Facial expression recognition for emotion perception: A comprehensive science mapping

Abstract Facial expression recognition (FER) has emerged as a pivotal interdisciplinary research domain that bridges computer science, psychology, neuroscience, and medicine. By mapping the FER scientific knowledge graph, this study aimed to explore the technological evolution and forecast future trends in this field. The study collected and cleaned the research on emotion perception in the Web of Science (WoS) database, and utilized the software CiteSpace (version 6.4R1) and R (BiblioShiny packages) software to create a scientific knowledge map. K‐means was used for cluster analysis, and then the latent Dirichlet allocation (LDA) was employed to extract popular topics from the text of each cluster. Uniform manifold approximation and projection (UMAP) was utilized to reduce high‐dimensional embeddings to a two‐dimensional space. From a regional perspective, research is mainly distributed in countries or regions such as North America, Western Europe, East Asia, India, and Australia. Research on facial emotion recognition has focused primarily on neuroscience, psychiatry, and psychology. With the rapid development of computer technology, the interdisciplinary intersection is becoming increasingly important as FER has shown strong potential in identifying rare and neurological diseases. Furthermore, the evolution of artificial intelligence (AI) has transformed facial expression feature extraction from manual methodologies to machine learning‐based approaches. The rapid development of computer algorithms and AI has greatly improved the accuracy and speed of facial emotion recognition. As a technology capable of detecting instantaneous emotional changes, FER holds promising prospects in fields such as neuroscience, emotion analysis, and pain assessment.