DCE: Dynamic channel and spatial redundancy elimination for Micro-Expression recognition

Key Points

• Micro-expression recognition accuracy improved significantly using dynamic channel redundancy elimination, and spatial redundancy methods.
• The analysis showed a notable increase in recognition performance, with an accuracy rate surpassing 90% in tested models.
• Utilizing deep learning algorithms and novel feature extraction techniques, significant advancements were achieved in recognizing subtle emotional cues.
• The findings suggest that enhancing micro-expression recognition may enable better emotional understanding in various fields like psychology and security.