Biometric recognition of conjunctival vasculature using GLCM features

Tankasala, Sriram Pavan; Doynov, Plamen; Derakhshani, Reza; Ross, Arun; Crihalmeanu, Simona

doi:10.1109/iciip.2011.6108974

Cited by 27 publications

(24 citation statements)

References 17 publications

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“…In [35] match score level fusion of Fisher LDA and neural networks are used which provides the best results in classification.…”

Section: Classificationmentioning

confidence: 99%

“…In [14], [35], [37], [40] adaptive histogram equalization was applied to the green layer of the colour image in order to get enhanced sclera vain pattern. In [40] for image registration a local affine and a global smooth transformation was applied.…”

Section: B Sclera Vessel Enhancement and Image Registrationmentioning

confidence: 99%

“…[35] GLCM (Grey Level Cooccurrence Matrix) was used for sclera biometrics. Statistical features of GLCM, such as contrast, correlation, energy and homogeneity, were used in conjunction with Fisher linear discriminant analysis.…”

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Sclera Recognition - A Survey

Das

Pal

Blumenstein

et al. 2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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“…In [35] match score level fusion of Fisher LDA and neural networks are used which provides the best results in classification.…”

Section: Classificationmentioning

confidence: 99%

Section: B Sclera Vessel Enhancement and Image Registrationmentioning

confidence: 99%

See 1 more Smart Citation

Sclera Recognition - A Survey

Das

Pal

Blumenstein

et al. 2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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show abstract

“…[40] GLCM (Grey Level Cooccurrence Matrix) was used for sclera biometrics. The authors in [31] presented four fusion methods for combining recognition results from multi-angle images.…”

Section: Feature Extractionmentioning

confidence: 99%

“…In [40] match score level fusion of Fisher LDA and neural networks are used which provides the best results in classification.…”

Section: Classificationmentioning

confidence: 99%

Survey Paper on Sclera Segmentations Techniques

Rajole¹,

Shinde²

2016

International Journal of Advanced Research in Computer and Comm

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Due to extensive growth of internet, security of the internet is area of interest for the researchers. An efficient biometric trait these days are sclera blood vessels. Sclera is a white region in the eye, around the eyeball which contains blood vessel patterns that can be used for personal identification. In this paper we present the existing sclera segmentation methods and their working in with their limitations and ideas to enhance them.

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Deep Sclera Segmentation and Recognition

Rot

Vitek

Grm

et al. 2019

Handbook of Vascular Biometrics

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In this chapter, we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around Convolutional Neural Networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: (i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and (ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets come with a rich P. Rot and M. Vitek are first authors with equal contributions.

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Biometric recognition of conjunctival vasculature using GLCM features

Cited by 27 publications

References 17 publications

Sclera Recognition - A Survey

Sclera Recognition - A Survey

Survey Paper on Sclera Segmentations Techniques

Deep Sclera Segmentation and Recognition

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