Natural Computing of Human Facial Emotion Using Multi-Learning Fuzzy Approach

Natural Computing of Human Facial Emotion Using Multi-Learning Fuzzy Approach

Praveen Kulkarni, Rajesh T. M.
Copyright: © 2021 |Pages: 17
DOI: 10.4018/IJNCR.2021100103
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Abstract

Emotions are described as strong feelings that are expressed by an individual in response to reactions to something or someone. Emotions are a very important aspect of day-to-day life interaction. Research shows that more than 90% of communication will happen non-verbally. This paper presents human emotion detection using a fuzzy relational model. The model consists of an image processing stage followed by an emotion recognition phase. The authors additionally made sub-categories in the most important expressions like happy and sad to discover the level of happiness and sadness in one face. Feature extraction along with multi-learning approach will help to test whether the person is truly happy or appearing to be happy. Experimental outcomes on the image dataset point out the accurate performance of the proposed technique. The experiment gives good accuracy results with the authors' own data set and robust with reference to some latest and leading edge.
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Introduction

People have used non-verbal communication like gestures, pruning, and facial expressions to communicate their thoughts and feelings. In human experiences, the emotional state affects interpretation and rational decision-making. Therefore, it is necessary to have an uncomfortable relationship or correspondence and automatically recognize emotions. Despite significant advances in the field of emotion analysis, assessment is an important prerequisite for classifying emotions reflected on a person's face in areas such as student orientation, criminal assessment, rating unit for patients with psychiatric disorders, and music therapy. For patients with depression and intellectual disabilities in others, Emotion recognition technology is now being used to help health professionals diagnose and demonstrate driver disabilities, test gamers' experience in video games, and assess patient wellbeing. We see recruitment agencies using these methods to determine if a candidate is suitable for the job. Corporations and universities around the world are researching emotional focus and evaluation. The assessment of emotions has the major disadvantage that simulations have to be carried out by humans to a certain extent so that conscious effects can be recognized incorrectly or intentionally.

Methods used include body language, philological patterns, and facial expressions. (Ekman and Frison, 1978) authors analyzed the face and introduced the face stack coding scheme, which is now a widely used imitation for facial expressions. Some researchers, including the famous psychologist Paul Ekman, have investigated whether facial expressions can show real human emotions. The researchers made a distinction between a real smile (known as the “Duchenne smile” after the French doctor Guillaume Duchene) and a “false” smile. Not only does Frank smile in the corners of his mouth (this can be easily repeated), he also increases the muscles of the cheeks and eyes (harder than wrong). One way to tell if someone is really happy or having fun is by looking at the muscles around their cheeks and looking up in their eyes. When a person's eyes smile instead of gnawing, the laugh is considered real. While most of us are happy we also have many surprises and fears feel sad, alone, or sad time. These are the most common reactions to losing things, difficulties in life, or self-esteem that hurt a lot these feelings can be caused over long periods physical symptoms prevent a person from moving forward a normal and active life with instructions depression. Sadness is a common human emotion and is common injured, frustrated, or excited by what happened heavy. Depression is an abnormal emotional state it influences our thinking, our perception, and our behavior broad and old-fashioned. The traditional approach in building any system controllers requires a prior model of the system. The quality of the model, i.e., accuracy loss due to linearization and/or uncertainties in the system's parameters, has a detrimental impact on the quality of the control that results. In addition, flexible calculation methods such as fuzzy logic have non-linear mapping functions, do not require analytical modeling, and can deal with uncertainties in system parameters. Although fuzzy logic is precise information; the information is processed into a solid mathematical theory. Based on the nature of fuzzy human thinking, “Fuzzy Logic” or “Fuzzy Set Theory” arose in 1965. Ambiguous thinking refers to problems of ambiguity or ambiguity.

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