Identity verification based on vessel matching from fundus images

Oinonen, Hannu; Forsvik, Heikki; Ruusuvuori, Pekka; Yli‐Harja, Olli; Voipio, Ville; Huttunen, Heikki

doi:10.1109/icip.2010.5650657

Cited by 34 publications

(25 citation statements)

References 7 publications

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“…The similarity measure used by [10,11,13] as a retina feature point matching score is c/ VTJ', where f and l' are the numbers of feature points in two images and c is the number of feature points matched between them. In terms of retina graphs this is a normalised node match score.…”

Section: Our Approachmentioning

confidence: 99%

“…Vessel cali bre was added in [6] as an additional feature to the template described in [5]. Templates based on feature points (posi tions of vessel branchings and crossovers) have been tested recently in [7,8,9,10,11,12] and the effect of matching 1 such features in fovea-centred versus optical disc-centred images has been studied in [13]. In this last paper, the ori entation of the principal branch at a vessel branch point is included for improved matching robustness.…”

Section: Introductionmentioning

confidence: 98%

“…The VARIA database [17] is the largest currently available publicly (139 individuals, 233 images, mostly collected by 2006). The studies [10, ]], ] 2, 13] use it and [13] also uses a database of subjects with diabetes.…”

Section: Introductionmentioning

confidence: 99%

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Retina features based on vessel graph substructures

Arakala

Davis

Horadam

2011

2011 International Joint Conference on Biometrics (IJCB)

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We represent the retina vessel pattern as a spatial re lational graph, and match features using error-correcting graph matching. We study the distinctiveness of the nodes (branching and crossing points) compared with that of the edges and other substructures (nodes of degree k, paths of length k). On a training set from the VARIA database, we show that as well as nodes, three other types of graph sub structure completely or almost completely separate genuine from imposter comparisons. We show that combining nodes and edges can improve the separation distance. We identify two retina graph statistics, the edge-to-node ratio and the variance of the degree distribution, that have low correla tion with node match score.

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Section: Our Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Retina features based on vessel graph substructures

Arakala

Davis

Horadam

2011

2011 International Joint Conference on Biometrics (IJCB)

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“…However, these approaches may suffer from the following drawbacks: (1) the extracted vasculature information is usually incomplete due to the convergence of multiple and various bent vessels; (2) injuries or diseases can alter the vascular features and lead to improper segmentation; (3) visible micro vascular cannot be extracted efficiently. To address these problems, the minutiae-based retinal identification system (MBRIS) [23–25] was proposed and in recent years it has been considered to be ideal and robust. In a typical MBRIS, a set of landmarks (bifurcations [26] and crossovers of the retinal vessel tree) are extracted and used as feature points.…”

Section: Related Workmentioning

confidence: 99%

Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform

Meng

Yin

Yang

et al. 2013

Sensors

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Abstract:Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT), which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF) is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.

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“…Oinonen et al [18] proposed a novel method for verification based on minutiae features. The proposed method consisted of three steps: blood vessel segmentation, feature extraction, and feature matching.…”

Section: Review Of the Last Proposed Methodsmentioning

confidence: 99%

Human recognition based on retinal images and using new similarity function

et al. 2013

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This paper presents a new human recognition method based on features extracted from retinal images. The proposed method is composed of some steps including feature extraction, phase correlation technique, and feature matching for recognition. In the proposed method, Harris corner detector is used for feature extraction. Then, phase correlation technique is applied to estimate the rotation angle of head or eye movement in front of a retina fundus camera. Finally, a new similarity function is used to compute the similarity between features of different retina images. Experimental results on a database, including 480 retinal images obtained from 40 subjects of DRIVE dataset and 40 subjects from STARE dataset, demonstrated an average true recognition accuracy rate equal to 100% for the proposed method. The success rate and number of images used in the proposed method show the effectiveness of the proposed method in comparison to the counterpart methods.

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Identity verification based on vessel matching from fundus images

Cited by 34 publications

References 7 publications

Retina features based on vessel graph substructures

Retina features based on vessel graph substructures

Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform

Human recognition based on retinal images and using new similarity function

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