Drowsiness Detection and Accident Prevention by using K Nearest Neighbor (KNN) Algorithm

With the advanced technologies, car accidents can be prevented by implementing an advanced driver drowsiness detection system. This research work has implemented a novel advanced drowsiness detection systems by employing mathematical techniques such as EAR, MAR, MOE, and PUC. A precise formula is used to calculate the MOE value. The pupil circularity value is also calculated to predict drowsiness. After successful calculation of these values, a person's drowsiness is detected by using a specified formula. In order to predict the accuracy of the proposed model, the K-Nearest Neighbor (KNN) algorithm has been used