Thesis Details
Simulation of Skin Diseases Effect Using GAN
The aim of this master's thesis is to generate a dataset of synthetic fingerprint images that display symptoms of skin disease. The thesis deals with damage caused by skin disease in the fingerprint images and synthetic fingerprint generation. The diseased fingerprints are generated using a model based on Wasserstein GAN with gradient penalty. A unique diseased fingerprint database created at FIT BUT was used for training of the GAN model. The model was trained on three types of skin disease: atopic eczema, psoriasis vulgaris and dyshidrotic eczema. The generator network of the trained WGAN-GP model was used to generate datasets of synthetic fingerprint images. The synthetic images were compared with real fingerprint images using the NFIQ and FiQiVi quality assessment tools and by comparing minutiae location and minutiae orientation distributions in the fingerprint images.
fingerprints, synthetic fingerprints, fingerprint generation, skin disease, generative adversarial network, GAN, convolutional neural networks, Python, PyTorch
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Lengál Ondřej, Ing., Ph.D. (DITS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
Zbořil František V., doc. Ing., CSc. (DITS FIT BUT), člen
@mastersthesis{FITMT24097,
author = "Adam Bak",
type = "Master's thesis",
title = "Simulation of Skin Diseases Effect Using GAN",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/thesis/24097/"
}