Detecting the Driver?s Closed Eyes Using Support Vector Machine

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@article{1705429,
      author = {Pravin Choudhary, Omkar Sawant, Sherilyn Kevin, Santosh Singh},
      title = {Detecting the Driver?s Closed Eyes Using Support Vector Machine},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {484-488},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705429.pdf},
      abstract = {In today's fast-paced world, road safety remains a critical concern as millions of lives are at stake every day due to vehicular accidents. Driver tiredness is one of the primary causes of these issues. Therefore, the detection of closed and open eyes in drivers holds immense significance in enhancing road safety and preventing potential accidents resulting from driver fatigue or distraction. Driver?s eyes closed or open detection using deep learning algorithm is a promising approach to prevent accidents caused by driver drowsiness. Deep learning algorithms have shown superior performance in image classification tasks, and they can be used to extract features from eye images that are indicative of whether the eyes are open or closed. This study gives detailed deep learning approaches of different strategies and methodologies used in driver closed and open eye recognition systems. The accurate identification of the driver's eye state is a crucial component of modern driver monitoring systems. Support Vector Machine (SVM) is used for the detection of a car driver?s close or open eye. The models are trained on a dataset of closed and open eyes which is taken from the Kaggle. The model achieves a 98.82% accuracy rate for detecting whether the driver's eyes are open or closed.},
      keywords = {Classification, Closed Eyes, Deep Learning, Open Eyes, Support Vector Machine.},
      month = {January},
  }