# Advancements in Gaze Coordinate Prediction Using Deep Learning: A Novel Ensemble Loss Approach
## 6 Conclusions  
The devices used for gaze tracking are expensive, and most predictive models use infrared cameras, making them unusable on a wide range of devices. Therefore, to overcome this problem, many studies using only RGB cameras are being conducted. In addition, models that can be used straight away without any calibration steps are more usable as they can be used in a wider range of situations. Therefore, our primary goal was to develop a model that accurately predicts the user's gaze position using only RGB images, without the need for individual calibration. As a result, state-of-the-art performance was achieved by applying a new loss function. In addition, as a result of comparing the performance before and after applying the proposed loss function to various existing models, it was confirmed that the performance of all models improved, showing that the loss function itself can help improve the performance of existing models. This can be used as an additional triggering method to improve performance with lower computational cost when applied to other models to be studied in the future. Lastly, as mentioned in the discussion, the model was able to make predictions and show sufficient performance even in a monitored environment that the model did not experience during the training phase. This means that the model is not dependent on the training data set and is applicable to a wider range of environments. Our future research direction is to expand not only to the computer monitor environment but also to many mobile devices such as mobile phones and tablets. As most mobile devices are equipped with RGB cameras for eye tracking technology, we can expect greater accessibility to eye tracking technology. Therefore, our future work is to present the results of this study on data obtained from a wider range of devices.  
Conceptualization, E.C.L., S.K. and S.L.; methodology, S.K. and S.L.; software, S.K. and S.L.; validation, S.K. and S.L.; formal analysis, S.K. and S.L.; investigation, S.K. and S.L.; resources, S.K. and S.L.; data curation, S.K. and S.L.; writing--original draft preparation, S.K. and S.L.; writing--review and editing, S.K. and S.L.; visualization, S.K. and S.L.; supervision, E.C.L.; project administration, E.C.L.; funding acquisition, E.C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a 2024 Research Grant from Sangmyung University (2024-A000-0090).  
Not applicable.  
**Data Availability Statement:** Since the data used in this study are a public open dataset, they can be used by contacting the data holder ([[https://perceptualui.org/research/datasets/MPIIFaceGaze/](https://perceptualui.org/research/datasets/MPIIFaceGaze/)]([https://perceptualui.org/research/datasets/MPIIFaceGaze/](https://perceptualui.org/research/datasets/MPIIFaceGaze/)), accessed on 18 May 2024).  
Figure 9: Scatter plot of the predicted and actual values of the x- and y-axis planes of the two-dimensional gaze coordinates predicted by applying the ensemble loss function to the existing iTracker for Person 6 (**(a)**: x-axis, **(b)**: y-axis).