Eyelid Fold Consistency in Facial Modeling
This work addresses the problem of improving facial modeling accuracy for diverse individuals, though it appears incremental as it builds on existing parametric models.
The paper tackled the problem of insufficient likeness preservation in parametric face models for diverse eyelid shapes by proposing a new definition of eyelid fold consistency and implementing geometric processing techniques, demonstrating significant improvements in face-related machine learning tasks.
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.