Deep learning for 3D ear detection: A complete pipeline from data generation to segmentation

The human ear has distinguishing features that can be used for identification. Automated ear detection from 3D profile face images plays a vital role in ear-based human recognition. This work proposes a complete pipeline including synthetic data generation and ground-truth data labeling for ear detection in 3D point clouds. The ear detection problem is formulated as a semantic part segmentation problem that detects the ear directly in 3D point clouds of profile face data. We introduce EarNet, a modified version of the PointNet++ architecture, and apply rotation augmentation to handle different pose variations in the real data. We demonstrate that PointNet and PointNet++ cannot manage the rotation of a given object without such augmentation. The synthetic 3D profile face data is generated using statistical shape models. In addition, an automatic tool has been developed and is made publicly available to create ground-truth labels of any 3D public data set that includes co-registered 2D images. The experimental results on the real data demonstrate higher localization as compared to existing state-of-the-art approaches

Human Ear Surface Reconstruction Through Morphable Model Deformation

International audienceIn this paper, a novel fully automated method is developed to acquire an accurate surface 3D reconstruction of the human ear by using multi-view stereo vision and morphable model without texture. As the results show, our method outperform state of the art approaches. Our method is based on using a template to estimate the pose and orientation of the camera without relying on correspondences, and after dense reconstruction is done, the ear morphable model is fitted on this point cloud by minimizing the distance between them, the form of the model can be transform as wished by its coefficients, and it only uses shape without relying on texture to converge its coefficients