Ear semantic segmentation in natural images with Tversky loss function supported DeepLabv3+ convolutional neural network

Year 2022, Volume: 10 Issue: 3, 337 - 346, 30.07.2022

Tolga Inan , Umit Kacar

https://doi.org/10.17694/bajece.1024073

Abstract

Semantic segmentation is a fundamental problem
for computer vision. On the other hand, for studies in the field
of biometrics, semantic segmentation is gaining more importance.
Many successful biometric recognition systems require a high-
performance semantic segmentation algorithm. In this study, we
present an effective ear segmentation technique in natural images.
A convolutional neural network is trained for pixel-based ear
segmentation. DeepLab v3+ network structure, with ResNet-18 as
the backbone and Tversky lost function layer as the last layer, has
been trained with natural and uncontrolled images. We perform
the proposed network training using only the 750 images in the
Annotated Web Ears (AWE) training set. The corresponding tests
are performed on the AWE Test Set, University of Ljubljana
Test Set, and the Collection A of In-The-Wild dataset. For the
Annotated Web Ears (AWE) dataset, intersection over union
(IoU) is measured as 86.3% for the AWE database. To the best of
our knowledge, this is the highest performance achieved among
the algorithms tested on the AWE test set.

Keywords

Semantic Segmentation, Ear Segmentation, Convolutional Neural Networks, Tversky Loss Function, biometrics

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