# Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods
## 3 Materials
### Real VOG Data  
The real data set is represented by the collection of real eye images previously reported in [59]. The data were collected from 40 different subjects and have a total of around 1.24 M samples. Every sample consists of a close-up eye image capture with the corresponding gaze information estimated via the means of video oculography (VOG). This custom VOG setup was used in place of standard commercial solutions, such as EyeLink 1000, due to the unique feature that allows saving raw eye images obtained by the ET camera [60]. The system was configured with an 850 nm IR illuminator, ThorLabs DCC1545M camera, and a hot mirror with a chin-rest from EyeLink 1000. The camera recorded monocular images with a resolution of 512 \(\times\) 320 pixels at 120 Hz. The specific software and hardware details of the recording equipment are available in [60]. The schematic side view of the hardware setup can be found in Figure 2. For the purpose of the current study, it is important to note that this set was obtained using a fixed camera setup. Therefore, it captures a significant amount of subject and gaze variability but almost none in camera position and lighting condition domains. Producing synthetic data that cover the existing feature distribution gaps in real data would be a perfect use case for a data augmentation tool considered herein.  
The VOG data contain recordings of the random oblique saccades task that was originally designed to evaluate the effect of calibration grid density on the afforded gaze estimation error [59]. The task presents 174 on-screen fixation targets that densely cover the range of \(\pm\)20.51\({}^{\circ}\) horizontally and \(\pm\)16.7\({}^{\circ}\) vertically in degrees of visual angle. All targets were split into 3 groups, depending on their use case in the prior calibration study. The first 29 target points used to form various calibration grids were shown on the screen for a fixed time of 1.75 s each. The remaining points used for validation and testing purposes were shown for random time drawn from the uniform distribution within the \([1\dots 1.25]\) s range.  
While the VOG data set was originally presented in [59], it was initially available only by private request. To facilitate future ET research, we make it publicly accessible at [[https://doi.org/10.5281/zenodo.13852050](https://doi.org/10.5281/zenodo.13852050)]([https://doi.org/10.5281/zenodo.13852050](https://doi.org/10.5281/zenodo.13852050)) (accessed on 9 October 2024). All recorded subjects  
Figure 2: A schematic side view of the VOG data collection system (reproduced from [60] with the authors’ permission).  
gave informed consent under an experimental protocol approved by the Institutional Research Board of Texas State University (approval code 2018044), and their data were anonymized prior to public release.  
Due to the nature of the employed stimulus, more than 90% of the data correspond to fixations on presented targets. It is unlikely that having a lot of similar images in the training set is beneficial for the performance of a generative network. If anything, when the coverage of the gaze feature is unevenly distributed, it creates a problem of imbalanced train data. This can directly hurt the network's generalization capabilities, as it is expected to have good performance across the whole gaze range. We solved this issue by resampling the original VOG data based on an eye movement classification label provided for each sample using the algorithm explained in [59]. Every image that corresponds to a saccade was included in the new set. For every consecutive range of fixation samples, only three images were included: the first, the last, and a randomly sampled one from within the range. This procedure reduced the total number of VOG samples from around 1.24 M to only 156 k, but resulted in a much more balanced set to be further used in training and evaluating the data generation methods considered herein.