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- BildiriYalnız künye2026Analysis 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.
- Yüksek Lisans TeziAçık Erişim2025Evrişimli sinir ağları tabanlı süper çözünürlük yöntemleri ile avuç izi görüntü kalitesinin iyileştirilmesi
Hüseyin Furkan Macan
Bu tez çalışması, avuç izi tanıma sistemlerinde kullanılan biyometrik görüntülerin kalitesini iyileştirmek amacıyla, derin öğrenme tabanlı süper-çözünürlük yöntemlerinin performansını incelemektedir. Literatürde yaygın olarak kullanılan GAN, DCGAN, BGAN, BEGAN ve özellikle çekişmeli otokodlayıcı (AAE) mimarileri değerlendirilmiş; elde edilen süper-çözünürlüklü çıktılar hem PSNR gibi referanslı hem de BRISQUE gibi referanssız metriklerle analiz edilmiştir. Yapılan deneylerde, AAE modeli en başarılı sonuçları verdiğinden, geliştirme süreci bu yapı üzerine odaklanmıştır. Modelin kod çözücü kısmı konvolüsyonel katmanlarla yeniden tasarlanarak 2 kat çözünürlük artırımı sağlayacak şekilde optimize edilmiş, IIT Delhi avuç içi veri seti ile eğitilmiştir. Görüntü kalitesi iyileştirme sürecinin yalnızca görsel kaliteyi değil, aynı zamanda biyometrik tanıma doğruluğunu da artırdığı yapılan testlerle ortaya konmuştur.
- BildiriYalnız künye2025Depth 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.
AtıfScopus1OpenAlex1İncele - BildiriAçık Erişim2024High-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.
- BildiriYayıncıda açık erişim2024High 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.
AtıfScopus2OpenAlex1İncele - MakaleYalnız künye2017Scopus SJRQ1Stereo-based palmprint recognition in various 3D postures
Bingöl, Özkan; Ekinci, Murat
In order to increase performance in palmprint recognition systems, various devices are normally used to restrict the movement of the hand. These can cause problems, especially for those users with physical disabilities. They also cause significant hygiene problems in multi-user systems. Recently, studies on palmprint recognition systems have progressed towards the development of unconstrained, contactless and unrestricted background techniques. The most common problem encountered in these studies is the alignment arising from the free movement of the hand. Despite 3D hand-acquisition devices which offer extra recognition features to overcome this problem, the applicability of these devices is low because of their increased cost. In this study, a stereo camera was proposed. Although due to matching problems, it is difficult to achieve precise, distinct feature extraction in the unrestricted 3D environment used for palmprint recognition, the orientation of the hand in 3D space can be determined by obtaining depth information. In this study, the depth information was extracted by using the binocular stereo approach. First, the orientation of the hand was estimated by fitting a surface model associated with the eigenvectors of the depth information. Pose correction was then accomplished by establishing a relationship between the orientation and the images. The pose correction greatly relieved the perspective distortion that usually occurs within the various poses of the hands. Next, the region of interest was determined by performing segmentation on the corrected images using the Active Appearance Model (AAM). The palmprint features were then extracted via Gabor-based Kernel Fisher Discriminant Analysis. In order to demonstrate the performance of the proposed approach, a new dataset was compiled from stereo images within various scenarios collected from 138 different individuals. As a result of these experimental studies, the EER values, especially on the images captured from different hand orientations in 3D, were reduced from around 14–0.75%. With the help of this suggested approach, the palmprint recognition system was transformed into a more portable form by removing the closed-box mechanisms and equipment restricting movement of the hand. This system can automatically perform pose estimation, hand segmentation and recognition processes without any special intervention.
AtıfScopus34WoS24İncele
