Development of a direct mapping model between hedonic rating and facial responses by dynamic facial expression...

Consumer tests are one of the most important activities in product development. More evidence indicates that consumer emotions in real life are mostly driven by unconscious mechanisms, and implicit measurements are regarded as beneficial by an increasing number of sensory and consumer scientists. Nonverbal manner such as facial expression analysis is a supplement to the declarative method and brings very insightful results. Up until now, the facial expression analysis for consumers' acceptance identification is limited to investigate the relationship between hedonic rating and facial expression descriptors, such as facial coding system (FACS or MAX), discrete facial expressions (i.e. happiness, sadness, surprise, fear, anger, and disgust), and affective dimensional model (valence and activation). In this study, we attempt to develop a direct mapping model between the hedonic rating and facial responses evoked by various taste stimuli. Basic taste solutions (sourness, sweetness, bitterness, umami, and saltiness) with six levels, and five types of juice are used as stimuli. Firstly, the hedonic rating categories are defined based on the nine-point hedonic scale, with a coarse-to-fine division of scale levels based on two directions of like and dislike. Secondly, the facial dynamic optical flow method is employed to analyze facial characteristics of the subjects' facial responses evoked by taste stimuli. And the genetic algorithm is conducted to select facial regions that have high contribution to hedonic rating identification. It indicates that the texture changes of eye area, wrinkles at the nasal root, and mouth area can effectively reflect the facial reaction corresponding to hedonic rating. The research shows that it is feasible to establish a direct mapping model between hedonic rating and facial responses. The hedonic rating can be predicted through automatic facial reading technology, without extra transformation from predefined emotional models. In general, this is the first try to discuss the direct prediction of hedonic rating through facial expressions up to now, and it is a complex problem due to various influence factors.