Biometric Retina Identification Based on Neural Network

Sadıkoğlu, Fahreddin; Uzelaltinbulat, Selin

doi:10.1016/j.procs.2016.09.365

Cited by 54 publications

(29 citation statements)

References 7 publications

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“…Here, many successful applications have been known to the community for more than a decade. Examples could be found in [228][229][230] where conventional factors, such as iris, retina, fingerprints, etc., are considered. Utilizing neural networks for the next-generation biometrics is the most likely way to proceed due to presently high levels of the analysis complexity [231,232].…”

Section: Discussion and Future Prospectsmentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir's Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.

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Section: Discussion and Future Prospectsmentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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“…There are also approaches connected with retina-based human identification. In [37] an interesting algorithm was proposed. However, there is no detailed information in it about the used solutions and how the system would react when the retina contains pathological changes.…”

Section: Discussionmentioning

confidence: 99%

A Novelty Approach to Retina Diagnosing Using Biometric Techniques With SVM and Clustering Algorithms

et al. 2020

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A method to extract the retina characteristic points for the purpose of medical diagnosis of the human eye is presented in this research. The proposed method helps to make the primary decision about the illness faster and can be used on mobile devices. The algorithm is mostly based on the characteristic points (the so-called minutiae). These structures are commonly used in the biometric applications for fingerprintbased people recognition. In the case of the conducted research, this trait was used to differentiate healthy eyes from unhealthy ones. The methods were evaluated by appropriately implemented algorithms, showing promising results. Each solution was created with object-oriented programming language. The accuracy of the classification (healthy versus samples with pathological changes) was evaluated using four algorithms: k-Nearest Neighbors, k-Means and Support Vector Machines (SVM) with linear and third-degree polynomial as well as our own approach based on counting the minutiae number. Performance requirements were also checked, and it was verified that the computing power of modern mobile devices is sufficient to implement the proposed solution. The highest accuracy result was equal to 96,45% and was obtained with the third-degree polynomial SVM. This was a novel approach. For comparative purposes, we also implemented currently used solutions for image analysis-deep learning (DL) and Convolution Neural Networks (CNNs). Both medical and computer science backgrounds are presented in the work with the main methodology components to include image segmentation using the Gaussian Matched Filter, binarization by Local Entropy Thresholding and classification with the previously mentioned approaches.

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“…It maps vascular pattern on the retinal portion of the eyeball. Sadikoglu and Uzelaltinbulat [21] used the feature vector of the segmented image with a neural network. The neural network is trained by backpropagation.…”

Section: Related Workmentioning

confidence: 99%

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

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Biometric Retina Identification Based on Neural Network

Cited by 54 publications

References 7 publications

Multi-Factor Authentication: A Survey

Multi-Factor Authentication: A Survey

A Novelty Approach to Retina Diagnosing Using Biometric Techniques With SVM and Clustering Algorithms

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

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