A Data-augmented 3D Morphable Model of the Ear

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationThe 13th IEEE International Conference on AUTOMATIC FACE AND GESTURE RECOGNITION (FG 2018)
Number of pages5
ISBN (Print)9781538623350
Publication statusPublished - 15 May 2018

Bibliographical note

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  • 3D Morphable Model; 3D shape registration; 3D image analysis

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