An Eyelid Simulator for Zero Shot Eyelid Segmentation

Eyelid detection is an important feature for fatigue estimation in autonomous driving, for example. It allows for the estimation of driver readiness if a takeover event occurs, and is therefore crucial for the safety of car passengers and traffic participants. We propose to use two splines and two randomly filled regions as a simple eyelid simulator for deep neural network training. This allows a fast generation of perfectly labeled training images and only needs a fraction of computational resources if compared to rendering-based approaches. We compared our approach against the training on real-world images as well as other rendering-based approaches in terms of accuracy and resource consumption. In addition, we evaluated different combinations of the rendering-based approaches, real-world images, and our approach.