A Fuzzy Logic Approach in Emotion Detection and Recognition and Formulation of an Odor-Based Emotional Fitness Assistive System

This paper aims at a Fuzzy relational approach for similar emotions expressed by different subjects by facial expressions and predefined parameters. Different Facial attributes contribute to a wide variety of emotions under varied circumstances. These same features also vary widely from person to person, introducing uncertainty to the process. Facial features like eye-opening, mouth-opening and length of eye-brow constriction from localized areas from a face are Fuzzified and converted into emotion space by employing relational models. This is dealt with Fuzzy Type-2 logic, which reigns supreme in reducing uncertainty.