A Review on Facial Emotion Recognition Using Machine and Deep Learning Algorithm

Abstract

Facial emotions are the changes in facial expressions about a person’s inner excited tempers, objectives, or social exchanges which are scrutinized with the aid of computer structures that attempt to subsequently inspect and identify the facial feature and movement variations from visual data. Facial emotion recognition (FER) is a noteworthy area in the arena of computer vision and artificial intelligence due to its significant commercial and academic potential. FER has become a widespread concept of deep learning and offers more fields for application in our day-to-day life. Facial expression recognition (FER) has gathered widespread consideration recently as facial expressions are thought of as the fastest medium for communicating any of any sort of information. Recognizing facial expressions provides an improved understanding of a person’s thoughts or views. With the latest improvement in computer vision and machine learning, it is plausible to identify emotions from images. Analyzing them with the presently emerging deep learning methods enhance the accuracy rate tremendously as compared to the traditional contemporary systems. This paper emphases the review of a few of the machine learning, deep learning, and transfer learning techniques used by several researchers that flagged the means to advance the classification accurateness of the FEM.