Erdoğmuş, Nesli
Loading...
Profile URL
Name Variants
Erdogmus, Nesli
Erdogmus, N
Erdogmus, N.
Erdoğmuş, N
Erdoğmuş, N.
Erdogmus, N
Erdogmus, N.
Erdoğmuş, N
Erdoğmuş, N.
Job Title
Email Address
neslierdogmus@iyte.edu.tr
Main Affiliation
03.04. Department of Computer Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
Research Products
3GOOD HEALTH AND WELL-BEING
0
Research Products
4QUALITY EDUCATION
0
Research Products
5GENDER EQUALITY
0
Research Products
6CLEAN WATER AND SANITATION
0
Research Products
7AFFORDABLE AND CLEAN ENERGY
0
Research Products
8DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
5
Research Products
10REDUCED INEQUALITIES
0
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
13CLIMATE ACTION
0
Research Products
14LIFE BELOW WATER
0
Research Products
15LIFE ON LAND
0
Research Products
16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
17PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents
28
Citations
800
h-index
8
This researcher does not have a WoS ID.
Scholarly Output
17
Articles
4
Views / Downloads
16390/8273
Supervised MSc Theses
6
Supervised PhD Theses
0
WoS Citation Count
7
Scopus Citation Count
15
Patents
0
Projects
5
WoS Citations per Publication
0.41
Scopus Citations per Publication
0.88
Open Access Source
15
Supervised Theses
6
| Journal | Count |
|---|---|
| Turkish Journal of Electrical Engineering and Computer Sciences | 2 |
| 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings | 1 |
| 2019 International Conference of The Biometrics Special Interest Group (Biosig 2019) | 1 |
| 2023 31st Signal Processing and Communications Applications Conference, Siu | 1 |
| 16th IEEE International Workshop on Multimedia Signal Processing | 1 |
Current Page: 1 / 2
Scopus Quartile Distribution
Competency Cloud
17 results
Scholarly Output Search Results
Now showing 1 - 10 of 17
Conference Object Citation - WoS: 1Citation - Scopus: 6Within- and Cross- Database Evaluations for Face Gender Classification Via Befit Protocols(Institute of Electrical and Electronics Engineers Inc., 2014) Erdoğmuş, Nesli; Vanoni, Matthias; Marcel, SebastienWith its wide range of applicability, gender classification is an important task in face image analysis and it has drawn a great interest from the pattern recognition community. In this paper, we aim to deal with this problem using Local Binary Pattern Histogram Sequences as feature vectors in general. Differently from what has been done in similar studies, the algorithm parameters used in cropping and feature extraction steps are selected after an extensive grid search using BANCA and MOBIO databases. The final system which is evaluated on FERET, MORPH-II and LFW with gender balanced and imbalanced training sets is shown to achieve commensurate and better results compared to other state-of-the-art performances on those databases. The system is additionally tested for cross-database training in order to assess its accuracy in real world conditions. For LFW and MORPH-II, BeFIT protocols are used. © 2014 IEEE.Master Thesis Synthetic Generation of Fingerprints(Izmir Institute of Technology, 2020) İrtem, Emre; Erdoğmuş, NesliFingerprints 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, NesliFingerprints 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.Publication Parmak İzlerinde Yönelim Kestirimi için \"sınıflandırma ile Regresyon\"(2023) Erdoğmuş, NesliParmak izi yönelim bilgisi, papil biçimini daha görünür kılma, öznitelik çıkarma, örüntü tipi sınıflandırma, dizinleme ve eşleştirme işlemleri için kritik role sahiptir ve bu sebeple parmak izi kimliklendirmede önemli bir rol oynar. Bu projede, parmak izi yönelimlerini daha düşük hata ile ve daha kısa sürede kestirebilen bir derin sinir ağı elde etmek için, hangi ayrıklaştırma, sınıf etiketleme, kodlama yöntemlerinin, farklı görevler için hangi kayıp fonksiyonlarının ve ne tür bir ağ modelinin seçilmesi gerektiği ve bu ağın eğitimi için elimizde bulunan veri kümesinin nasıl genişletilebileceği sorularına cevap aranmıştır. Buna yönelik olarak öncelikle tam evrişimli bir sinir ağı modeli eşit uzunluk, eşit olasılık ve K-ortalama tabanlı 3 farklı ayrıklaştırma, tek-etkin, sırasal ve döngüsel 3 farklı kodlama, beklenen değer ve en yüksek olasılıklı sınıf dikkate alınmak üzere 2 farklı kod çözme ve 5 farklı sınıf sayısı ile buna bağlı ayrıklaştırma sayısı kullanılarak toplamda 44 deney tasarlanmıştır. Bu deneyler sonucu en iyi performans gösteren konfigürasyonlar için ise farklı eğitim kümesi genişletme yöntemleri hem aynı model hem de bir görü dönüştürücü kullanılarak test edilmiştir. Çalışmalar sonucunda FOE-TEST veri kümesinde iyi parmak izlerinde 5.34°, kötü parmak izlerinde 8.67° ortalama karesel hata değeri elde edilmiştir.Conference Object Citation - WoS: 2Citation - Scopus: 3Impact of Variations in Synthetic Training Data on Fingerprint Classification(IEEE, 2019) İrtem, Pelin; İrtem, Emre; Erdoğmuş, NesliCreating and labeling data can be extremely time consuming and labor intensive. For this reason, lack of sufficiently large datasets for training deep structures is often noted as a major obstacle and instead, synthetic data generation is proposed. With their high acquisition and labeling complexity, this also applies to fingerprints. In the literature, a number of synthetic fingerprint generation systems have been proposed, but mostly for algorithm evaluation purposes. In this paper, we aim to analyze the use of synthetic fingerprint data with different levels of degradation for training deep neural networks. Fingerprint classification problem is selected as a case-study and the experiments are conducted on a public domain database, NIST SD4. A positive correlation between the synthetic data variation and the classification rate is observed while achieving state-of-the-art results.Master Thesis Privacy-Preserving Rare Disease Analysis With Fully Homomorphic Encryption(01. Izmir Institute of Technology, 2023) Akkaya, Güliz; Erdoğmuş, Nesli; Akgün, MeteRare 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, NesliFingerprints 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.Book Multiforesee2021 Book of Abstracts(01. Izmir Institute of Technology, 2021) Erdoğmuş, NesliArticle Citation - WoS: 1Citation - Scopus: 1Prism: Privacy-Preserving Rare Disease Analysis Using Fully Homomorphic Encryption(Oxford Univ Press, 2025) Akkaya, Guliz; Erdogmus, Nesli; Akgun, MeteMotivation Rare diseases affect millions of people worldwide, yet their genomic foundations remain poorly understood due to limited patient data and strict privacy regulations, such as the General Data Protection Regulation (GDPR) (https://gdpr.eu/tag/gdpr/) in March 2025. These restrictions can hinder the collaborative analysis of genomic data necessary for uncovering disease-causing variants.Results We present PRISM, a novel privacy-preserving framework based on fully homomorphic encryption (FHE) that facilitates rare disease variant analysis across multiple institutions without exposing sensitive genomic information. To address the challenges of centralized trust, PRISM is built upon a Threshold FHE scheme. This approach decentralizes key management across participating institutions and ensures no single entity can unilaterally decrypt sensitive data. Our method filters disease-causing variants under recessive, dominant, and de novo inheritance models entirely on encrypted data. We propose two algorithmic variants: a multiplication-intensive (MUL-IN) approach and an addition-intensive (ADD-IN) approach. The ADD-IN algorithms minimize the number of costly multiplication operations, enabling up to a 17x improvement in runtime for recessive/dominant filtering and 22x for de novo filtering, compared to MUL-IN methods. While ADD-IN produces larger ciphertexts, efficient parallelization via SIMD and multithreading allows it to handle millions of variants in reasonable time. To the best of our knowledge, this is the first study that utilizes FHE for privacy-preserving rare disease analysis across multiple inheritance models, demonstrating its practicality and scalability in a single-cloud setting.Availability and implementation The source code and the data used in this work can be found in https://github.com/mdppml/PRISM.git.Conference Object Citation - WoS: 1Citation - Scopus: 2Scene text localization using keypoints(Institute of Electrical and Electronics Engineers Inc., 2015) Erdoğmuş, Nesli; Özuysal, MustafaScene text localization and recognition (also known as text localization and recognition in real-world images, nature scene OCR or text-in-the-wild problem) is an open problem, attracting increasing interest from researchers. In this paper, we address the localization issue and leave the recognition part out of its scope. For the purpose of scene text localization, Scale-Invariant Feature Transform (SIFT) keypoints are extracted from the images and classified as text and non-text. Subsequently, the text keypoints are utilized to compute the bounding boxes around text regions. The proposed technique is tested on the database of ICDAR 2013 Robust Reading Competition-Challenge 2 and the experimental results are reported in detail. Although the idea introduced here is still at its infancy, it is observed to achieve remarkable results and due to the fact that there is a large room for improvement, it is found to be promising.