Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abstract

Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

Design of University Financial Information System Based on Data Mining Technology and IoT

“…Also in this layer is the information storage database, which is used to store the configuration information of the system and the information about the deployment of applications. At the same time, this information storage database is also responsible for the collection and storage of statistical information [20]. It resides in the CORBA object of the network and is responsible for collecting the data required by the system.…”

A Classroom Emotion Recognition Model Based on a Convolutional Neural Network Speech Emotion Algorithm

“…By collecting the address and text data of teachers and students in the secondary school classroom, we use deep learning technology to explore the emotion recognition method based on the two modalities of classroom speech and classroom text and, finally, achieve the purpose of automatically recognizing the emotion of classroom speakers and judging the whole classroom emotional atmosphere. For students, it can reflect the learning situation through students' emotions and assist teachers in implementing timely teaching interventions, that is, to transform students' emotional states into teachers' decision-making suggestions and eventually help teachers to carry out accurate teaching; for teachers, automatic recognition of teachers' emotions will allow teachers to reflect on their teaching behaviors after class and improve their teaching ability, which can also be used as a basis for evaluating teachers' teaching level [ 8 ]. Realizing the overall emotional portrait of the classroom will facilitate macroscopic decision-making, help teachers grasp the overall teaching atmosphere of the school from the students and their levels, effectively promote objective evaluation of the school, and ultimately make suggestions for improvement from the teachers and students' levels to enhance the overall teaching effectiveness of the school.…”

Sentiment Analysis from Face Expressions Based on Image Processing Using Deep Learning Methods

“…This situation contributed significantly to the solution due to the pose and illumination, which does not change due to the nature of the data. Similar developments are also observed in facial expression recognition (Göngör & Tutsoy, 2018;Tutsoy et al, 2017;Zhao et al, 2015;Kuo et al, 2018;Jung et al, 2015;Abdullah, et al, 2021). Göngör & Tutsoy proposed a facial emotion recognition system to use in humanoid robot.…”