โก Quick Summary
This study provides a comprehensive mapping of facial expression recognition (FER) research, highlighting its interdisciplinary nature and technological advancements. The findings indicate that AI-driven methodologies have significantly enhanced the accuracy and speed of emotion detection, with promising applications in various fields.
๐ Key Details
- ๐ Dataset: Research collected from the Web of Science (WoS) database
- ๐งฉ Technologies used: CiteSpace, R (BiblioShiny), K-means, LDA, UMAP
- ๐ Research regions: North America, Western Europe, East Asia, India, Australia
- ๐ฌ Focus areas: Neuroscience, psychiatry, psychology
๐ Key Takeaways
- ๐ค AI evolution has transformed FER from manual to machine learning-based approaches.
- ๐ Improved accuracy and speed of emotion detection through advanced algorithms.
- ๐ง Interdisciplinary research is crucial for advancements in FER technology.
- ๐ Potential applications in identifying rare and neurological diseases.
- ๐ Future trends forecasted through scientific knowledge mapping.
- ๐ Cluster analysis revealed popular topics in emotion perception research.
- ๐ Global collaboration is essential for further advancements in this field.
๐ Background
Facial expression recognition (FER) is an emerging field that integrates insights from computer science, psychology, neuroscience, and medicine. As technology evolves, understanding human emotions through facial expressions has become increasingly relevant, particularly in contexts such as mental health assessment and human-computer interaction.
๐๏ธ Study
The study aimed to create a scientific knowledge map of FER research by analyzing data from the Web of Science. Utilizing advanced software tools like CiteSpace and R, the researchers conducted a thorough analysis of the literature, employing techniques such as K-means clustering and latent Dirichlet allocation (LDA) to identify key themes and trends in the field.
๐ Results
The findings revealed a significant shift towards AI-driven methodologies in FER, with machine learning techniques outperforming traditional methods in terms of accuracy and efficiency. The study also highlighted the geographical distribution of research, with notable contributions from regions such as North America and Western Europe.
๐ Impact and Implications
The implications of this research are profound, as FER technology holds the potential to revolutionize fields such as neuroscience, emotion analysis, and pain assessment. By accurately detecting emotional changes, FER can enhance our understanding of human behavior and improve diagnostic processes in clinical settings.
๐ฎ Conclusion
This comprehensive mapping of facial expression recognition research underscores the transformative potential of AI in understanding human emotions. As the field continues to evolve, ongoing interdisciplinary collaboration will be essential for unlocking new applications and improving existing technologies. The future of FER looks promising, and further research is encouraged to explore its full capabilities.
๐ฌ Your comments
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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.
Author: [‘Kan HM’, ‘Chen LP’, ‘Zhang Y’, ‘Hong HY’, ‘Qin YY’, ‘Cui YG’, ‘Mao YB’, ‘Cheng YZ’, ‘Lu Z’, ‘Ni HY’, ‘Ding XT’]
Journal: Ibrain
Citation: Kan HM, et al. Facial expression recognition for emotion perception: A comprehensive science mapping. Facial expression recognition for emotion perception: A comprehensive science mapping. 2026; 12:38-51. doi: 10.1002/ibra.70010