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A Lightweight Dual-Stream Network with an Adaptive Strategy for Efficient Micro-Expression Recognition.

Xinyu Liu^1,2,3, Ju Zhou^1,2,3,4, Feng Chen^1,2,3

¹College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

This study introduces a lightweight dual-stream network for recognizing micro-expressions (MEs). The novel adaptive method enhances accuracy and robustness, showing strong potential for edge sensor applications.

Keywords:

adaptive strategy deep learning lightweight model micro-expression recognition motion magnification optical flow

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Area of Science:

Computer Vision
Artificial Intelligence
Biometrics

Background:

Micro-expressions (MEs) are challenging to recognize due to their brief duration and subtle facial movements.
Accurate ME recognition requires specialized computational methods for spatio-temporal feature extraction.

Purpose of the Study:

To propose a lightweight dual-stream network with an adaptive strategy for efficient micro-expression recognition.
To enhance the robustness and accuracy of ME recognition systems.

Main Methods:

Employed a motion magnification network using transfer learning to amplify facial muscle movements in MEs.
Extracted magnified dense optical flow (MDOF) from onset and magnified apex frames.
Designed a dual-stream spatio-temporal network (DSTNet) utilizing magnified frames and MDOF.
Introduced an adaptive strategy to dynamically adjust the magnification factor based on confidence scores.

Main Results:

The proposed method achieved superior F1-scores on multiple datasets (SMIC, CASME II, SAMM) and in cross-dataset tasks.
Adaptive DSTNet demonstrated significant improvements in handling imbalanced sample data.
The system exhibited robustness and a lightweight design suitable for edge deployment.

Conclusions:

The lightweight dual-stream network with an adaptive strategy offers an efficient and robust solution for micro-expression recognition.
The method shows significant potential for real-world applications, particularly on edge sensors.
The approach effectively addresses challenges in ME recognition, including sample imbalance and subtle feature extraction.