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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Micro Expression Recognition via Dual-Stream Spatiotemporal Attention Network.

Yan Wang¹, Yikun Huang², Can Liu³

¹College of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China.

Journal of Healthcare Engineering

|August 30, 2021

Summary

This summary is machine-generated.

This study introduces a Dual-Stream Spatiotemporal Attention Network (DSTAN) to improve microexpression recognition, especially with limited data. The DSTAN effectively captures subtle facial movements for better mood and depression analysis.

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Area of Science:

Computer Science
Artificial Intelligence
Biomedical Engineering

Background:

Microexpressions reveal true human emotions, crucial for clinical diagnosis and depression analysis.
Small datasets and subtle microexpression movements pose challenges for feature extraction.

Purpose of the Study:

To develop a novel network for accurate microexpression recognition with limited data.
To capture both deformation and spatiotemporal features of microexpressions.

Main Methods:

Proposed a Dual-Stream Spatiotemporal Attention Network (DSTAN) integrating dual-stream networks and attention mechanisms.
Utilized a Spatiotemporal Appearance Network (STAN) for appearance features and a Spatiotemporal Motion Network (STMN) for motion features.
Incorporated multiscale spatial attention and global dual-pool channel attention, along with temporal attention for dynamic feature refinement.

Main Results:

The DSTAN effectively extracts discriminative spatiotemporal and deformation features from microexpression sequences.
Attention mechanisms in DSTAN focus on salient motion areas and frame importance.
Experiments on SMIC, CASME, and CASME II datasets validated DSTAN's effectiveness.

Conclusions:

The DSTAN significantly enhances microexpression recognition accuracy, particularly in small-sample scenarios.
The proposed method offers a robust solution for analyzing subtle human emotional cues.
DSTAN demonstrates potential for applications in clinical diagnosis and mental health analysis.