Abstract
Drowsiness detection is a solution for identifying signs of fatigue or sleepiness in individuals. One of the key features of our model is that it can detect drowsiness at night as well using Mobile cameras (infrared sensors). The system captures infrared images of the person's face and analyzes the physiological and behavioral cues related to drowsiness. Infrared sensors allow for drowsiness detection in low-light conditions, making it particularly useful for night-time scenarios such as night driving. The system can trigger alerts or interventions if drowsiness is detected, helping to prevent accidents or mistakes. We will be using libraries like OpenCV, TensorFlow, CNN, and VGG19 features in our model. By combining the accessibility of Android devices with the advanced capabilities of the Deep Learning algorithm, drowsiness detection using infrared sensors has the potential to greatly improve the safety and productivity of individuals in their daily lives.
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