Thesis Details
Detekce a klasifikace poškození otisku prstu s využitím neuronových sítí
The aim of the diploma thesis is to study and propose improvement of the current convolutional neural network for the classification and detection of fingerprint disease. An improvement of the current convolutional neural network is the change of library for the algorithm of learning, detecting and classifying fingerprint damage. Other improvements are to change the convolutional neural network model and a change in the activation function. At the same time, preprocessing using the Gabor filter will be added. Another change is in the area of thresholding. Next, there will be a change in general-purpose algorithms that will simplify the work for expanding database creation, the learning process itself, the classification and detection process, and the network testing process. At the same time, this network will be expanded with a new prediction and classification. Specifically the damage caused by eczema, psoriasis, pressure and moisture. The improved convolutional neural network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. At the same time, the type of disease or imprint damage is classified during detection. Synthetic fingerprints are used in network training and are supplemented by real fingerprints.
fingerprint, damage and detection, convolutional neural networks, dyshidrosis, warts, PyTorch, Keras, Python, Metacentrum, pressure, psoriasis
Drábek Vladimír, doc. Ing., CSc. (DCSY FIT BUT), člen
Očenášek Pavel, Mgr. Ing., Ph.D. (DIFS FIT BUT), člen
Rogalewicz Adam, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Smrž Pavel, doc. RNDr., Ph.D. (DCGM FIT BUT), člen
@mastersthesis{FITMT25222,
author = "Peter Vican",
type = "Master's thesis",
title = "Detekce a klasifikace po\v{s}kozen\'{i} otisku prstu s vyu\v{z}it\'{i}m neuronov\'{y}ch s\'{i}t\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "slovak",
url = "https://www.fit.vut.cz/study/thesis/25222/"
}