A Data-Augmented 3D Morphable Model of the Ear

作者：H. Dai, Nick E. Pears, William Smith · 发表于：IEEE International Conference on Automatic Face & Gesture Recognition · 年份：2018 · DOI：10.1109/FG.2018.00065 · 被引用次数：36 · 研究领域：Computer Science

Morphable models are useful shape priors for biometric recognition tasks. Here we present an iterative process of refinement for a 3D Morphable Model (3DMM) of the human ear that employs data augmentation. The process employs the following stages 1) landmark-based 3DMM fitting; 2) 3D template deformation to overcome noisy over-fitting; 3) 3D mesh editing, to improve the fit to manual 2D landmarks. These processes are wrapped in an iterative procedure that is able to bootstrap a weak, approximate model into a significantly better model. Evaluations using several performance metrics verify the improvement of our model using the proposed algorithm. We use this new 3DMM model-booting algorithm to generate a refined 3D morphable model of the human ear, and we make this new model and our augmented training dataset public.