A Fuzzy Logic Approach in Emotion Detection and Recognition and Formulation of an Odor-Based Emotional Fitness Assistive System | IGI Global Scientific Publishing
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A Fuzzy Logic Approach in Emotion Detection and Recognition and Formulation of an Odor-Based Emotional Fitness Assistive System

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

Sudipta Ghosh (CIEM, Kolkata, India), Debasish Kundu (ETI, Hoogly, India), and Gopal Paul (IIT Kharagpur, Kharagpur, India)
Copyright: © 2015 |Volume: 6 |Issue: 2 |Pages: 21
ISSN: 1947-9093|EISSN: 1947-9107|EISBN13: 9781466678125|DOI: 10.4018/IJSE.2015070102
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MLA

Ghosh, Sudipta, et al. "A Fuzzy Logic Approach in Emotion Detection and Recognition and Formulation of an Odor-Based Emotional Fitness Assistive System." IJSE vol.6, no.2 2015: pp.14-34. https://doi.org/10.4018/IJSE.2015070102

APA

Ghosh, S., Kundu, D., & Paul, G. (2015). A Fuzzy Logic Approach in Emotion Detection and Recognition and Formulation of an Odor-Based Emotional Fitness Assistive System. International Journal of Synthetic Emotions (IJSE), 6(2), 14-34. https://doi.org/10.4018/IJSE.2015070102

Chicago

Ghosh, Sudipta, Debasish Kundu, and Gopal Paul. "A Fuzzy Logic Approach in Emotion Detection and Recognition and Formulation of an Odor-Based Emotional Fitness Assistive System," International Journal of Synthetic Emotions (IJSE) 6, no.2: 14-34. https://doi.org/10.4018/IJSE.2015070102

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Abstract

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.

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