Abstract
This paper proposes a generation method of facial expressions using fuzzy measure and fuzzy integral for robotic heads. Human’s emotion state can be represented by a fuzzy measure which can effectively deal with ambiguity. Because facial expressions are usually ambiguous such that it is difficult to discern emotions and assign a sharp boundary to each emotion. In this method, users can adjust the personality of robot by assignign fuzzy measure to every set of emotions. The partial evaluation values of the current emotion state are obtained from a difference between the ideal basic emotion states and the current emotion state. The Choquet integral of the partial evaluation values with respect to the fuzzy measure is calculated to decide which emotion should occur. The effectiveness of the proposed method is demonstrated through computer simulations and experiments with a robotic head with 19 degrees of freedom, developed in RIT Lab., KAIST.
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References
Muhl, C., Nagai, Y.: Does Disturbance Discourage People from Communication with a Robot? In: 16th IEEE International Conference on Robot & Human Interactive Communication, Jeju, Korea, pp. 1137–1142 (2007)
Mehrabian, A.: Nonverbal Communication. Aldine-Atherton (1972)
Mehrabian, A.: Communication without words. Psychology Today 2(4) (1968)
Breazeal, C.L.: Designing Sociable Robots. MIT Press, Cambridge (2002)
Park, J.-W., Kim, W.-H., Lee, W.-H., Kim, W.-H., Chung, M.-J.: Lifelike Facial Expression of Mascot-Type Robot based on Emotional Boundaries. In: IEEE Int. Conf. Robotics and Biomimetics, Guilin, China, pp. 830–835 (2009)
Matsui, Y., Kanoh, M., Kato, S., Nakamura, T., Itoh, H.: Evaluating A Model for Generating Interactive Facial Expressions using Simple Recurrent Network. In: IEEE Int. Conf. Syst. Man Cybern., Texas, USA, pp. 1639–1644 (2009)
Bui, T., Heylen, D., Poel, M., Nijholt, A.: Generation of Facial Expressions from Emotion Using a Fuzzy Rule Based System. In: 14th Australian Joint Conf. Artificial Intelligence, Adelaide, Australia (2001)
Ekman, P., Friesen, W.: Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto (1978)
Sugeno, M.: Theory of fuzzy integrals and its applications. Ph.D. dissertation, Tokyo Institute of Technology (1974)
Kim, J.-H., Han, J.-H., Kim, Y.-H., Choi, S.-H., Kim, E.-S.: Preference-based Solution Selection Algorithm for Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computations (December 2010) (accepted)
Grabisch, M., Nguyen, H.-T., Walker, E.-A.: Fundamentals of uncertainty Calculi, with Applications to Fuzzy Inference. Kluwer, Dordrecht (1995)
Ekman, P., Friesen, W.: Unmasking the face: A Guide to Recognizing Emotions From Facial Expressions. Prentice Hall, Englewood Cliffs (1975)
Takahagi, E.: On identification methods of λ-fuzzy measures using weights and λ. Journal of Japan Society for Fuzzy Theory and Systems 12(5), 665–676 (2000)
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Yoo, BS., Cho, SH., Kim, JH. (2011). Facial Expression Generation Using Fuzzy Integral for Robotic Heads. In: Li, TH.S., et al. Next Wave in Robotics. FIRA 2011. Communications in Computer and Information Science, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23147-6_8
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DOI: https://doi.org/10.1007/978-3-642-23147-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23146-9
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