A Comprehensive Study on State-Of-Art Learning Algorithms in Emotion Recognition

Abstract

The potential uses of emotion recognition in domains like human-robot interaction, marketing, emotional gaming, and human-computer interface have made it a prominent research subject. Better user experiences can result from the development of technologies that can accurately interpret and respond to human emotions thanks to a better understanding of emotions. The use of several sensors and computational algorithms is the main emphasis of this paper's thorough analysis of the developments in emotion recognition techniques. Our results show that using more than one modality improves the performance of emotion recognition when a variety of metrics and computational techniques are used. This paper adds to the body of knowledge by thoroughly examining and contrasting several state-of-art computational techniques and measurements for emotion recognition. The study emphasizes how crucial it is to use a variety of modalities along with cutting-edge machine learning algorithms in order to attain more precise and trustworthy emotion assessment. Additionally, we pinpoint prospective avenues for additional investigation and advancement, including the incorporation of multimodal data and the investigation of innovative features and fusion methodologies. This study contributes to the development of technology that can better comprehend and react to human emotions by offering practitioners and academics in the field of emotion recognition insightful advice.