Abstract
Emotional inference by recognizing facial expressions in an image or video has been actively conducted for several years, for example, visual emotion recognition and emotional state recognition. The limitations of illumination conditions, variations of head poses, and partial occlusion are the most challenging problems in image processing. Furthermore, recognizing and inferring insignificant objects in the background is complex. These factors degrade performance. We collected evidence from human sensing through a psychological approach rather than a numerical approach, and converted it into a language through fuzzy theory. This can not only be intuitively explained, but it is also possible to evaluate and analyze numerically. The predicted inference accuracy increased by approximately 9.7% compared with the simple average method.
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