Abstract
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.
Document Type
Journal Article
Date of Publication
1-1-2021
Volume
9
Publication Title
IEEE Access
Publisher
IEEE
School
School of Science
RAS ID
39868
Funders
Edith Cowan University
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
First Page
164976
Last Page
164985
Comments
Mursalin, M., & Islam, S. M. S. (2021). Deep learning for 3D ear detection: A complete pipeline from data generation to segmentationn. IEEE Access, 9, 164976-164985.
https://doi.org/10.1109/ACCESS.2021.3129507