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A study on expression recognition based on improved mobilenetV2 network.

Qiming Zhu¹, Hongwei Zhuang², Mi Zhao³

¹College of Equipment Support and Management, Engineering University of PAP, Xi'an, 710086, China.

Scientific Reports

|April 6, 2024

Summary

This summary is machine-generated.

This study introduces an improved MobileNetV2 (I-MobileNetV2) for facial emotion recognition, significantly reducing parameters while enhancing accuracy. The enhanced model addresses feature loss and improves real-time performance for better emotion detection.

Keywords:

Attention mechanism Expression recognition MobileNetV2 Reverse fusion SELU

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep convolutional neural networks often have large parameter quantities.
Lightweight neural networks like MobileNetV2 can suffer from feature information loss, poor real-time performance, and low accuracy in facial emotion recognition.

Purpose of the Study:

To propose an improved MobileNetV2 (I-MobileNetV2) strategy for facial emotion recognition.
To address the limitations of existing models, including large parameter counts and feature information loss.

Main Methods:

Inherited depthwise separable convolution from MobileNetV2 for reduced computational load.
Incorporated a reverse fusion mechanism to retain negative features and minimize information loss.
Replaced the RELU6 activation function with SELU to prevent gradient vanishing.
Integrated the Squeeze-and-Excitation Networks (SE-Net) channel attention mechanism to boost feature recognition.

Main Results:

The I-MobileNetV2 model achieved accuracies of 68.62% on FER2013 and 95.96% on CK+ datasets.
Accuracy improved by 0.72% (FER2013) and 6.14% (CK+) compared to the original MobileNetV2.
The parameter count was reduced by 83.8%, indicating a more lightweight model.

Conclusions:

The proposed I-MobileNetV2 model demonstrates significant improvements in facial emotion recognition accuracy and efficiency.
The integration of reverse fusion, SELU activation, and SE-Net effectively enhances feature recognition and reduces model complexity.