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Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation

2022-11-05 02:05:52
Saurav K. Aryal, Teanna Barrett, Gloria Washington
arXiv_CV
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.02799

PDF

https://arxiv.org/pdf/2211.02799.pdf

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