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A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems

  • Fadi Al Machot
  • Ahmad Haj Mosa
  • Alireza Fasih
  • Christopher Schwarzlmüller
  • Mouhanndad Ali
  • Kyandoghere Kyamakya
Part of the Studies in Computational Intelligence book series (SCI, volume 391)

Abstract

This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.

Keywords

Support Vector Machine Support Vector Regression Emotion Recognition High Recognition Rate Advance Driver Assistance System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fadi Al Machot
    • 1
  • Ahmad Haj Mosa
    • 1
  • Alireza Fasih
    • 1
  • Christopher Schwarzlmüller
    • 1
  • Mouhanndad Ali
    • 1
  • Kyandoghere Kyamakya
    • 1
  1. 1.Institute of Smart System Technologies, Transportation Informatics GroupAlpen-Adria-University KlagenfurtKlagenfurtAustria

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