The project focuses on the drowsiness of IT employees, drivers, pilots, crane operators, student etc. These people need a system which can alert them, and others when they start taking a nap. A nap during work is quite important, but can also be dangerous for some types of work. So it is quite sensible to create a system which can detect drowsiness. The approaches which we used for the project are Support Vector Machine; YOLO architecture and Resnet-101 model of deep learning. The best accuracy was however achieved using SVM and HOG implementation, since they used mathematical approach to designate facial properties, based on a fixed ratio of facial features. Thus, we also conclude that a problem must be identified before implementation and every deep learning model cannot bring accurate predictions and accuracy.