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Facial expression recognition based on improved depthwise separable convolutional network.

Hua Huo1, YaLi Yu1, ZhongHua Liu2

  • 1Engineering Technology Research Center of Big Data and Computational Intelligence, Henan University of Science and Technology, Kaiyuan Avenue, Luoyang, 471003 Henan China.

Multimedia Tools and Applications
|December 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved depthwise separable convolutional network for face expression recognition, combining Xception and MobileNetV2 models. The novel method enhances feature extraction and generalization, achieving high accuracy on benchmark datasets.

Keywords:
Canny edge detectionDepthwise separable convolutionExpression recognitionInverted residual module

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Single network models struggle with complex feature extraction and often have large parameter counts.
  • Combining multiple network models is a promising approach for extracting complementary features.
  • Existing methods face challenges in extracting high spatial depth features, managing redundant parameters, and ensuring generalization.

Purpose of the Study:

  • To develop an improved depthwise separable convolutional network for face expression recognition.
  • To address limitations of single network models in feature extraction and generalization.
  • To leverage complementary features from multiple network architectures.

Main Methods:

  • Utilized Xception module and inverted residual structure (MobileNetV2) to construct the neural network.
  • Applied Gaussian filtering via Canny operator for noise reduction, creating a three-channel input image.
  • Employed Softmax classifier for feature classification and ReLU6 as the nonlinear activation function.

Main Results:

  • Achieved a 70.76% recognition rate on the Fer2013 dataset.
  • Attained a 97.92% recognition rate on the CK+ dataset.
  • Demonstrated effective mining of deeper, abstract image features.

Conclusions:

  • The proposed method effectively mines deeper and more abstract image features for face expression recognition.
  • The approach prevents network over-fitting and enhances generalization ability.
  • The combination of Xception and MobileNetV2 structures offers a robust solution for facial expression analysis.