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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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The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance.

Jung Hwan Kim1, Alwin Poulose1, Dong Seog Han1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea.

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Summary

Improving facial emotion recognition (FER) for autonomous vehicles is crucial. This study introduces a facial image threshing (FIT) machine to enhance FER dataset quality, significantly boosting real-time performance and accuracy.

Keywords:
CK+ DatasetFER 2013 DatasetMTCNNResNetXceptionautonomous drivingconvolution neural network (CNN)facial emotion recognition (FER)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Facial emotion recognition (FER) is vital for driver monitoring in autonomous vehicles to mitigate road rage.
  • Current FER systems struggle with real-time performance due to inadequate training datasets, irrespective of algorithm sophistication.
  • Dataset quality critically impacts FER system efficacy more than algorithmic advancements.

Purpose of the Study:

  • To enhance the performance of facial emotion recognition (FER) systems in autonomous vehicles.
  • To address the limitations of existing FER datasets and improve real-time testing accuracy.
  • To propose a novel method for improving FER system robustness through advanced dataset processing.

Main Methods:

  • Development of a facial image threshing (FIT) machine utilizing pre-trained facial recognition and the Xception algorithm.
  • Implementation of data cleaning techniques including removal of irrelevant images, data collection, correction of misaligned data, and large-scale dataset merging.
  • Application of data-augmentation techniques to enrich the training dataset.

Main Results:

  • The proposed FIT machine achieved a 16.95% improvement in validation accuracy compared to conventional methods using the FER 2013 dataset.
  • Confusion matrix evaluation on an unseen private dataset demonstrated a 5% improvement over the original approach, validating real-time performance.
  • Enhanced dataset quality led to superior FER system performance in autonomous vehicle simulations.

Conclusions:

  • The proposed FIT machine significantly enhances FER system performance for autonomous vehicles by improving dataset quality.
  • The method demonstrates a practical approach to overcoming dataset limitations in real-world FER applications.
  • Improved FER accuracy contributes to safer and more reliable autonomous driving experiences.