Sabrina Hoppe (PhD Student)

Common computational methods for automated eye movement detection - i.e. the

task of detecting different types of eye movement in a continuous stream of

gaze data - are limited in that they either involve thresholding on

hand-crafted signal features, require individual detectors each only detecting

a single movement, or require pre-segmented data. We propose a novel approach

for eye movement detection that only involves learning a single detector

end-to-end, i.e. directly from the continuous gaze data stream and

simultaneously for different eye movements without any manual feature crafting

or segmentation. Our method is based on convolutional neural networks (CNN)

that recently demonstrated superior performance in a variety of tasks in

computer vision, signal processing, and machine learning. We further introduce

a novel multi-participant dataset that contains scripted and free-viewing

sequences of ground-truth annotated saccades, fixations, and smooth pursuits.

We show that our CNN-based method outperforms state-of-the-art baselines by a

large margin on this challenging dataset, thereby underlining the significant

potential of this approach for holistic, robust, and accurate eye movement

protocol analysis.