Methodology

We propose a fully convolutional neural network for pupil and eyelid segmentation as well as eyelid landmark and pupil ellipsis regression. The network is jointly trained using the Log loss for segmentation and L1 loss for landmark and ellipsis regression.

A blink detection algorithm on eye images tailored towards head-mounted eye-trackers. Blinks are an indicator of fatigue and drowsiness. They can assist in the diagnosis of mental disorders, such as schizophrenia.

Approaches for segmentation and synthesis of eye-tracking data using different neural networks and machine learning approaches.

The reliable estimation of the pupil position in eye images is perhaps the most important prerequisite in gaze-based HMI applications.

Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding.

We evaluated Generative Adversarial Networks(GAN) for eyelid and pupil area segmentation, data generation, and image refinement. While the segmentation GAN performs the desired task, the others serve as supportive Networks.