ABSTRACT In this paper, we have proposed a low-cost solution for driver fatigue detection based on micro-sleep patterns. Contrary to conventional methods, we acquired images by placing a camera on the extreme left side of the driver and proposed two algorithms that facilitate accurate face and eye detections, even when the driver is not facing the camera or driver’s eyes are closed. The classification to find whether eye is closed or open is done on the right eye only using SVM and Adaboost. Based on eye states, micro-sleep patterns are determined and an alarm is triggered to warn the driver, when needed. In our dataset, we considered multiple subjects from both genders, having different appearances and under different lightning conditions. The proposed scheme gives 99.9% and 98.7% accurate results for face and eye detection, respectively. For all the subjects, the average accuracy of SVM and Adaboost is 96.5% and 95.4%, respectively.

中文翻译：

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使用 Viola Jones 和主成分分析进行驾驶员疲劳检测

摘要在本文中，我们提出了一种基于微睡眠模式的驾驶员疲劳检测低成本解决方案。与传统方法相反，我们通过将摄像头放置在驾驶员的最左侧来获取图像，并提出了两种算法，即使在驾驶员未面对摄像头或驾驶员闭眼时，也能促进准确的面部和眼睛检测。仅使用 SVM 和 Adaboost 在右眼上完成确定眼睛是闭合还是睁开的分类。根据眼睛状态，确定微睡眠模式，并在需要时触发警报以警告驾驶员。在我们的数据集中，我们考虑了来自两个性别的多个主体，它们具有不同的外观和不同的闪电条件。所提出的方案分别为面部和眼睛检测提供了 99.9% 和 98.7% 的准确结果。