Emotion Recognition Using Various Measures and Computational Methods: Review

Abstract

 Emotion recognition has garnered significant attention as a burgeoning research domain, owing to
its potential applications across diverse fields such as human-computer interaction, affective gaming, marketing,
and human-robot interaction. Accurately interpreting and appropriately responding to human emotions remains a
critical challenge in the development of systems. This obstacle necessitates a thorough understanding of emotions
to enhance user experiences within such systems. This paper conducts a comprehensive review focusing on
advancements in emotion recognition techniques, with an emphasis on leveraging a variety of sensors and
computational methods. Our study findings highlight the significant improvement to emotion recognition accuracy
when multiple measures and computational methods, rather than a single modality, is used. This article contributes
to the field by thoroughly reviewing and comparing diverse measures and computational methods for emotion
recognition. The study highlights the pivotal role of employing multiple modalities and advanced machine learning
algorithms to achieve superior accuracy and reliability in emotion recognition. Furthermore, this research identifies
potential avenues for further investigation and development, such as integrating multimodal data and exploring
novel features and fusion techniques. The insights offered in this study provide valuable guidance for researchers
and practitioners in the field, facilitating the advancement of technologies that adeptly understand and respond to
human emotions.