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Region Dual Attention-Based Video Emotion Recognition
Xiaodong Liu1, Huating Xu1, Miao Wang1
1School of Computing, Henan University of Engineering, Zhengzhou, China.
Computational Intelligence and Neuroscience
|June 27, 2022
Summary
A new region dual attention method (RDAM) improves video emotion recognition by analyzing regional differences and relationships within frames. This approach enhances emotion feature extraction for more accurate video analysis.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Video emotion recognition is challenging due to spatial-temporal complexities.
- Existing methods often fail to fully capture inter-regional emotional dynamics within video frames.
Purpose of the Study:
- To propose a novel region dual attention-based video emotion recognition method (RDAM).
- To enhance video emotion recognition by leveraging both regional differences and inter-regional relationships.
Main Methods:
- RDAM utilizes two parallel attention modules: regional location attention and regional relationship attention.
- Regional location attention identifies important feature regions.
- Regional relationship attention models inter-regional dependencies within frames.
Main Results:
- The proposed RDAM method effectively extracts emotion features by considering regional importance and relationships.
- Experimental results on video emotion recognition datasets demonstrate superior performance compared to existing methods.
Conclusions:
- RDAM offers a significant advancement in video emotion recognition.
- The dual attention mechanism effectively captures nuanced emotional information from video frames.
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