Driver weariness is one of the real reasons behind accidents. Recognizing the sleepiness of the driver is one of the best methods for estimating driver weariness. The motivation behind this paper is to build up a drowsiness detection system that works by monitoring the eye movement of the driver and alerting the driver by producing an alarm or vibration when the person is found drowsy. This paper shows a non-intrusive model for the fatigue detection dependent on processing video streams of an individual's face. The proposed model is not quite the same as meddlesome techniques dependent on natural methodology (Electroencephalogram, Electrooculogram and some sensors), which require gadgets explicitly. Unlike traditional image processing techniques, we use computer vision and machine learning technique to display a prototypal adaptation of a real-time system with individual feedback to monitor and identify when the driver may be sleepy directly from a web camera. The drowsiness detection model depends on face alignment and then evaluation of the Eye Aspect Ratio (EAR) which uses Histogram of oriented gradient (HOG) features combined with Support Vector Machine (SVM) classifier for blink detection. Utilizing such a system, it is conceivable to alarm the client of the threat of nodding off, so that enough actions can be made, diminishing the risk of human mistake and avoiding accidents.