Drowsiness Detection using Facial Emotions and Eye Aspect Ratios

Abstract

Drowsy drivers are a major cause of many road accidents around the world. Facial emotions are known to be one of the visual cues for detecting drowsiness. In this paper, we propose a machine learning approach to drowsiness detection based on using a combination of facial emotion features extracted by using deep convolutional neural networks (CNN) and eye-aspect-ratio (EAR) features. The combined feature vectors are then used for training a classifier. From our experiments, we obtain a classification accuracy of 81.7% when we use the combined features with a support vector machines (SVM) classifier.