Abstract
In this research, the authors succeeded in creating facial expressions made with the minimum necessary elements for recognizing a face. The elements are two eyes and a mouth made using precise circles, which are transformed to make facial expressions geometrically, through rotation and vertically scaling transformation. The facial expression patterns made by the geometric elements and transformations were composed employing three dimensions of visual information that had been suggested by many previous researches, slantedness of the mouth, openness of the face, and slantedness of the eyes. In addition, the relationships between the affective meanings of the visual information also corresponded to the results of the previous researches.
The authors found that facial expressions can be classified into 10 emotions: happy, angry, sad, disgust, fear, surprised, angry*, fear*, neutral (pleasant) indicating positive emotion, and neutral (unpleasant) indicating negative emotion. These emotions were portrayed by different geometric transformations. Furthermore, the authors discovered the “Tetrahedral model,” which can express most clearly the geometric relationships between facial expressions. In this model, each side connecting the face is an axis that controlled the rotational and vertically scaling transformations of the eyes and mouth.
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Acknowledgement
This work was supported by Grant-in-Aid for Scientific Research on Innovative Areas No. 18H04203 “Construction of the Face-Body Studies in Transcultural Conditions”.
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Kanaya, I., Tawaki, M., Yamamoto, K. (2020). Cross-Cultural Design of Facial Expressions of Robots. In: Rau, PL. (eds) Cross-Cultural Design. User Experience of Products, Services, and Intelligent Environments. HCII 2020. Lecture Notes in Computer Science(), vol 12192. Springer, Cham. https://doi.org/10.1007/978-3-030-49788-0_45
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