A Novel Smart Facial Features for Real-Time Motorists Sleepiness Prediction and Alerting System Using Hybrid Deep Convolutional Neural Network in Computer Vision

Motorists Sleepiness prediction is a process of detectingwhen anOperator is experiencing Sleepiness or fatigue while driving a vehicle. This is an important
safety feature, as Sleepy driving can lead to accidents and injuries. There are several methods used to predict Operator Sleepiness, including physiological monitoring, behavioural monitoring, and hybrid methods. Physiological monitoring
methods like CNN involve measuring the Operator’s physiological signals, such as image or video frame processing from a camera. These frames can provide
information about the Operator’s level of alertness and can be used to detect Sleepiness. Behavioural monitoring methods that are DCNN on the other hand, involve observing theOperator’s behaviour, such as comparing the frameswith the processed dataset. This information is mainly used to detect Sleepiness. Hybrid methods combine physiological and behavioural monitoring methods of CNN and DCNN and added to the fuzzy logic algorithm makes an HDCNN (Hybrid Deep
Convolutional Neural Network) to provide a more comprehensive assessment of the Operator’s level of Sleepiness and improves the accuracy. This article explains
techniques to spot the lips and eyes in a video taken during a research project by the Indian Institute of Road Safety (IIROS). A footage of the transition from awake to fatigued to drowsy will be captured using the digital camera. The Proposed algorithm’s function is to locate the face recognized in a captured video image. The face region is used mostly for its ability to act independently.