Study for Emotion Recognition of Different Age Groups Students during Online Class

Student's learning and education is the key for their success. Teachers always judge students attentiveness in class by their facial expressions which shows their interest in the class. But when we look at present, due to COVID-19, students are learning totally on online platform. During these classes, teachers can see students only through their video cameras and it is difficult to know level of understanding of students, therefore they can be judged by their various emotions such as happy, sad, disinterested, frustration, neutral, confusion, anger, disgust, surprise and learning. It becomes compulsory for educators to identify the state of mind of students during online class by their emotion recognition. This paper presents a review for different facial expressions, body parts and gestures through which identification can be done. With the help of Computer vision and deep learning techniques this is identified by tool in which student's image is captured by video camera and further applying feature extraction and classification techniques. This results in benefitting to both students and faculty for easy execution of online classes. Implementation results shows that emotions recognized through image classification can make better learning outcomes for students.