CNN Based Subject Independent Driver Emotion Recognition System Involving Physiological Signals for ADAS
Supporting drivers by Advanced Driver Assistance Systems (ADAS) significantly increases road safety. Driver's emotions recognition is a building block of advanced systems for monitoring the driver's comfort and driving ergonomics additionally to driver's fatigue and drowsiness forecasting. This paper presents an approach for driver emotions recognition involving a set of three physiological signals (Electrodermal Activity, Skin Temperature and the Electrocardiogram) Additionally, we propose a CNN (cellular neural network) based classifier to classify each signal into four emotional states. Moreover, the subject independent classification results of all signals are fused using Dempster Shafer evidence theory in order to obtain a more robust detection of the true emotional state. The new system is tested using the benchmarked MAHNOB HCI dataset and the results show a relatively high performance compared to existing competing algorithms from the recent relevant literature.