Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

Abstract

Drowsiness detection during driving is still an unsolved research problem which needs to be addressed to reduce road accidents. Researchers have been trying to solve this problem using various methods where most of these solution lacks behind in accuracy, real-time performance, costly, complex to build, and has a higher computational cost with low frame rate. This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially. Proposed method used frame aggregation strategy in case of face region cannot be extracted in any frame due to shortcomings, i.e. light reflection, shadow. In order to improve identification under highly varying lighting conditions, proposed research used cascade of regressors cutting edge method where each regression refers estimation of facial landmarks. Proposed method used deep convolutional neural network (DCNN) for accurate pupil detection to learn non linear data pattern. In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations during training phase which makes overall methodology less influenced by parameter initialization. Proposed research performed extensive experimentation where accuracy rate of 98.97% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.