Sürdürülebilir Kalkınma Amaçları

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• BildiriYalnız künye2026/r/gumushane/13691
  Analysis of Temporal Consistency in Video-Based Palmprint ROI Tracking

  Sunnetci, Betul Kosmaz; Bingol, Ozkan; Gedikli, Eyup

  In real-time palmprint recognition systems, operating under unconstrained conditions, maintaining the defined palmprint region of interest (ROI) throughout hand motion presents a significant challenge. Variations in hand position or orientation may cause the ROI to shift unexpectedly or partially or completely disappear, which can adversely affect subsequent processing steps and their computational cost. In this study, the temporal stability and consistency of palmprint ROIs extracted from video data in the MPW-180 dataset were evaluated using two different approaches. Specifically, the behavior of two methods (optical flow based tracking and keypoint-based MediaPipe Hands detection) was quantitatively analyzed over time using corner position shifts and overlap ratios. Experimental results indicate that although the keypointbased method produces spatially accurate ROI estimates, it may exhibit sudden positional jumps in certain frames. In contrast, the optical flow method provides smoother motion transitions but may introduce positional drift over time due to the accumulation of tracking errors.

  İncele
• MakaleAçık Erişim2025Scopus SJRQ2/r/gumushane/14251
  A Video-Based Mobile Palmprint Dataset and an Illumination-Robust Deep Learning Architecture for Unconstrained Environments

  Koşmaz Sünnetci, Betül; Bingöl, Özkan; Gedikli, Eyüp; Ekinci, Murat; Doğan, Ramazan Özgür; Türk, Salih; Güngör, Nihan

  Featured Application: The proposed framework facilitates secure and user-friendly biometric authentication on mobile platforms, particularly in mobile payment and access control scenarios, under unconstrained conditions. The widespread adoption of mobile devices has made secure and user-friendly biometric authentication critical. However, widely used modalities such as fingerprint and facial recognition show limited robustness under uncontrolled illumination and on heterogeneous devices. In contrast, palmprint recognition offers strong potential because of its rich textural patterns and high discriminative power. This study addresses the limitations of laboratory-based datasets that fail to capture real-world challenges. We introduce MPW-180, a novel dataset comprising videos of 180 participants recorded on their own smartphones in everyday environments. By systematically incorporating diverse illumination conditions (with and without flash) and natural free-hand movements, MPW-180 is the first dataset to adopt a bring-your-own-device paradigm, providing a realistic benchmark for evaluating generalization in mobile biometric models. In addition, we propose PalmWildNet, an SE-block-enhanced deep learning architecture trained with Triplet Loss and a cross-illumination sampling strategy. The experimental results show that conventional methods suffer over 50% performance degradation under cross-illumination conditions. In contrast, our method reduces the Equal Error Rate to 1–2% while maintaining an accuracy above 97%. These findings demonstrate that the proposed framework not only tolerates illumination variability but also learns robust illumination-invariant representations, making it well-suited for mobile biometric authentication.

  AtıfScopus1

  İncele
• BildiriYalnız künye2025/r/gumushane/14242
  Depth Estimation Based Automatic Pose Correction in Hand Images

  Sünnetci Koşmaz, Betül; Bingöl, Özkan; Gedikli, Eyüp; Ekinci, Murat

  In palm biometric recognition systems, the hand being at different angles and positions relative to the camera presents a critical challenge in terms of accuracy and reliability. Especially in images captured in free environments, hand misalignment can negatively affect recognition performance by making it difficult to extract biometric attributes. Perspective distortions due to camera position change the geometric structure of the hand, compromising biometric data integrity. While 3D imaging systems can offer successful solutions for pose normalization, performing a similar transformation for 2D images poses a major technical challenge. In this study, depth estimation supported plane fitting is used to remove perspective distortions in 2D images and pose normalization of the hand is obtained. The proposed method uses the information obtained from depth estimation to realign the hand image as if it were standing directly in front of the camera, improving geometric consistency. This will make the extraction of biometric features more reliable and contribute to improving the accuracy of palm recognition systems.

  AtıfScopus1·OpenAlex1

  İncele
• BildiriAçık Erişim2024/r/gumushane/16231
  Cross-view Gait Recognition Based on Fine-Tuned Deep Networks

  Yaprak, Busranur; Gedikli, Eyup; Bingöl, Ozkan; Dogan, Ramazan Ozgur

  Gait recognition is a biometrics-based computer vision process used to identify people based on their walking styles. Compared to other types of biometrics, gait offers a more advantageous recognition process as it does not require high-resolution and close-range images and obtains without contact. But besides this, gait biometrics is highly affected by cross-view variation, and under this variation recognition performance decreases significantly. In this study, performance evaluations of fine-tuned VGG-16 and ResNet-50 deep CNN networks on the cross-view gait recognition problem are performed. For this purpose, Gait energy images (GEI) and Silhouettes obtained from CASIA-B, the most comprehensive data set in gait recognition, are given as input to the networks. The experimental results showed that the VGG-16 network achieved higher recognition rates in cross-view gait recognition.

  İncele
• BildiriAçık Erişim2024/r/gumushane/14518
  High-Accuracy Hand Segmentation for Palmprint Biometric Systems Improving Auto-Encoder Techniques

  Yalcin, Kadir; Dogan, Ramazan Ozgur; Dogan, Hulya; Bingol, Ozkan

  Biometric technologies have been widely adopted in various commercial products, ranging from security systems to personal devices, due to their exceptional reliability and user-friendliness. Among these, palmprint biometrics has gained increasing attention for its advantages, such as ease of use, a larger surface area, and the ability to capture more features compared to other biometric methods. A typical palmprint biometric system involves five key stages: (a) Image Acquisition, (b) Hand Segmentation, (c) Pattern Generation, (d) Feature Extraction, and (e) Verification/Identification. The overall success of the system depends on the effectiveness of each stage, with hand segmentation being crucial for identifying the most relevant features for accurate verification. This study aims to achieve high-accuracy biometric recognition by segmenting hands from images captured in uncontrolled environments with complex backgrounds. Accuracy (ACC), Intersection over Union (IoU), F1-Score, and Dice Coefficient are the four critical metrics used to assess the performance of segmentation techniques. The findings demonstrate that the U-Net + ViT and MA-Net + ResNet34 techniques outperform traditional auto-encoder methods in both quantitative performance metrics and visual analysis, leading to significantly improved hand segmentation for biometric systems.

  İncele
• BildiriAçık Erişim2024/r/gumushane/12679
  Super-Resolution Based ROI Enhancement Approaches for Palmprint Recognition; [Avu Izi Tanima I in S per z n rl k Tabanli ROI Iyilestirme Yaklasimlari]

  MacAn, Huseyin Furkan; Bingol, Ozkan; Turk, Salih; Ekinci, Murat

  The sizes of regions of interest (ROI) obtained from images captured by cameras in palmprint recognition may vary depending on the camera specifications and the position of the hand. The regions of interest that recognition systems can use are typically rescaled through interpolation techniques and then defined as inputs to the system. In such cases, there may be some loss of biometric data. This study aims to enhance the quality of the palmprint ROI images, which are critical for palmprint recognition systems. To achieve this, various super-resolution techniques were employed to reconstruct low-resolution ROI images to the target sizes while preserving the distinctiveness of their biometric features. This approach is designed to optimize recognition accuracy and improve data reliability in palmprint recognition processes.

  AtıfScopus4·OpenAlex3

  İncele
• MakaleAçık Erişim2024Scopus SJRQ4/r/gumushane/12439
  Analysis of grover's quantum search algorithm on a classical computer: Identifying opportunities for improvement

  Necati, Çeli̇K; Bi̇Ngöl, Özkan

  In this paper, Grover’s quantum search algorithm is analyzed using a classical computer by calculating the amplitudes and the probabilities of finding a single marked state for n=5, 10, 15, 20, 25, and 27 qubit states. The calculations show that the marked state can be found in iterations, where N = 2n is the number of items. The possibility of improving Grover’s search algorithm to find a single item in N search elements is discussed by calculating the amplitudes and hence the probabilities of finding a single marked state for n=5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 qubit states. The calculations showed that the marked state could be found with sufficiently high probability in (ln(N)) iterations. This is quite a remarkable speed-up that can be achieved to find a single marked element in an unsorted N search element. Copyright 2021, Yıldız Technical University.

  AtıfScopus2·WoS1·OpenAlex5

  İncele
• MakaleAçık Erişim2024Scopus SJRQ1/r/gumushane/11731
  The impact of implementing backbone architectures on fracture segmentation in X-ray images

  Turk, Salih; Bingol, Ozkan; Coskuncay, Ahmet; Aydin, Tolga

  X-ray imaging is widely utilized for the detection of bone fractures due to its affordability, rapid processing capabilities, broad accessibility, and ease of use. Despite these advantages, the intricate analysis of X-ray images necessitates advanced computational techniques to fully exploit their rich informational content. Notably, accurate segmentation of these images plays a critical role in aiding medical professionals with precise diagnoses and effective treatment planning. This study examines the impact of integrating different backbone architectures for the task of fracture segmentation in X-ray images. Specifically, the research focuses on enhancing the widely-used DeepLabV3 model by incorporating pre-trained networks such as ResNet50, ResNet101, and MobileNetV3 into the encoder component to improve feature extraction and segmentation accuracy. The FracAtlas dataset, which presents unique challenges due to its small size and the diversity of fractures from various anatomical regions, was employed for model evaluation. Data augmentation techniques were implemented to expand the dataset, and an additional subset focusing on cropped images of fracture areas was developed. The models were trained over 50 epochs, and their performance was assessed using metrics such as Intersection over Union (IoU), loss values, and Dice scores. The results indicate that the DeepLabV3 model with ResNet-based backbones achieved IoU values exceeding 0.93 on the original dataset and demonstrated outstanding performance on the augmented and cropped datasets, with AUC values reaching up to 0.99. The study also highlights the computational complexity of the models, with ResNet101 exhibiting the highest time complexity, while MobileNetV3 was the most efficient in terms of processing time and memory consumption.

  AtıfScopus8·WoS6·OpenAlex6

  İncele
• BildiriYayıncıda açık erişim2024/r/gumushane/11473
  High Performance Automatic ROI Extraction from Hand Images with Convolutional Neural Networks

  Kosmaz Sunnetci, Betul; Bingol, Ozkan; Gedikli, Eyup; Ekinci, Murat

  Automatic ROI extraction from hand images is a critical step in palmprint recognition. Traditional methods involve steps such as identifying the region of the hand, finding the valleys between the fingers and marking the palm area. Some of these processes are performed manually and some are performed with image processing techniques. However, these methods are time-consuming, vulnerable to human error, and computationally redundant. In this study, a deep learning-based convolutional neural network is developed for high-performance automatic ROI extraction from hand images. The proposed method eliminates the hand segmentation process and provides a solution where the palm region can be automatically selected directly. This reduces processing time, minimizes human error and improves overall accuracy.

  AtıfScopus2·OpenAlex1

  İncele
• MakaleAçık Erişim2023Scopus SJRQ4/r/gumushane/11455
  Detection of pneumonia from pediatric chest X-ray images by transfer learning

  Demir, Yasin; Bingöl, Özkan

  When pathogens such as viruses, bacteria and fungi attack the lungs, the alveoli fill with inflamed fluid, causing pneumonia. Early diagnosis of this disease, which has fatal outcomes especially in children under 5 years old, is very important in controlling undesirable situations. Chest X-ray images play an important role in the diagnosis of pneumonia. In addition, the fact that the amount of radiation is lower than imaging devices such as tomography and the possibility of being accessible even from rural areas creates an advantage for X-ray devices. However, X-ray images that are not always clear or human conditions such as fatigue and lack of attention can make it difficult for specialists to detect pneumonia. In this study, a transfer learning-based convolutional neural network (CNN) approach is proposed, which can help specialists in the early and accurate diagnosis of pneumonia in children and, classify healthy and diseased individuals through Chest X-ray images. As a result of the study, an original CNN was proposed by adding additional layers to the AlexNet architecture layers and a test accuracy of 96.31% was obtained. Copyright 2021, Yıldız Technical University.

  AtıfScopus9·WoS4

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