ArticleScientific reports2024
Improved facial emotion recognition model based on a novel deep convolutional structure.
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 9 papers.
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9 citing papers in PubMed.
- Multimodal emotion recognition using hybrid deep feature fusion under speaker-independent evaluation.Scientific reports · 2026Article
- An algorithm for optimizing psychological regulation strategies for college students based on image recognition and reinforcement learning.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
- Distinct neural temporal architectures encode rapid social expressions and sustained internal mood states.bioRxiv : the preprint server for biology · 2025Article
- Fusion of EEG feature extraction and CNN-MSTA transformer emotion recognition classification model.Scientific reports · 2025Article
- Lightweight Convolutional Neural Network with Efficient Channel Attention Mechanism for Real-Time Facial Emotion Recognition in Embedded Systems.Sensors (Basel, Switzerland) · 2025Article
- Feasibility of an AI-Enabled Smart Mirror Integrating MA-rPPG, Facial Affect, and Conversational Guidance in Realtime.Sensors (Basel, Switzerland) · 2025Article
- Multiscale wavelet attention convolutional network for facial expression recognition.Scientific reports · 2025Article
- CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM.PloS one · 2025Article
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4 authors.
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Abstract
Facial Emotion Recognition (FER) is a very challenging task due to the varying nature of facial expressions, occlusions, illumination, pose variations, cultural and gender differences, and many other aspects that cause a drastic degradation in quality of facial images. In this paper, an anti-aliased deep convolution network (AA-DCN) model has been developed and proposed to explore how anti-aliasing can increase and improve recognition fidelity of facial emotions. The AA-DCN model detects eight distinct emotions from image data. Furthermore, their features have been extracted using the proposed model and numerous classical deep learning algorithms. The proposed AA-DCN model has been applied to three different datasets to evaluate its performance: The Cohn-Kanade Extending (CK+) database has been utilized, achieving an ultimate accuracy of 99.26% in (5 min, 25 s), the Japanese female facial expressions (JAFFE) obtained 98% accuracy in (8 min, 13 s), and on one of the most challenging FER datasets; the Real-world Affective Face (RAF) dataset; reached 82%, in low training time (12 min, 2s). The experimental results demonstrate that the anti-aliased DCN model is significantly increasing emotion recognition while improving the aliasing artifacts caused by the down-sampling layers.
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