With an increase in population, the occurrence of automobile accidents has also seen an increase. Road traffic accidents are a common cause of trauma and loss of life in the community. Drowsiness or sleepiness is one of the key causes for traffic accidents and has a serious impact on road safety. Many fatal accidents can be avoided by warning sleepy drivers on time. According to World Health Organization, traffic accidents have gone up to 1.25 billion around the world and sensing driver’s fatigue will be one of the main potential areas for avoiding many sleep-induced traffic accidents. There are different types of drowsiness detection methods that scan whether the person driving is fatigued or not while driving and alert the driver if he is not focused on driving. To prevent this from happening, we have proposed a facial recognition algorithm that detects driver drowsiness from facial features. The algorithm first detects facial features such as yawning and blinking frequency. The facial features determine the driver’s condition. By combining the condition of the driver with the responsiveness of the eyes and mouth, the driver will be alerted.


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    Titel :

    Drowsiness Detection Using Facial Features, Image Processing and Machine Learning


    Weitere Titelangaben:

    Lecture Notes on Data Engineering and Communications Technologies


    Beteiligte:
    Pandian, A. Pasumpon (Herausgeber:in) / Fernando, Xavier (Herausgeber:in) / Haoxiang, Wang (Herausgeber:in) / Nandhini, S. (Autor:in) / Venkatasubramanian, Vaishnavi (Autor:in) / Aparna, C. (Autor:in)


    Erscheinungsdatum :

    2022-05-22


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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