Development of model for automatic tutor in e-learning environment based on student reactions extraction using facial recognition

Emotion recognition refers to the process of identifying the emotions expressed by an individual, typically through their facial expressions, speech, body language, and sometimes physiological signals like heart rate or skin conductance. In this chapter, facial expression is used to recognise. Emotions like happiness, sadness, anger, fear, surprise, and disgust are typically recognized. This chapter aims at developing a real-time approach to classification of facial emotions such as happy, normal, yawn, and sleep in a real-time context. For this, images are captured using sensors and stored in a cloud storage bucket in which the processing is done. The facial emotions are identified through the use of Haar cascade classifiers. The histogram-oriented gradients features are extracted in the detected facial emotion images, and the extracted features are classified by using machine learning models support vector machine and k-nearest neighbour classifiers as happy, normal, yawn, and sleep. The suggested system outperforms other current systems when tested with real-time datasets.

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Development of model for automatic tutor in e-learning environment based on student reactions extraction using facial recognition

Cited by 2 publications

References 4 publications

Behavioral Analysis of Students by Integrated Radial Curvature and Facial Action Coding System using DCNN

Behavioral Analysis of Students by Integrated Radial Curvature and Facial Action Coding System using DCNN

Facial Emotion Recognition Using Osmotic Computing

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