Monitoring the emotions of drivers are the key aspects while designing the advanced driver assistance systems (ADAS) in vehicles. To ensure the safety and track the possibility of the accidents, the emotion monitoring will play a key role in justifying the mental status of the driver. Recent developments in face expression recognition have brought the tremendous attention across the world due to its intellectual capabilities to track the facial expressions. Machine learning and deep learning technologies have helped a lot in developing an efficient face expression recognition systems. Two novel approaches using machine learning, deep learning algorithms and residual neural networks are proposed to monitor six class of expressions of the driver in different pose variations and occlusions. We obtained the better accuracies with these two novel approaches when compared to the state of art methods
