Publication Type:

Conference Paper

Authors:

M. Patil; S. Veni

Source:

2019 International Conference on Communication and Signal Processing (ICCSP), p.420-424 (2019)

Keywords:

driver emotion recognition, Driver response time, driving behavior, emotion, Emotion recognition, face, facial expression, Facial landmark, facial landmark features, Feature extraction, Histograms, Human Machine Interface, Local binary pattern, man-machine systems, Microsoft Windows, supervised machine learning algorithm, Support Vector Machine, Support vector machines, SVM, Vehicles

Abstract:

Emotion recognition of a driver is a crucial task in vehicles. Emotions have an adverse influence on driving behavior. Emotions like anger, fear, and sadness have a negative impact on driver response time and may lead to fatal accidents. This paper considers five specific kinds of emotions labelled anger, fear, happy, neutral, and sadness, that may occur in a driver. This method fuses both LBP and facial landmark features to detect emotions. The supervised machine learning algorithm, Support Vector Machine (SVM) is used for the classification of different emotions. Performance on extended Cohn-Kanade dataset is obtained, exhibited and analyzed. With this proposed method, we obtained an accuracy of 86.7%.

Notes:

cited By 0; Conference of 8th IEEE International Conference on Communication and Signal Processing, ICCSP 2019 ; Conference Date: 4 April 2019 Through 6 April 2019; Conference Code:147623

Cite this Research Publication

M. Patil and S. Veni, “Driver Emotion Recognition for Enhancement of Human Machine Interface in Vehicles”, in 2019 International Conference on Communication and Signal Processing (ICCSP), 2019, pp. 420-424.