Abstract
In this paper we study the feasibility of using standard deformable model fitting techniques to accurately track the deformation and motion of the human eye. To this end, we propose two highly detailed shape annotation schemes (open and close eyes), with \(+30\) feature landmark points, high resolution eye images. We build extremely detailed Active Appearance Models (AAM), Constrained Local Models (CLM) and Supervised Descent Method (SDM) models of the human eye and report preliminary experiments comparing the relative performance of the previous techniques on the problem of eye alignment.
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Alabort-i-Medina, J., Qu, B., Zafeiriou, S. (2015). Statistically Learned Deformable Eye Models. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_19
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DOI: https://doi.org/10.1007/978-3-319-16178-5_19
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