Eyelid Fold Consistency in Facial Modeling
Lohit Petikam, Charlie Hewitt, Fatemeh Saleh, Tadas Baltrušaitis
TL;DR
This work proposes a new definition of eyelid fold consistency and implements geometric processing techniques to model diverse eyelid shapes in a unified topology and reprocess data used to train a parametric face model and demonstrates significant improvements in face-related machine learning tasks.
Abstract
Eyelid shape is integral to identity and likeness in human facial modeling. Human eyelids are diverse in appearance with varied skin fold and epicanthal fold morphology between individuals. Existing parametric face models express eyelid shape variation to an extent, but do not preserve sufficient likeness across a diverse range of individuals. We propose a new definition of eyelid fold consistency and implement geometric processing techniques to model diverse eyelid shapes in a unified topology. Using this method we reprocess data used to train a parametric face model and demonstrate significant improvements in face-related machine learning tasks.
