# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
### Automatic Emotion Recognition System  
The most significant components of human emotions are input into machines so that they can automatically detect the emotions expressed by a human through their facial features. The advent of, and advancement in, automatic facial expression recognition systems can tremendously increase the amount of processed data. The real-time implementation of the FER can boost society. The FER can be applied in various areas such as mental disease diagnosis, human social/physiological interaction detection, criminology, security systems, customer satisfaction, human-computer interaction, etc. There are many computing techniques that are used to automate facial expression recognition [19]. The most implemented and successful technique is \"deep learning\" [20; 21; 22; 23; 24]. Deep learning is basically a subfield of machine learning using artificial intelligence that imitates the biological processes of the human brain and deals with algorithms inspired by the structure and function of the human brain known as artificial neural networks. [PERSON], on his Brussels 2014 TEDx talk, states that computers trained using deep learning techniques have been able to achieve some effective computing processes that are similar to human emotion recognition and which are necessary for machines to better serve their purpose [25]. The facial expression recognition systems use either static images or videos. Most applications of facial expression recognition systems use imagery datasets [26; 27]. It has been observed that the imagery dataset requires a trained person to label the images into discrete emotions and that, moreover, these discrete emotions do not cover the micro-expression of the individual [28]. The research argues that analyzing facial expressions using a huge imagery dataset with unnecessary input dimensions can decrease the efficiency of the FER system. Similarly, another study concludes that images taken from different angles, low resolution, and noisy backgrounds can be problematic in automatic facial expression recognition [29; 30; 31; 32; 33]. Another research has argued that static images are not sufficient for automatic facial expression recognition and the authors conducted a study using recorded video of the classroom to improve the accuracy of FER [34; 35; 36; 37]. One of the researchers has divided their proposed emotion detection system into positive and negative emotions [38]; they used two positive facial expressions happiness and surprise and five negative expressions of anger, contempt, disgust, fear, and sadness to analyze the impact of negative and positive emotions on students' learning. Another study was conducted to analyze students' facial expressions in the classroom using deep learning, focused on the concentration level and interest of learners using eye and head movement [39]. There are many deep learning techniques used in building automatic facial expression recognition systems. The convolutional neural network (CNN) is one of the most efficient and widely used models to develop an automatic emotion recognition system [40; 41; 42; 43; 44]. The Table 1. below show the existing facial expression recognition systems. The Figure 1 Above show the general composition of the existing FER systems. It has been observed that the performance of automatic facial expression recognition is determined by feature selection, and appropriate movements associated with the facial feature in order to identify certain expressions [65; 66]. A research study that was conducted to focus on the selection and extraction of facial features for an automatic facial expressions recognition system; emphasized the facial geometric feature selection of eyebrows, eyes, etc., and used local binary patterns. They have modified hidden Markov model to mitigate the change in the local facial landmarks distance [67; 68; 69; 70; 71; 72; 73].