Driver drowsiness detection using hybrid convolutional neural network and long short-term memory

Abstract

Drowsiness and fatigue of the drivers are amongst the significant causes of the car accidents. Every year the number of deaths and fatalities are tremendously increasing due to multifaceted issues and henceforth requires an intelligent processing system for accident avoidance. In relevant with this, an effective driver drowsiness detection system is proposed. The main challenges are robustness of the algorithm towards variation of the human face and real-time processing capability. The first challenge pertaining to the facial variation has been handled well using conventional image processing and hand-craft features of computer vision algorithms. Yet, variations such as facial expression, lighting condition, intra-class variation, and pose variation are additional issues that conventional method failed to address. Deep learning is an alternative solution which provides a better performance by learning features automatically. Thus, this paper proposed a new concept for handling the real-time driver drowsiness detection using the hybrid of convolutional neural network (CNN) and long short-term memory (LSTM). The performance of the system has been tested using the public drowsy driver dataset from ACCV 2016 competition. The results show that it can outperform the former schemes in the literature.