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@article{152123,
        author = {Aditi Gaur and Mr. Raghuveer Sachan and Ashutosh Dwivedi and Niharika Singh and Shivansh Pandey and Ankush Singh},
        title = {Eye Blinking and Drowsiness Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {582-586},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152123},
        abstract = {A driver sleepiness detection system is presented, which involves the use of an algorithm to identify driver tiredness. The most relevant visual indications that represent the driver's condition for detecting drowsiness are eye behaviour. An eye aspect ratio and physical landmark data are used in the facial algorithm. The algorithm's landmark detectors are resistant to a wide range of head orientations, face expressions, and illumination conditions. In each video frame, the proposed real-time method will estimate eye aspect ratio, which measures eye open level. It interprets the pattern of eye blinks as EAR values. Potential sleepiness is recognised in this way. Drivers falling asleep due to weariness or long-haul driving, as well as irresponsibility, cause a huge number of road accidents. By delivering non-invasive and simple-to-use specialised gadgets, the suggested system under development can assist prevent this.},
        keywords = {dlib library, drowsiness, EAR(Eye Aspect Ratio), Warnings},
        month = {},
        }