Face produce data that can be used to determine tiredness level. Many facial appearances derived from the face to decide the extent of fatigue. Yawning, head movements and eye blink are examples. In this paper we detect the driver’s tiredness condition without equipping their body to devices. However, developing a drowsiness detection system that is dependable and systematic is a difficult challenge that necessitates precise and robust algorithms. To identify driver tiredness, a number of procedures have been tested in the past. Because deep learning is becoming more popular, these algorithms must be reevaluated to determine their capability to detect drowsiness. Therefore, this study examines machine learning approaches such as Hidden Markov Models (HMMs), Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) in the context of drowsiness detection.

Machine Learning, Drowsiness Detection System, facial expression, fatigue detection.