Driver drowsiness monitoring system based on facial Landmark detection with convolutional neural network for prediction

Several factors often contribute to car accidents, most of them caused by human error, and the notable are drowsiness, fatigue, distracted driving, alcohol. Although self-driving cars best solution save lives avoid they expensive. The roads in many countries not prepared for movement this type car. Scare new technologies included modern cars, such as backup cameras sensors, contributed keeping drivers safer paper. A driver monitoring system is based on determining driver’s face’s main points, which provide required vital information face analysis. EfficientNet convolutional neural network (ConvNet) model used facial landmarks prediction, employed detect drowsiness fatigue real-time. trained multiple traits, including expressions, yawning head poses. results show that employing will assist efficiently producing eyes mouth features, can appropriately creating models analyze drowsiness. Due this, proposed safety features applicable available future vehicles.