Deep Learning-Based Drivers Emotion Classification System in Time Series Data for Remote Applications

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 3; p. 587
• Main Authors: Naqvi, Rizwan Ali, Arsalan, Muhammad, Rehman, Abdul, Rehman, Ateeq Ur, Loh, Woong-Kee, Paul, Anand
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-02-2020
• Subjects: aggressive driving; change in gaze; deep learning; emotions sensing; facial emotions; gaze tracking; normal driving; time series data
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12030587

Aggressive driving emotions is indeed one of the major causes for traffic accidents throughout the world. Real-time classification in time series data of abnormal and normal driving is a keystone to avoiding road accidents. Existing work on driving behaviors in time series data have some limitations and discomforts for the users that need to be addressed. We proposed a multimodal based method to remotely detect driver aggressiveness in order to deal these issues. The proposed method is based on change in gaze and facial emotions of drivers while driving using near-infrared (NIR) camera sensors and an illuminator installed in vehicle. Driver’s aggressive and normal time series data are collected while playing car racing and truck driving computer games, respectively, while using driving game simulator. Dlib program is used to obtain driver’s image data to extract face, left and right eye images for finding change in gaze based on convolutional neural network (CNN). Similarly, facial emotions that are based on CNN are also obtained through lips, left and right eye images extracted from Dlib program. Finally, the score level fusion is applied to scores that were obtained from change in gaze and facial emotions to classify aggressive and normal driving. The proposed method accuracy is measured through experiments while using a self-constructed large-scale testing database that shows the classification accuracy of the driver’s change in gaze and facial emotions for aggressive and normal driving is high, and the performance is superior to that of previous methods.