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A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach.

Suparshya Babu Sukhavasi1, Susrutha Babu Sukhavasi1, Khaled Elleithy1

  • 1Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA.

International Journal of Environmental Research and Public Health
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid deep learning model to detect driver emotions, crucial for predicting behavior and enhancing road safety. The advanced system accurately identifies emotions, paving the way for safer driving experiences.

Keywords:
ADAS (advanced driver assistance systems)convolutional neural networkdriver emotion detectionfacial expression recognitionhybrid modelmachine learningsupport vector machine

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Driver emotions significantly impact driving behavior and road safety.
  • Advanced Driver Assistance Systems (ADAS) in the automotive industry utilize AI for driver support.
  • Continuous monitoring of driver emotions can predict behavior and prevent accidents.

Purpose of the Study:

  • To develop a novel hybrid network architecture for predicting driver emotions.
  • To enhance road safety by understanding the link between emotions and driving behavior.
  • To achieve accurate emotion prediction under various conditions like different poses, occlusions, and illumination.

Main Methods:

  • A hybrid network combining a deep neural network and a support vector machine was developed.
  • Fusion of Gabor and Local Binary Pattern (LBP) features was used for emotion determination.
  • A Support Vector Machine (SVM) classifier integrated with a Convolutional Neural Network (CNN) was employed for classification.

Main Results:

  • The proposed model achieved high accuracy across multiple datasets: 84.41% (FER 2013), 95.05% (CK+), 98.57% (KDEF), and 98.64% (KMU-FED).
  • The model demonstrated effectiveness in predicting emotions under challenging conditions.
  • The hybrid approach proved superior in emotion recognition tasks.

Conclusions:

  • The developed hybrid deep learning model shows significant promise for real-time driver emotion recognition.
  • This technology can be integrated into ADAS to improve driver monitoring and road safety.
  • Accurate emotion detection is a key step towards proactive accident prevention systems.