Applications and Reliability of Automatic Facial Emotion Recognition Software: A Five-Year Review (2020-2024)

Emotions play a crucial role in shaping human behavior and interactions in various contexts. While emotions can enhance an individual's effectiveness, they can also hinder performance. However, our understanding and prediction of human behavior are often limited to individuals' own perceptions of their thoughts and emotions. The development of technology, specifically automatic facial recognition of emotions (FER), has the potential to advance our ability to predict behavior. This technology allows researchers to objectively measure an individual's internal emotional state. To ensure its effectiveness, standardization in protocols across scientific fields is necessary. This involves creating and analyzing diverse databases to train machine learning models more effectively and improving the algorithms used. The objective of this literature review is to explore the applications of FER technology and evaluate the effectiveness of its results in recent research. Although the results have been satisfactory, researchers have used various data collection methods, which may complicate replication in future studies. To ensure reproducibility, it is important to develop an experimental protocol that clearly outlines the data collection process. Additionally, employing the self-report method can strengthen the findings obtained from the software. Considering the unique characteristics of individuals, it is advisable to establish predetermined values before analyzing and generalizing the results obtained from the software.