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A sleepy driver is arguably much more dangerous on the road than the one whois speeding as he is a victim of microsleeps. Automotive researchers andmanufacturers are trying to curb this problem with several technologicalsolutions that will avert such a crisis. This article focuses on the detectionof such micro sleep and drowsiness using neural network based methodologies.Our previous work in this field involved using machine learning withmulti-layer perceptron to detect the same. In this paper, accuracy wasincreased by utilizing facial landmarks which are detected by the camera andthat is passed to a Convolutional Neural Network (CNN) to classify drowsiness.The achievement with this work is the capability to provide a lightweightalternative to heavier classification models with more than 88% for thecategory without glasses, more than 85% for the category night without glasses.On average, more than 83% of accuracy was achieved in all categories. Moreover,as for model size, complexity and storage, there is a marked reduction in thenew proposed model in comparison to the benchmark model where the maximum sizeis 75 KB. The proposed CNN based model can be used to build a real-time driverdrowsiness detection system for embedded systems and Android devices with highaccuracy and ease of use.

Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application