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Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks
Xiangwei Mou1,2, Yongfu Song1, Xiuping Xie1
1College of Electronic and Information Engineering/Integrated Circuits, Guangxi Normal University, Guilin 541004, China.
Sensors (Basel, Switzerland)
|June 27, 2025
Summary
This study introduces a novel dual neural network model for dynamic facial expression recognition from image sequences. The method significantly improves accuracy and efficiency, paving the way for advanced intelligent sensing applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Facial expression recognition from static images lacks temporal dynamics, limiting real-world applications.
- Integrating dynamic facial expression recognition into intelligent sensing is challenging due to performance degradation.
Purpose of the Study:
- To develop an effective facial expression recognition method for image sequences.
- To address the limitations of static image-based recognition by incorporating temporal information.
Main Methods:
- Proposed a novel dual neural network model fusing ResNet and residual bidirectional GRU (Res-RBG).
- Evaluated the model on benchmark datasets (CK+ and Oulu-CASIA) for image sequence-based facial expression recognition.
Main Results:
- Achieved high recognition accuracies: 98.10% on CK+ and 88.64% on Oulu-CASIA.
- The model boasts a compact parameter size of 64.20 M.
- Demonstrated superior performance compared to existing image sequence-based methods.
Conclusions:
- The proposed Res-RBG model effectively captures temporal dynamics for accurate facial expression recognition.
- The model's efficiency and performance show strong potential for edge sensor deployment in intelligent sensing applications.
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