Thesis Details
Adaptace neuronových sítí pro identifikaci osob
This thesis deals with facial recognition using convolutional neural networks and with their current problems, which are pose, lighting and expression variance. It summarizes existing approaches, architectures and most recent loss functions. Further it deals with methods for rotating faces using GAN networks. In this thesis 3 neural networks are designed and trained for facial recognition. The best of them achieves 99.38% accuracy on LFW dataset and 88.08% accuracy on CPLFW dataset. Next face rotation network PCGAN is designed, which can be used for face frontalization or data augmentation purposes. This network is evaluated on Multi-PIE dataset and using the face frontalization it increases identification accuracy.
neural networks, convolutional neural networks, facial recognition, face rotation, face synthesis, GAN networks, augmentation, frontalization
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Horák Aleš, doc. RNDr., Ph.D. (FI MUNI), člen
Hrubý Martin, Ing., Ph.D. (DITS FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT22093,
author = "Jan Stratil",
type = "Master's thesis",
title = "Adaptace neuronov\'{y}ch s\'{i}t\'{i} pro identifikaci osob",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2019,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/22093/"
}