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Driver Emotional Status Recognition Using Artificial Neural Network

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  • Mohan Arava
  • Prudhvi Ravi Raja Reddy Mallidi

Abstract

Artificial neural network is one of the fascinating area of study, the proposed architecture is performs better feature extraction than earlier proposed Fuzzy logic and SVM algorithms. Day to day the automotive industries are actively supporting research and innovations related to safety issues, performance, and environment. A driver status assessment system constructed in two different modules: one is for driver fatigue detection based on captured images through Digital cameras. The fatigue is a percentage of eyes closure of the best indicator of fatigue for vision systems. And the second one is driver distraction system by using head, facial expressions, and body, a fusion strategy is to deduce the type's driver distractions. Of course many aspects are there but these two fatigue and distraction are only a fraction of all possible drivers' states of their dramatic impact on traffic safety.The standard databases are used for quantitative evaluation of the current state of the art approach using ANN achieves better accuracy then compared to Fuzzy and SVM. The proposed architecture endorses the efficiency and reliable usage of the work for real world applications.

Suggested Citation

  • Mohan Arava & Prudhvi Ravi Raja Reddy Mallidi, 2017. "Driver Emotional Status Recognition Using Artificial Neural Network," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(6), pages 1181-1185, December.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i6:id:hcseit1726329
    Note: Article URL: https://ijsrcseit.com/CSEIT1726329
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