# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
###### Abstract  
Emotions play a vital role in education. Technological advancement in computer vision using deep learning models has improved automatic emotion recognition. In this study, a real-time automatic emotion recognition system is developed incorporating novel salient facial features for classroom assessment using a deep learning model. The proposed novel facial features for each emotion are initially detected using HOG for face recognition, and automatic emotion recognition is then performed by training a convolutional neural network (CNN) that takes real-time input from a camera deployed in the classroom. The proposed emotion recognition system will analyze the facial expressions of each student during learning. The selected emotional states are happiness, sadness, and fear along with the cognitive-emotional states of satisfaction, dissatisfaction, and concentration. The selected emotional states are tested against selected variables gender, department, lecture time, seating positions, and the difficulty of a subject. The proposed system contributes to improve classroom learning.  
automatic emotion recognition; deep learning in education; facial expression recognition system; deep learning; CNN +
Footnote †: journal: Medical Image and Vision  
The researcher [PERSON] conducted the first study and demonstrated that facial expressions were reliably associated with certain emotional states [2]. Later, [PERSON] recruited [PERSON] and [PERSON] to work on this phenomenon, they performed a compressive study regarding facial expression and emotions which are known as \"universality studies\". [PERSON] proposed universal facial expressions (happiness, sadness, disgust, anger, fear, and neutral) and claimed that these were present in every human belonging to any culture, these expressions were later agreed to be universal expressions [3].  
Facial expressions are powerful tools to analyze emotions due to the expressive behavior of the face, with the possibility of capturing rich content in multi-dimensional views. Humans can use the movement of facial features to express their desired inner feelings and mental states.  
Emotions play a vital role in all human activities, recent developments in neurology have shown that there is a connection between emotions, cognition, and audio functions [4]. Emotions have a strong impact on learning and thus play a vital role in education [5; 6; 7]. Emotions do not just impact traditional classroom learning but influence various types of learning such as language learning, skills learning, ethics learning, etc.  
A human possesses different emotions, and each has a different role in different situations, it can therefore be said that there is no limit to the emotions affecting a classroom environment. An educator needs to analyze their student's facial expressions with a deep understanding of the surrounding environment during learning in order to improve the learning process [8; 9].  
Research studies have depicted the relationship between a students' emotions explicitly expressed through the face and their academic performances; and have demonstrated the importance of considering the quadratic relationship between a students' positive emotions during learning and their academic grades [10; 11; 12; 13; 14; 15; 16]. Good learning can have a positive impact on the academic grades of the learner [17] while difficulty in learning can lower a student's academic grades and can cause dropout [18].