ReviewIbrain2026
Facial expression recognition for emotion perception: A comprehensive science mapping.
Review in Ibrain, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.