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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Optimal Compact Network for Micro-Expression Analysis System.

Koo Sie-Min¹, Mohd Asyraf Zulkifley¹, Nor Azwan Mohamed Kamari¹

¹Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This summary is machine-generated.

This study introduces an optimized deep learning system for micro-expression analysis, enhancing emotion recognition accuracy. Synthetic data augmentation improved performance, making the system suitable for mobile applications.

Keywords:

compact network convolutional neural network emotion classification micro-expression analysis

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Area of Science:

Computer Science
Artificial Intelligence
Psychology

Background:

Micro-expression analysis studies subtle facial movements to reveal genuine emotions.
Manual analysis is time-consuming; deep learning offers efficiency but faces data limitations and overfitting risks.
Existing automated systems require optimization for spotting and recognition.

Purpose of the Study:

To develop a complete deep learning-based system for micro-expression analysis, including spotting, recognition, and data augmentation.
To address the challenge of insufficient micro-expression data and network overfitting.
To create an efficient and accurate system for detecting genuine human emotions.

Main Methods:

An optimized continuous labeling scheme for spotting apex frames in micro-expression videos.
Generative adversarial networks (GANs) for synthetic data augmentation of apex frames.
A novel convolutional neural network, Optimal Compact Network (OC-Net), for emotion recognition.

Main Results:

The system achieved an F1-score of 0.69 and an accuracy of 79.14% in emotion categorization.
Synthetic data augmentation improved the performance of tested networks by at least 0.61%.
The proposed system demonstrates suitability for mobile-based micro-expression analysis.

Conclusions:

The developed deep learning system effectively analyzes micro-expressions for emotion recognition.
Synthetic data augmentation is a viable strategy to overcome data scarcity in micro-expression analysis.
The optimized and compact OC-Net is well-suited for real-time, mobile applications detecting genuine human emotions.