Titrek, Fatih

Profile URL:
Job Title:Dr. Arş. Gör.
Email Address:ftitrek@ktun.edu.tr
Main Affiliation:10.01. Department of Computer Engineering
Status: Current Staff
Website: https://www.ktun.edu.tr/tr/Universite/PersonelBilgi/?prsnl=U6CQEcG8UGQSvYD3Wgwka81XXqsLlEAGVTu0vfpExcqzFk6sIh2Z09S0qyYhaE55wcLl9EekLfnz7y5khPE27Q==
ORCID:ORCID Profile0000-0003-2113-9733
Scopus ID:Scopus Profile57218489194
YÖK Akademik: EE876959B145AC58
Google Scholar:Google Scholar Profile8XheDwMAAAAJ
Web of Science ID:Web of Science ProfileHPE-5413-2023
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Author of Publication: search.filters.isAuthorOfPublication.515bde6b-b781-4c73-9178-9d8265037032

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Finger Vein Recognition by Combining Anisotropic Diffusion and a New Feature Extraction Method
    (INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2020) Titrek, Fatih; Baykan, Ömer Kaan
    In recent years, Finger Vein (FV) Recognition System is frequently used where personal security is required. Image distortion caused by light scattering in the tissue is one of the major problems about the visibility of the FV. In this study, Homomorphic Filter and Anisotropic Diffusion are used for removing the light scattering problem in our captured FV image and to increase the visibility of the veined region. Novelty of the study is proposing two new features: Horizontal Total Proportion (HTP) and Vertical Total Proportion (VTP). These two new features were used together with both spatial and frequency domain features and it was observed that the success rates obtained by our attributes were significantly increased. Experimental results demonstrate that the proposed HTP and VTP features are effective and reliable to improve the classification success in FV recognition problem. According to the experiments, the use of Perona-Malik and Homomorphic Filter together has been shown to reduce the light scattering problem and improve vascular visibility by removing the noise in the finger vein image. In this study, four different classifiers are used: Complex Tree, Ensemble, Support Vector Machines (SVM), K-Nearest Neighbors (KNN). The best success rate was achieved by using the KNN classifier.
  • Article
    Finger Vein Recognition Based on Multi-Features Fusion
    (Int Information & Engineering Technology Assoc, 2023) Titrek, Fatih; Baykan, Ömer K.
    Biometric Recognition Systems allow individuals to be automatically authenticated or identified by using their unique characteristics. Finger vein (FV), widely used for this purpose, has a crucial place among biometric systems because of its advantages, which are user-friendliness, ability to detect living tissue, high reliability, low system cost, and less area requirement in installation. It has a wide usage area, especially in places where personal safety is at the forefront. In this study, we examine the effect of the Horizontal and Vertical Total Proportion (HVTP) feature extraction algorithm on the success rate when the fusion technique is applied. Homomorphic Filter (HF) and Perona-Malik Anisotropic Diffusion (PMAD) are used to remove the noise and light scattering issue in the FV databases, and Gray Level Run Length Matrices (GLRLM), Gray Level Co-occurrence Matrices (GLCM), Segmentation-based Fractal Texture Analysis (SFTA), Horizontal Total Proportion (HTP), and Vertical Total Proportion (VTP) methods are applied to describe texture features. The fusion of multiple features instead of using only one type of feature can improve the accuracy of FV recognition systems. The novelty of the study is the fusion of HTP and VTP with the GLRLM, GLCM, and SFTA features by using Yang finger vein databases (Database_1) and MMCBNU_6000 (Database_2). Experimental results reveal that the HTP and VTP significantly improved the classification success in these FV image databases. The best success rate achieved in the Ensemble classifier is 99.7% using Database_1 and 97.6% using Database_2.
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Traitement Du Signal1
TRAITEMENT DU SIGNAL1
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2

Articles

2

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2/8

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WoS Citation Count

4

Scopus Citation Count

4

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0

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0

WoS Citations per Publication

2.00

Scopus Citations per Publication

2.00

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1

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0

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