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A Student Facial Expression Recognition Model Based on Multi-Scale and Deep Fine-Grained Feature Attention

Zhaoyu Shou^1,2, Yi Huang¹, Dongxu Li¹

¹School of Information and Communication, Guilin University of Electronic Science Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)

|October 26, 2024

Summary

This summary is machine-generated.

This study introduces a new student facial expression recognition model (SFER-MDFAE) for smart classrooms. The model enhances feature extraction for more accurate recognition of student emotions and learning states.

Keywords:

deep fine-grained features facial expression recognition key region-oriented attention mechanism multi-scale features smart classroom

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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

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Published on: August 26, 2016

Area of Science:

Artificial Intelligence
Computer Vision
Educational Technology

Background:

Accurate student facial expression recognition is vital for adaptive teaching in smart classrooms.
Existing methods struggle with precise facial feature extraction and robustness in classroom settings.

Purpose of the Study:

To develop an advanced student facial expression recognition model (SFER-MDFAE).
To improve the accuracy and robustness of facial expression analysis in smart classroom environments.

Main Methods:

Proposed a multi-scale dual-pooling feature aggregation module for comprehensive facial information capture.
Designed a key region-oriented attention mechanism to enhance fine-grained facial expression features.
Fused multi-scale and attention-enhanced features for improved facial key information representation.

Main Results:

Achieved high accuracy on benchmark datasets: 76.18% (FER2013), 92.75% (FERPlus), 92.93% (RAF-DB), 67.86% (AffectNet).
Demonstrated superior performance on a real smart classroom dataset (SCFED) with 93.74% accuracy.
Outperformed existing state-of-the-art methods in facial expression recognition tasks.

Conclusions:

The SFER-MDFAE model effectively addresses limitations in facial feature extraction and recognition robustness.
The proposed method significantly enhances the accuracy of student facial expression recognition in smart classrooms.
Validated effectiveness through comprehensive experiments on diverse datasets.