ArticleScientific reports2024
Image-based facial emotion recognition using convolutional neural network on emognition dataset.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- MGCP-FER: Meta Control Genetic Programming (MCGP-FER) based feature extraction and fusion for facial expression recognition.Scientific reports · 2026Article
- Emotion recognition in dance therapy driven by DanceEmoNet: a deep learning model based on facial expression and pose estimation.Frontiers in psychology · 2026Article
- An improved facial emotion recognition system using convolutional neural network for the optimization of human robot interaction.Scientific reports · 2025Article
- An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance.Bioengineering (Basel, Switzerland) · 2025Article
- CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM.PloS one · 2025Article
- Article
- Facial expression recognition reveals students' engagement in online class: Correlations with six engagement measurements.PloS one · 2025Article
- Article
- Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.Biomimetics (Basel, Switzerland) · 2024Article
- Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
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3 authors.
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Abstract
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data.
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