A New Biometric Modality Based on Conjunctival Vasculature

Derakhshani, Reza; Ross, Arun; Crihalmeanu, Simona

doi:10.1115/1.802566.paper74

Cited by 47 publications

(14 citation statements)

References 11 publications

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“…From manual to semi-automated and lastly, it comes to fully automated. Previously study is manual based segmentation, it has been applied by [4] and [5] the study is an unreliable approach for real-time applications because of the human supervision required with the segmentation process and the expensive processing time. In the semi-automated method suggested by [6], [7] and [8] based on K-means clustering, the eyelids included in the resulting sclera image were manually corrected.…”

Section: Fig 1 Anatomy Of the Eyementioning

confidence: 99%

Interactive Growcut Multi-Label N-D Segmentation Based Algorithm for SCLERA Segmentation

Hellwig¹

2018

IJRASET

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This paper presents a computationally efficient sclera detection, segmentation and enhancement approach. The proposed sclera detection and segmentation approach is developed based on the cellular automaton which evolves using the Grow-Cut algorithm and enhancement approach based on Multiscale Retinex. The major advantage of the developed approach is its computational simplicity, speed and accuracy as compared to the prior detection, segmentation and enhancement approaches developed for sclera segmentation images. The new used approach was tested in Intel i5 4570 with 3.2 GHZ processor and 16GB RAM and Intel Dual Core with 2.48GHZ processor and with 2GB RAM. Using these two different units, the result shows that segmentation under Intel i5 segment sclera for three 3 hours with an image size of 640x480 pixels while in Intel Dual Core 2GB RAM having the same image size segments the sclera for fourteen (14) hours and eighteen (18) minutes. The experimental results obtained from ND-IRIS 0405 database and publicly available images and respectively achieved better performance in terms of computational simplicity, speed and accuracy as compared to the previous proposed method. Multiscale Retinex enhanced image very fast with a highest minimum execution time of seventeen seconds (17secs) for an image size of 640 x 480 pixels for both colour and gray scale. The proposed approach for enhancement shows better performance in enhancing low contrast, dark with poor illumination images but colour rendition is quite poor for images having good illumination. The experimental results presented in this paper clearly demonstrate the applicability of the proposed sclera detection, segmentation and enhancement approach, i.e., significant reduction in computational complexity in detection segmentation and enhancement while providing comparable segmentation and enhancement performance.

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Section: Fig 1 Anatomy Of the Eyementioning

confidence: 99%

Interactive Growcut Multi-Label N-D Segmentation Based Algorithm for SCLERA Segmentation

Hellwig¹

2018

IJRASET

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“…Derakhshani et al [10], the sclera region was manually segmented. Their research studied the feasibility of using blood veins in the sclera as a method for recognition.…”

Section: Literature Surveymentioning

confidence: 99%

“…Various techniques for sclera vessel enhancement are found in literature. The first recognized work on sclera vessels enhancement is recorded in [33]. In [29] for image registration a local affine and a global smooth transformation was applied.…”

Section: B Sclera Vessel Enhancement and Image Registrationmentioning

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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“…However, as the conjunctiva is the toprecord transparent layer of the sclera and images of the sclera region capture more than just this top-most layer, it is more specific to refer to the scheme as performing "sclera identification". In [10], Derakhshani et. al.…”

Section: IImentioning

confidence: 99%

A Comprehensive Approach for Multi Biometric Recognition Using Sclera Vein and Finger Vein

K.R¹,

Vanitha²,

priya³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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Multimodal biometrics use more than one means of biometric identification to achieve higher detection accuracy, since sometimes a single biometric is not good enough used to do identification and codification. Sclera and finger print strain is a new counterpart vein recognition method using a two-stage parallel approach for documentation and bouting. First, modeled a rotation-and rule-invariant Y shape descriptor based feature extraction method to efficiently eliminate most unlikely matches. Second, developed a weighted polar line sclera descriptor structure to incorporate mask information to reduce Graphics processing unit (GPU) awareness cost. Third, modeled a coarse-tofine two-rung matching approach. Finally, developed a weighing scheme to map the subtasks to GPU processing things. The experimental upshots show that the urged method can achieve dramatic processing speed improvement without compromising the detection accuracy. Here examined the previously proposed finger-strain identification approaches and develop a new approach that illustrates it superiority over prior published efforts. Then is to progress and examine two new score-level blends, i.e., holistic and non linear fusion, and comparatively evaluate them with more popular score-level fusion approaches to ascertain their effectiveness in the proposed scheme. The aim is to combine the upshots obtained by different biometric traits and significantly improve the overall accuracy of the biometric system.

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A New Biometric Modality Based on Conjunctival Vasculature

Cited by 47 publications

References 11 publications

Interactive Growcut Multi-Label N-D Segmentation Based Algorithm for SCLERA Segmentation

Interactive Growcut Multi-Label N-D Segmentation Based Algorithm for SCLERA Segmentation

Survey Paper on Sclera Segmentations Techniques

A Comprehensive Approach for Multi Biometric Recognition Using Sclera Vein and Finger Vein

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