Affective Computing, Focus on Emotion Expression, Synthesis by Jimmy Or (Editor)

By Jimmy Or (Editor)

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Extra info for Affective Computing, Focus on Emotion Expression, Synthesis and Recognition

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P. Chandrasiri, T. Naemura, and H. Harashima. Interactive analysis and synthesis of facial expressions based on personal facial expression space. In IEEE International Conference on Automatic Face and Gesture Recognition, 2004. I. Cohen, N. Sebe, A. Garg, L. S. Huang. Facial expression recognition from video sequences: Temporal and static modeling. , 91(1-2):160-187, 2003. F. Dornaika and F. Davoine. View- and texture-independent facial expression recognition in videos using dynamic programming.

Y. Zhang and Q. Ji. Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):699-714, 2005. S. Zhou, R. Chellappa, and B. Mogghaddam. Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Transactions on Image Processing, 13(11):1473-1490, 2004. S. Zhou, V. Krueger, and R. Chellappa. Probabilistic recognition of human faces from video. Com- puter Vision and Image Understanding, 91(1-2):214-245, 2003.

Specifically, facial expressions also signal behavioral intentions - such as intentions to approach or to avoid - as well as personality characteristics of the person such as dominance and affiliativeness. For example, anger signals dominance and an intention to approach, whereas fear signals submissiveness and an intention to withdraw. In this sense facial expressions not only provide information on what emotion a person is feeling, but also tell us about who a person is and what to expect from them behaviorally.

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