M. N., Lyons, M. J., Ishi, H., Joyce, C., Gyoba, J., & Cottrell, G. W. (2010). Evidence and a computational explanation of cultural differences in facial expression recognition. Emotion, American Psychological Association, 10(6), 874–893.Google Scholar
- Ekman, P. (1972). Universals and cultural differences in facial expressions of emotions. In Cole, J. (Ed.), Nebraska Symposium on Motivation (pp. 207–282). Lincoln, NB: University of Nebraska Press.Google Scholar
- Engelmann, J. B., & Pogosyan, M. (2013). Emotion perception across culture: The role of cognitive mechanisms. Frontiers in Psychology, 4, 118. https://doi.org/10.3389/fpsyg.2013.00118.
- Lin, J., Spraragen, M., & Zyda, M. (2012). Computational models of emotion and cognition. Advances in Cognitive Systems, 2(1), 59–76.Google Scholar
- Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The extended Cohn–Kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, (pp. 94–101). San Francisco, CA: IEEE.Google Scholar
- Lyons, M., Akamatsu, S., Kamachi, M., & Gyoba, J. (1998). Coding facial expressions with gabor wavelets. In Third IEEE International Conference on Automatic Face and Gesture Recognition. IEEE, pp. 1–6.Google Scholar
- Martinez, A., & Du, S. (2012). A model of the perception of facial expressions of emotion by humans: Research overview and perspectives. Machine Learning Research, 13(1), 1589–1608.Google Scholar
- Mesquita, B., Vissers, N., & De Leersnyder, J. (2015). Culture and amotion. In J. Wright & J. Berry (Eds.), International encyclopedia of social and behavioral sciences (pp. 542–549). New York: Elsevier.Google Scholar
- Robinson, D. L. (2009). Brain function, mental experience and personality. The Netherlands Journal of Psychology, 64(1), 152–167.Google Scholar
- Russell, J. A. (1995). Facial expressions of emotion: What lies beyond minimal universality? Psychological Bulletin, 118(3), 379–391.Google Scholar
- Scott, D. W. (2015). Multivariate density estimation (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
- Silverman, B. W. (1998). Density estimation for statistics and data analysis. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Chapman and Hall/CRC.Google Scholar
- Tian, Y., Kanade, T., & Cohn, J. F. (2011). Facial expression recognition. In S. Z. Li & A. K. Jain (Eds.), Handbook of face recognition (pp. 487–519). Springer: London.Google Scholar
- Wehrle, T., & Scherer, K. R. (2001). Towards computational modeling of appraisal theories. In K. R. Scherer, A. Schorr, & T. Johnstone (Eds.), Appraisal processes in emotions: Theory, methods, research (pp. 350–365). New York: Oxford University Press.Google Scholar
- Whissell, C . M. (1989). The dictionary of affect in language. In R. Plutchik & H. Kellerman (Eds.), Emotion: Theory, research, and experience (pp. 112–131). New York: Academic Press.Google Scholar
- Woolfolk, A. (2006). Educational psychology (9th ed.). New York: Pearson Education.Google Scholar