LAE-GAN with Algorithm

LAE-GAN-based Face Image Restoration for Low-light Age Estimation

Introduction

Any work that uses the provided pretrained network must acknowledge the authors by including the following reference

Se Hyun Nam, Yu Hwan Kim, Jiho Choi, Seung Baek Hong, Muhammad Owais and Kang Ryoung Park, “LAE-GAN-based Face Image Restoration for Low-light Age Estimation,” Mathmetics, in submission 

We propose a low-illumination facial image enhancement system with Generative Adversarial Network for Low-light Age Estimation (LAE-GAN), and CNN models for age estimation. These systems are designed to overcome the performance degradation caused by low-illumination environment. Three open databases named as MORPH [1], FG-Net [2], and AFAD [3] are used for experiments. LAE-GAN and age estimation models are opened to other researchers for fair judgement.

[1] MORPH database. Available online: https://ebill.uncw.edu/C20231_ustores/web/store_main.jsp?STOREID=4 (accessed on 17 May 2021)

[2] FGNET database. Available online: https://yanweifu.github.io/FG_NET_data/index.html (accessed on 17 May 2021).

[3] AFAD database. Available online: https://afad-dataset.github.io (accessed on 17 May 2021).

Implementation

• Python >= 3.5
• Tensorflow >= 2.2.0
• Unbuntu 10.4

Model weights (LAE-GAN and DEX)

• LAE-GAN with MORPH, FG-NET, and AFAD database
• DEX with MORPH, FG-NET, and AFAD database