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@article{150832,
        author = {Eagalaivan R and Saravanan S and Gokul Krishna and Mrinal mahindran},
        title = {Driver Drowsiness Detection using Facial Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {10},
        pages = {224-229},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=150832},
        abstract = {Every year road accidents are increasing and people are losing lives. The root cause of many accidents is due to fatigue of drivers . There are some ongoing research happening to identify the fatigueness of driver many methods have been identified and systems are prepared the use of such systems are expensive and hence it is difficult to implement in every vehicles.Therefore, in this paper, a light-weight, real time driver’s drowsiness detection system is developed and implemented on Android application and as a python application for remote server functioning . The system captures the video frames and detects the driver's face in every frame by employing image processing techniques. The system is capable of detecting facial landmarks, computes Eye Aspect Ratio (EAR) and Eye Closure Ratio (ECR) to detect driver’s drowsiness based on thresholding, thresholding is modified based on driver distance towards the camera. Machine learning algorithms have been employed to test the efficacy of the suggested system. Empirical results demonstrate that the suggested model is able to achieve accuracy of 84% using random forest classifiers.},
        keywords = {},
        month = {},
        }