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The application of user emotion recognition for fear is expanding in various fields, including the quantitative evaluation of horror movies, dramas, advertisements, games, and the monitoring of emergency situations in convenience stores (i.e., a clerk threatened by a robber), in addition to criminal psychology. Most of the existing methods for the recognition of fear involve referring to a single physiological signal or recognizing circumstances in which users feel fear by selecting the most informative one among multiple physiological signals. However, the level of accuracy as well as the credibility of these study methods is low. Therefore, in this study, data with high credibility were obtained using non-intrusive multimodal sensors of near-infrared and far-infrared light cameras and selected based on t-tests and Cohen’s d analysis considering the symmetrical characteristics of face and facial feature points. The selected data were then combined into a fuzzy system using the input and output membership functions of symmetrical shape to ultimately derive a new method that can quantitatively show the level of a user’s fear. The proposed method is designed to enhance conventional subjective evaluation (SE) by fuzzy system based on multi-modalities. By using four objective features except for SE and combining these four features into a fuzzy system, our system can produce an accurate level of fear without being affected by the physical, psychological, or fatigue condition of the participants in SE. After conducting a study on 20 subjects of various races and genders, the results indicate that the new method suggested in this study has a higher level of credibility for the recognition of fear than the methods used in previous studies.

Fuzzy System-Based Fear Estimation Based on the Symmetrical Characteristics of Face and Facial Feature Points