ArticleSensors (Basel, Switzerland)2018
A Brief Review of Facial Emotion Recognition Based on Visual Information.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
79 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms.Sensors (Basel, Switzerland) · 2024Pooled it
- Systematic Review of Emotion Detection with Computer Vision and Deep Learning.Sensors (Basel, Switzerland) · 2024Pooled it
- Accuracy of Mobile Device-Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2020Pooled it
- Machine and deep learning in facial expression recognition: a survey based on facial action units.Scientific reports · 2026Review
- Article
- Article
- Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.Frontiers in psychology · 2026Review
- Article
- A standardized lexicon of body odor words crafted from 17 countries.Scientific data · 2025Article
- Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification.Sensors (Basel, Switzerland) · 2025Article
- Recording dynamic facial micro-expressions with a multi-focus camera array.Biomedical optics express · 2025Article
- Exploiting facial emotion recognition system for ambient assisted living technologies triggered by interpreting the user's emotional state.Frontiers in neuroscience · 2025Article
- CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM.PloS one · 2025Article
- A modular machine learning tool for holistic and fine-grained behavioral analysis.Behavior research methods · 2024Article
- Smartphone-generated 3D facial images: reliable for routine assessment of the oronasal region of patients with cleft or mere convenience? A validation study.BMC oral health · 2024Article
- Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study.JMIR mental health · 2024Article
- Image-based facial emotion recognition using convolutional neural network on emognition dataset.Scientific reports · 2024Article
- Leveraging the Sensitivity of Plants with Deep Learning to Recognize Human Emotions.Sensors (Basel, Switzerland) · 2024Article
- An exploratory study of the effect of age and gender on face scanning during affect recognition in immersive virtual reality.Scientific reports · 2024Article
- Facial expression recognition (FER) survey: a vision, architectural elements, and future directions.PeerJ. Computer science · 2024Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. Deep-learning-based FER approaches using deep networks enabling "end-to-end" learning are then presented. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (CNN) for the spatial features of an individual frame and long short-term memory (LSTM) for temporal features of consecutive frames. In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described. This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work.
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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.