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Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion
arXiv – CS AI|Mingjie Zhang, Bo Li, Wanting Liu, Hongyan Cui, Yue Li, Qingwen Li, Hong Li, Ge Gao||1 views
🤖AI Summary
Researchers developed a dual-branch neural network for micro-expression recognition that combines residual and Inception networks with parallel attention mechanisms. The method achieved 74.67% accuracy on the CASME II dataset, significantly outperforming existing approaches like LBP-TOP by over 11%.
Key Takeaways
- →New dual-branch network architecture combines residual and Inception networks for better micro-expression detection.
- →Adaptive feature fusion module integrates features from both network branches to improve recognition accuracy.
- →Method achieved 74.67% accuracy on CASME II dataset, beating LBP-TOP by 11.26% and MSMMT by 3.36%.
- →Residual network component addresses gradient vanishing problems in deep learning architectures.
- →Research focuses on detecting subtle, transient facial expressions that are difficult for existing optical flow methods.
#micro-expression#computer-vision#neural-networks#facial-recognition#deep-learning#inception#residual-networks#feature-fusion#attention-mechanisms
Read Original →via arXiv – CS AI
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