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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...

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Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions.

Cheng Qian¹, João Alexandre Lobo Marques², Auzuir Ripardo de Alexandria³

¹Institute of Data Engineering and Science, University of Saint Joseph, Macau SAR, China.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This summary is machine-generated.

This study evaluates ten deep learning models for facial expression recognition (FER). EfficientNet V2 and ResNet50 achieved top accuracy, balancing performance and efficiency for emotion detection.

Keywords:

FER2013 dataset artificial intelligence deep learning facial expression recognition model performance evaluation

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Facial expression recognition (FER) is crucial for understanding human emotions.
Applications span big data analytics, healthcare, security, and user experience.
Deep learning models offer advanced capabilities for FER tasks.

Purpose of the Study:

To comprehensively evaluate ten state-of-the-art deep learning models for FER.
To analyze model performance based on accuracy, training time, and file size.
To identify optimal architectures for specific FER application requirements.

Main Methods:

Utilized the FER2013 dataset for facial expression recognition.
Evaluated ten deep learning models: VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG.
Assessed key performance metrics including test accuracy, training time, and weight file size.

Main Results:

EfficientNet V2 and ResNet50 demonstrated superior performance with high accuracy and stable convergence.
DenseNet, GoogLeNet V1, and RepVGG showed strong results but with slower initial convergence.
Lightweight models (MobileNet V1, ShuffleNet V2) offered computational efficiency but lower accuracy on challenging emotions.

Conclusions:

A critical trade-off exists between computational efficiency and predictive accuracy in FER models.
Model selection for FER should align with specific application needs and constraints.
This research advances deep learning for FER by detailing model performance and trade-offs.