Accurate Emotion Recognition for Driving Risk Prevention in Driver Monitoring System

Abstract

A new driver monitoring system called DeriskNet for driving risk prevention is proposed in this paper. We first develop a deep convolutional neural network to recognize the driver's emotions. Then, we devise an audio on demand mechanism to automatically collect audio resources via web crawling for preventing driving risks from driver's negative emotions. Experiment results show that our DeriskNet provides superior accuracy and reliability than the conventional convolutional neural network designs in terms of facial emotion recognition.