AEPI: Automated Ear Pinna Identification.

Code release for the paper AEPI: Representation Learning and Evaluation of Human Ear Identification based on a blend of Residual Network and Spatial Encoding.

Authors:Usama Hasan ,Waqar Hussain,Nouman Rasool

Introduction

In this work, we present automated ear identification model on Ear VN dataset, a large-scale ear images dataset in the wild.

Supported features and ToDo list

• Multiple GPUs for training

Requirements:

All the codes are tested in the following environment:

• Linux (tested on Ubuntu 16.04/18.04)
• Python 3.6+
• PyTorch 1.6

Usage:

a. Clone the AEPI repository.

git clone  https://github.com/UsamaHasan/AEPI-Automated-Ear-Pinna-Identification

cd AEPI-Automated-Ear-Pinna-Identification && cd src

python train.py --epochs 100 --batch_size 256 --lr 1e-3

Results:

Method	Top 1 Accuracy	Top 3 Accuracy
VGG-19	55.34	72.13
VGG-19 + SE	59.79	75.75
ResNet-50	60.55	76.64
ResNet-50 + SE	66.22	81.54
ResNet-152+ SE	75.5410	87.207

Dataset:

EarVN1.0: A new large-scale ear images dataset in the wild.

Hoang VT. EarVN1.0: A new large-scale ear images dataset in the wild. Data in Brief. 2019 Dec;27:104630. DOI: 10.1016/j.dib.2019.104630.