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Related Experiment Video

Updated: Aug 24, 2025

Profiling Maternal Behavior Responses During Whole-Brain Imaging

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Published on: January 24, 2025

Micro-expression recognition model based on TV-L1 optical flow method and improved ShuffleNet.

Yanju Liu¹, Yange Li², Xinhan Yi²

¹School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing, 210038, China.

Scientific Reports

|October 20, 2022

Summary

This summary is machine-generated.

This study introduces an improved ShuffleNet model with a self-attentive module for accurate micro-expression recognition. The novel approach enhances global feature extraction, achieving competitive results on benchmark datasets.

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Area of Science:

Computer Vision
Machine Learning
Affective Computing

Background:

Micro-expression recognition is crucial for objective emotion detection, with convolutional neural networks (CNNs) being a primary method.
CNNs excel in efficiency but struggle with localized feature extraction, necessitating improvements in global feature learning.
Self-attentive modules are increasingly integrated into CNNs to enhance the capture of global sample features.

Purpose of the Study:

To propose a novel micro-expression recognition model by integrating ShuffleNet with a miniature self-attentive module.
To improve the global feature extraction capabilities of CNNs for more accurate micro-expression recognition.
To develop a computationally efficient model with a low parameter count (1.53 million).

Main Methods:

Extracted TV-L1 optical flow features from the start and vertex frames of micro-expression samples.
Pre-trained the ShuffleNet model with the miniature self-attentive module using the extracted optical flow features.
Utilized pre-trained weights to initialize the model for training on complete micro-expression samples and subsequent classification using an SVM classifier.

Main Results:

The proposed ShuffleNet model combined with a miniature self-attentive module demonstrated competitive performance.
The model achieved effective micro-expression recognition when trained and tested on a composite dataset (CASMEII, SMIC, SAMM).
Leave-one-out subject cross-validation confirmed the model's robustness and effectiveness compared to state-of-the-art methods.

Conclusions:

The integration of a miniature self-attentive module significantly enhances the performance of ShuffleNet for micro-expression recognition.
The proposed method offers an efficient and effective approach to micro-expression analysis, improving upon traditional CNN limitations.
This research contributes a valuable tool for objective emotion detection through advanced computer vision techniques.