ArticleSensors (Basel, Switzerland)2021
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets.F1000Research · 2025Pooled it
- New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Facial emotion-based movie recommendation system using optimized compound scaling neural network with polynomial and RBF kernels.Scientific reports · 2026Article
- Self-Learning Multimodal Emotion Recognition Based on Multi-Scale Dilated Attention.Brain sciences · 2026Article
- ArecaNet: Robust Facial Emotion Recognition via Assembled Residual Enhanced Cross-Attention Networks for Emotion-Aware Human-Computer Interaction.Sensors (Basel, Switzerland) · 2025Article
- Empowering emotional intelligence through deep learning techniques.Scientific reports · 2025Article
- An Explainable Framework for Mental Health Monitoring Using Lightweight and Privacy-Preserving Federated Facial Emotion Recognition.Sensors (Basel, Switzerland) · 2025Article
- Lightweight Convolutional Neural Network with Efficient Channel Attention Mechanism for Real-Time Facial Emotion Recognition in Embedded Systems.Sensors (Basel, Switzerland) · 2025Article
- GBV-Net: Hierarchical Fusion of Facial Expressions and Physiological Signals for Multimodal Emotion Recognition.Sensors (Basel, Switzerland) · 2025Article
- Feature Analysis of Facial Color Information During Emotional Arousal in Japanese Older Adults Playing eSports.Sensors (Basel, Switzerland) · 2025Article
- CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM.PloS one · 2025Article
- Emotion estimation from video footage with LSTM.Frontiers in neurorobotics · 2025Article
- Exploiting facial emotion recognition system for ambient assisted living technologies triggered by interpreting the user's emotional state.Frontiers in neuroscience · 2025Article
- A Student Facial Expression Recognition Model Based on Multi-Scale and Deep Fine-Grained Feature Attention Enhancement.Sensors (Basel, Switzerland) · 2024Article
- Establishing the Foundations of Emotional Intelligence in Care Companion Robots to Mitigate Agitation Among High-Risk Patients With Dementia: Protocol for an Empathetic Patient-Robot Interaction Study.JMIR research protocols · 2024Article
- Memristive patch attention neural network for facial expression recognition and edge computing.Cognitive neurodynamics · 2024Article
- Machine to brain: facial expression recognition using brain machine generative adversarial networks.Cognitive neurodynamics · 2024Article
- Facial Expression Recognition for Measuring Jurors' Attention in Acoustic Jury Tests.Sensors (Basel, Switzerland) · 2024Article
- FERDCNN: an efficient method for facial expression recognition through deep convolutional neural networks.PeerJ. Computer science · 2024Article
- Facial expression recognition on partially occluded faces using component based ensemble stacked CNN.Cognitive neurodynamics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier's output. Through experimental results, we show that different emotions are sensitive to different parts of the face.
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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.