A Descriptive Survey on Face Emotion Recognition Techniques

Abstract

Recognition of natural emotion from human faces has applications in Human–Computer Interaction, image and video retrieval, automated tutoring systems, smart environment as well as driver warning systems. It is also a significant indication of nonverbal communication among the individuals. The assignment of Face Emotion Recognition (FER) is predominantly complex for two reasons. The first reason is the nonexistence of a large database of training images, and the second one is about classifying the emotions, which can be complex based on the static input image. In addition, robust unbiased FER in real time remains the foremost challenge for various supervised learning-based techniques. This survey analyzes diverse techniques regarding the FER systems. It reviews a bunch of research papers and performs a significant analysis. Initially, the analysis depicts various techniques that are contributed in different research papers. In addition, this paper offers a comprehensive study regarding the chronological review and performance achievements in each contribution. The analytical review is also concerned about the measures for which the maximum performance was achieved in several contributions. Finally, the survey is extended with various research issues and gaps that can be useful for the researchers to promote improved future works on the FER models.