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eXnet: An Efficient Approach for EmotionRecognition in the Wild.

Muhammad Naveed Riaz1, Yao Shen1, Muhammad Sohail2

  • 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Sensors (Basel, Switzerland)
|February 22, 2020
PubMed
Summary

We developed eXnet, a lightweight Convolutional Neural Network (CNN) for facial expression recognition. This efficient model achieves high accuracy on embedded systems, overcoming computational constraints of current deep learning methods.

Keywords:
CK+CNNFERRAF-DBdeep learningembedded devicesemotion classification

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Facial expression recognition is crucial for human-computer interaction and social sciences.
  • Deep learning has advanced facial expression recognition, often surpassing human accuracy.
  • Existing deep learning models require high computational power and memory, limiting their use on hardware-constrained devices.

Purpose of the Study:

  • To propose a novel Convolutional Neural Network (CNN) architecture, eXnet (Expression Net), designed for efficient and accurate facial expression recognition.
  • To address the limitations of high computational power and memory requirements in current deep learning models for facial expression recognition.
  • To create a lightweight and efficient model suitable for real-time systems and embedded devices.

Main Methods:

  • Developed eXnet, a CNN architecture utilizing parallel feature extraction.
  • Applied modern data augmentation techniques to enhance eXnet's generalization and prevent overfitting.
  • Evaluated eXnet on benchmark datasets: FER-2013, CK+, and RAF-DB.
  • Performed ablation studies to validate the contribution of individual architectural components.
  • Deployed eXnet on a Raspberry Pi 4B to assess its performance on embedded systems.

Main Results:

  • eXnet demonstrates superior accuracy compared to existing methods on multiple benchmark datasets.
  • eXnet possesses significantly fewer parameters (4.57 million) than VGG19 (14.72 million), indicating a more lightweight design.
  • Data augmentation techniques improved network accuracy without increasing model size.
  • eXnet proved efficient and effective when deployed on a Raspberry Pi 4B for real-time emotion recognition.

Conclusions:

  • eXnet offers a superior solution for facial expression recognition, balancing high accuracy with computational efficiency.
  • The proposed architecture is well-suited for real-time applications and deployment on resource-limited embedded systems.
  • eXnet represents a significant advancement in developing lightweight yet powerful models for emotion recognition in real-world scenarios.