Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction
This work addresses the need for efficient and flexible analysis of eye movement data in fields like human-computer interaction and psychology, though it is incremental in its application of existing neural network architectures.
The paper tackles the problem of processing raw eye tracking data by using fully convolutional neural networks for segmentation, generation, and reconstruction, achieving results comparable to state-of-the-art methods on three public datasets.
In this paper, we use fully convolutional neural networks for the semantic segmentation of eye tracking data. We also use these networks for reconstruction, and in conjunction with a variational auto-encoder to generate eye movement data. The first improvement of our approach is that no input window is necessary, due to the use of fully convolutional networks and therefore any input size can be processed directly. The second improvement is that the used and generated data is raw eye tracking data (position X, Y and time) without preprocessing. This is achieved by pre-initializing the filters in the first layer and by building the input tensor along the z axis. We evaluated our approach on three publicly available datasets and compare the results to the state of the art.