The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.
@article{arxiv.2211.02799,
title = {Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation},
author = {Saurav K. Aryal and Teanna Barrett and Gloria Washington},
journal= {arXiv preprint arXiv:2211.02799},
year = {2022}
}