Systematic Literature Review: The Influence and Effectiveness of Deep Learning in Image Processing for Emotion Recognition

Abstract

Abstract In the current digital era, image processing and Emotion Recognition are important topics in the field of artificial intelligence. Deep learning, as one of the most widely used AI techniques in pattern recognition, has shown great potential in addressing these challenges. This research employs a Systematic Literature Review method to collect and analyze previous studies related to deep learning algorithms, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), allowing the researchers to conclude efficient deep learning methods for emotion recognition through image processing. This paper has the result that most studies used CNN to identify emotion from facial expressions, while some studies used RNN. Furthermore, some researchers used combined CNN and RNN to identify emotion from images. Based on the analysis of this research, it is recommended that further studies to take a more holistic approach by considering a wider range of indicators that can be used as signs or signals to analyze a person's emotions. This approach allows for a comprehensive understanding of emotions from multiple perspectives.