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Micro-Expression Recognition Based on Optical Flow and PCANet.

Shiqi Wang¹, Suen Guan¹, Hui Lin¹

¹School of Informatics, Xiamen University, Xiamen 361005, China.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This summary is machine-generated.

This study introduces a new method for recognizing micro-expressions, which are fast, subtle facial movements. The proposed OF-PCANet+ model effectively learns spatiotemporal features, outperforming existing techniques on benchmark datasets.

Keywords:

PCANet+deep learning micro-expression recognition optical flow

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Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Micro-expressions are rapid, subtle facial movements, difficult to detect and recognize.
Limited micro-expression datasets pose challenges for deep learning models, leading to overfitting.
Micro-expression recognition has significant applications across various domains.

Purpose of the Study:

To propose an effective and lightweight model for micro-expression recognition.
To address the overfitting issue in deep learning models due to small datasets.
To develop a spatiotemporal feature learning strategy for enhanced recognition accuracy.

Main Methods:

Developed the OF-PCANet+ method integrating optical flow sequence stacking with PCANet+.
Designed a spatiotemporal feature learning strategy utilizing a shallow PCANet+ model.
Conducted experiments on publicly available SMIC and CASME2 datasets.

Main Results:

The OF-PCANet+ model achieved superior performance compared to popular hand-crafted methods.
The proposed lightweight model demonstrated comparable performance to advanced deep learning methods like 3D-FCNN and ELRCN.
Effective learning of discriminative spatiotemporal features was demonstrated.

Conclusions:

The OF-PCANet+ method offers an efficient solution for micro-expression recognition.
The approach successfully mitigates overfitting issues in small-dataset scenarios.
This work contributes a valuable tool for advancing micro-expression analysis in computer vision.