Eyes Detector Approach for Driving Monitoring System for Occluded Faces without using Facial Landmarks

The current health situation with the use of masks complicates the analysis of gaze and head direction in driver monitoring systems based on facial detection since landmarks are not working properly. Due to this issue, the need to solve occlusion problems using an alternative method to the current ones has increased. On the other hand, the deployment of these systems inside the vehicles must be carried out in the least intrusive way possible for the driver. This article presents an approach for driver distraction analysis based on the driver's eyes without using landmarks applying Deep Learning methods, and the study of different parameters such as detection speed for the deployment of the best accuracy-speed method in an embedded platform. Different state-of-the-art and open source neural networks have been used and tuned to address our current problem. On the other hand, as is well known, training these models requires an enormous amount of data. In the case of gaze, there are very few data sets dedicated specifically to it. UnityEyes software has been used to create the training and test datasets for the system since it creates the necessary amount of data needed by the models easily.