Abstract
Coupled with deep learning technology, a field of gaze estimation has shown remarkable advances in recent decades. These advances however relied on the large amount of computations and resources due to the complex deep learning architecture or the huge volume of data, respectively. In this paper, we propose a resource efficient approach with ensemble loss function to improve the gaze estimation performance. Since eye gaze estimation is initially a regression problem in a broad sense, we employed the ensemble technique of regression loss functions in the pursuit of estimating gaze coordinates on a higher precision, instead of using additional deep layers or much data. Preliminary experiments on MPIIGaze data showed the improved performance compared to state-of-the-art models, the mean average error of 3.7887 cm which is 10.2%, 50.7%, 42.9% better than that of AFF-Net, Itracker, GazeNet, respectively.
S.H. Kim and S.G. Lee—Are co-first authors and contributed equally.
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Kim, S.H., Lee, S.G., Lee, J.H., Lee, E.C. (2023). Improving Gaze Estimation Performance Using Ensemble Loss Function. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_51
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DOI: https://doi.org/10.1007/978-3-031-27199-1_51
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