Odhad kvality snímků duhovky pro identifikaci osob

Language

Slovak

Abstract

Iris image recognition is one of the most accurate ways of biometric identification. Various verification errors can be caused if the biometric system receives poor input. By assessing the image quality it is possible to eliminate inputs causing such errors. There is a relatively insignificant development in the field of iris quality assessment and many methods that could potentially be used have not been tested in this area yet. This work focuses on different quality assessment methods used in face recognition. These quality assessment methods are then applied to the area of iris identification. The solution uses verification systems based on various iResNet and MobileNetV3 architectures. Selected quality assessment methods are applied to individual systems. Different quality assessment methods train either the system directly or use its outputs to obtain information about quality. The resulting system achieves a reduction of false non-match rate by up to 56% with the absolute value of 0.5% for iResNet50 and up to 22 \% with the absolute value of 6.4% for MobileNetV3 when using the best quality assessment method. The results are given for the data set University of Notre Dame Iris CrossSensor 2013 with an input reject rate of 10% and a false match rate of 0.1%.

Keywords

biometrics, CR-FIQA, MagFace, neural networks, iris image quality assessment, PyTorch, PFE, iris image recognition, SDD-FIQA, SER-FIQ, machine learning

Department

Department of Computer Graphics and Multimedia FIT BUT

Degree Programme

Information Technology and Artificial Intelligence, Specialization Intelligent Systems

Files

Status

defended, grade B

Date

20 June 2022

Reviewer

Herout Adam, prof. Ing., Ph.D.

Committee

Zbořil František V., doc. Ing., CSc. (DITS FIT BUT), předseda
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Kanich Ondřej, Ing., Ph.D. (DITS FIT BUT), člen
Peringer Petr, Dr. Ing. (DITS FIT BUT), člen
Smrž Pavel, doc. RNDr., Ph.D. (DCGM FIT BUT), člen

Citation

VAŠKO, Marek. Odhad kvality snímků duhovky pro identifikaci osob. Brno, 2022. Master's Thesis. Brno University of Technology, Faculty of Information Technology. 2022-06-20. Supervised by Hradiš Michal. Available from: https://www-dev.fit.vutbr.cz/study/thesis/24833/

BibTeX

@mastersthesis{FITMT24833,
    author = "Marek Va\v{s}ko",
    type = "Master's thesis",
    title = "Odhad kvality sn\'{i}mk\r{u} duhovky pro identifikaci osob",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2022,
    location = "Brno, CZ",
    language = "slovak",
    url = "https://www.fit.vut.cz/study/thesis/24833/"
}