Facial Emotion Recognition of Mentally Retarded Children to Aid Psychotherapist

Emotions play a crucial role in human communication and behavior, making the accurate detection and analysis of emotions an important research area in artificial intelligence. Human emotion recognition is a field of research which can be used in online education systems as online education continues to grow, understanding how students feel during the learning process is becoming increasingly important. Detecting and analyzing human emotions can provide valuable insight into student engagement, motivation, and satisfaction. There are many datasets and many methods to carry out the process of detecting human emotions. This paper proposes a human emotion detection system that utilizes machine learning techniques to recognize and classify human emotions from facial expressions of mentally retarded children and generate a report of the emotion detected, which can be used by the psychotherapist for treatment purposes. The system extracts relevant features from input data and applies machine learning algorithms to classify emotions into several categories such as happiness, sadness, anger, fear, and disgust. This experiment is carried out in MATLAB software. This system aims to achieve high accuracy in emotion.