Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Erdoğmuş, Nesli

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Now showing 1 - 6 of 6
  • Master Thesis
    Privacy-Preserving Rare Disease Analysis With Fully Homomorphic Encryption
    (01. Izmir Institute of Technology, 2023) Akkaya, Güliz; Erdoğmuş, Nesli; Akgün, Mete
    Rare diseases severely affect many people across the world at the present time. Researchers conduct studies to understand the reasons behind rare diseases and as a result of this research, diagnosis, and treatment methods are developed. Rare disease analysis is performed to specify the disease-causing variants on the genome data of patients. The researchers need access to as much genome data as possible to find causing variants of rare diseases. On the other hand, the genome data of patients should be protected because it can be used to detect the identity of individuals. The researchers are not able to share the genome data of patients easily because of regulations such as General Data Protection Regulation (GDPR). For this reason, rare disease analysis should be performed in a secure way that protects the privacy of patients while enabling the collaboration of multiple medical institutions. In this context, a privacy-preserving collaborative system for rare disease analysis should be provided. This thesis study focuses on the utilization of fully homomorphic encryption, a method that enables unlimited number of operations to be performed on encrypted data, for privacy-preserving collaborative rare disease analysis. Two different methods, the boolean circuit method, and the integer arithmetic method, are implemented to perform rare disease analysis on the encrypted genome data to find disease-causing variants, and various experiments are performed to assess the efficiency of the proposed methods.
  • Master Thesis
    Analysis of fingerprint matching performance with deep neural networks
    (01. Izmir Institute of Technology, 2022) Göçen, Alper; Erdoğmuş, Nesli
    Fingerprints are unique biometric properties for each person. In the literature and industry, they are widely used for identification purposes. Collecting biometric datasets is a tedious work since it is not possible without the owners’ consent, and existing fingerprint datasets are either not sufficient to use in deep learning tasks by means of size or most of them are kept private to the collectors’ use. This increases the need of synthetic fingerprint images and their use in a variety of tasks especially for training deep learning models. In this study, the performance of a CNN architecture named Finger ConvNet[1] is compared to well-known networks and the question of whether a mixed dataset consisting of synthetically generated and real fingerprint images can reach a performance close or equal to ones having only real images is discussed. As a result of experiments, it is shown that the number of real images in the dataset is an important factor and that the performance of the mixed dataset was less than the one having only real images proposed in the referred study.
  • Master Thesis
    Synthetic Fingerprint Generation With Gans
    (01. Izmir Institute of Technology, 2021) Kılınç, Vahdettin Onur; Erdoğmuş, Nesli
    Fingerprints regarded as the most reliable form of human identification for thousands of years. Even though the fingerprint collection process became more convenient with technological advancements, privacy concerns slowed down the researchers working on fingerprint biometrics. Like every other problem solved with deep learning, biometrics requires a sizable database to succeed. This study generates synthetic fingerprints to tackle bottlenecks created by privacy laws. First, the pipeline designed enhances images from a small publicly available fingerprint dataset. The new enhanced dataset is given as an input to a generative network to create candidate synthetic fingerprints. Fingerprint image quality models choose high-quality fingerprint images from the candidate set to form the synthetic fingerprint dataset. Numerous experiments were conducted to show the quality of the generated synthetics fingerprints using both real and synthetic fingerprint datasets available.Experimental results show that enhancing fingerprint images from real-life datasets helps integrate synthetic fingerprint images into real life. Synthetic fingerprints generated from the pipeline can generate large datasets with a representative quality close to their real-life counterparts without privacy concerns.
  • Master Thesis
    Synthetic Generation of Fingerprints
    (Izmir Institute of Technology, 2020) İrtem, Emre; Erdoğmuş, Nesli
    Fingerprints are unique to each person and they have been widely used and accepted for identification purposes by the society. Fingerprints can be captured by using ink and paper to get a print and then digitizing it or more recently by using specialized sensors. But in both cases, trained specialist supervision is mostly needed. Moreover, since fingerprints are personal information, they are protected by the laws on personal data protection. Therefore, collection/sharing of real fingerprints is difficult and illegal without the consent of their owner. On the otherhand, deep learning systems that are proven to be very successfull in many machine learning task, usually depend on very large training sets to achive high accuracies. In this study, to overcome the data hunger problem for training deep neural networks, synthetic fingerprints are generated by using model-based methods. For this purpose, firstly master fingerprint images are generated and next many impressions are derived from them by applying real-world degradations. The realism and the usability of synthetic fingerprints are tried and validated using a fingerprint classification system. For which, a deep neural networks are trained with and without the synthetically generated data. As a result of the experiments, it is shown that the generated fingerprint images are realistic enough to positively effect the classification results and that the usage of the synthetically generated fingerprints in training deep systems are promising.
  • Master Thesis
    Deep Learning in Fingerprint Analysis
    (Izmir Institute of Technology, 2020) İrtem, Pelin; Erdoğmuş, Nesli; Erdoğmuş, Nesli
    Fingerprints are one of the most widely used personal identification traits. They play a crucial role in forensics because they are considered to be unique to each person. For many years, the identification of individuals had been carried out by human operators. However, with technological developments, automated fingerprint recognition systems have arisen, and the growth in the population has increased the importance of their robustness. On the other hand, deep learning has led to many impressive developments in the area of computer vision. Fingerprint analysis is indeed in the scope of image processing and computer vision; however, the usage of deep learning in fingerprint analysis is rather limited. This study focuses on using deep learning techniques on two different stages of the automated fingerprint recognition pipeline: Fingerprint classification and fingerprint minutiae extraction. Deep learning systems are developed for those two selected stages and analysed with respect to several aspects such as dataset size and different network architectures.
  • Master Thesis
    Robustness of Fingerprint Verification Algorithms Against Synthetic Deformations
    (Izmir Institute of Technology, 2019) Cantürk, Sinem; Erdoğmuş, Nesli
    Fingerprint recognition is one of the biometric techniques used for the identification of humans. The developments and research about fingerprint recognition to date are of great importance in advancing fingerprint recognition and verification scenarios. The fact that fingerprint recognition systems are used almost everywhere and are easily accessible is directly proportionate to a large amount of research in these areas. During the acquisition of the fingerprint, there are many environmental factors that may affect the quality of the print and eventually, its ability to be recognized. For a fingerprint recognition algorithms, it is important to handle the difficulties that arise due to those variations. The aim of the thesis is to obtain and compare the results of not only an existing feature-based fingerprint recognition techniques but a fingerprint recognition technique that uses deep learning. The main focus is on how fingerprint verification algorithms behave under the circumstances of synthetically distorted fingerprint images. After developing two different verification systems, the goal is to compare system results with and without distorted images. The results of the two methods with and without externally added deformations effect on the fingerprint image is compared. The first system has a feature-based approach comparing the images via local features on the fingerprint. In order to do this two different descriptors that are called ORB and SIFT are used. In the feature-based approach, there is also a matching part and this part is tried with two different matching algorithms that are called Brute Force Matcher and Approximate Nearest Neighbor (ANN) matcher. The second algorithm makes the decision of match or non-match by feeding the raw fingerprint images as an input to a deep neural network and comparing the feature vectors calculated by the network. This study has revealed that deep neural network approach has given more robust and faster results on both the original dataset and distorted versions of the dataset.