A texture-based method for identificaiton of retinal vasculature

Gottemukkula, Vikas; Saripalle, Sashikanth; Derakshani, Reza; Tankasala, Sriram Pavan

doi:10.1109/ths.2011.6107908

Cited by 7 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of today's biometric market is focused on two modalities: fingerprints, which are among the oldest and iris, which is often quoted to be one of the most accurate among all other biometric modalities [5,6]. Gait, voice, face, retinal vasculature, vasculature seen on the white of eyes, and hand geometry are examples of other existing or emerging biometric authentication methods [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Method for using visible ocular vasculature for mobile biometrics

Gottemukkula¹,

Saripalle

Tankasala

et al. 2016

IET biom.

Self Cite

View full text Add to dashboard Cite

Securing personal information on handheld devices, especially smartphones, has gained a significant interest in recent years. Yet, most of the popular biometric modalities require additional hardware. To overcome this difficulty, the authors propose utilising the existing visible light cameras in mobile devices. Leveraging visible vascular patterns on whites of the eye, they develop a method for biometric authentication suitable for smartphones. They start their process by imaging and segmenting whites of the eyes, followed by image quality assessment. The authors' stage 1 matcher is a three-step process that entails extracting interest points [Harris-Stephens, features from accelerated segment test, and speeded up robust features (SURF)], building features (SURF and fast retina keypoint) around those points, and match score generation using random sample consensus-based registration. Stage 2 matcher uses registered Gabor phase filtered images to generate orientation of local binary pattern features for its correlation-based match metric. A fusion of stage 1 and stage 2 match scores is calculated for the final decision. Using a dataset of 226 users, the authors' results show equal error rates as low as 0.04% for long-term verification tests. The success of their framework is further validated on UBIRIS v1 database.

show abstract

Section: Introductionmentioning

confidence: 99%

Method for using visible ocular vasculature for mobile biometrics

Gottemukkula¹,

Saripalle

Tankasala

et al. 2016

IET biom.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Retinal scans' high recognition accuracies and their reported resistance to spoofing has led to the incorporation of retinal biometric systems in high-security access control cases [2][3][4]. However, retinal scanning requires specialised equipment and significant user cooperation [5,6]. Near infrared iris biometric systems have also shown high accuracies under amenable conditions.…”

Section: Introductionmentioning

confidence: 99%

Ocular surface vasculature recognition using curvelet transform

et al. 2016

Self Cite

View full text Add to dashboard Cite

The vascular patterns seen on the white of the eye, mainly in conjunctival and episcleral layers, are termed as ocular surface vasculature (OSV). OSV is visible in images captured with commercial RGB cameras, and its unique texture can be used for biometric recognition. This study demonstrates the capabilities of curvelet transform for OSV feature extraction. Nonlinear feature enhancement and feature mapping in curvelet domain are shown to be effective in differentiating OSV texture. Linear discriminant analysis and similarity metrics are used for matching. A match-score level fusion is used across multiple gaze directions for both eyes. Using a multi-distance dataset of 50 volunteers, where eye images were acquired from 30, 150, and 250 cm using a dSLR, a best equal error rate (EER) of 0.2% is obtained. Using a second dataset of 40 volunteers acquired from 150 cm using a dSLR, a best EER of 3.1% is obtained. For a 216-participant dataset of ocular images acquired using cellular phones from close proximity, an EER of 0.9% is obtained. The proposed methodology was also tested on the publicly available UBIRIS V1 dataset, yielding an EER of 0.7%. The experimental results support the theoretically formulated advantages of the curvelet transform and its capability in successful extraction of curved structures when applied to OSV patterns.

show abstract

Steady Illumination Color Local Ternary Pattern as a Feature Extractor in Iris Authentication

Khatoon¹,

Ghose

2017

Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

View full text Add to dashboard Cite

A texture-based method for identificaiton of retinal vasculature

Cited by 7 publications

References 18 publications

Method for using visible ocular vasculature for mobile biometrics

Method for using visible ocular vasculature for mobile biometrics

Ocular surface vasculature recognition using curvelet transform

Steady Illumination Color Local Ternary Pattern as a Feature Extractor in Iris Authentication

Contact Info

Product

Resources

About