Fast normalized cross-correlation based retinal recognition

Rubaiyat, Abu Hasnat Mohammad; Aich, Shubhra; Toma, Tanjin Taher; Mallik, Abidur Rahman; Rafat-Al-Islam,

doi:10.1109/iccitechn.2014.7073086

Cited by 3 publications

(1 citation statement)

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“…The test results referred that the system can reach high average true recognition. Rubaiyat et al (2014) presented biometric scheme based on color retinal images. The scheme includes three stages (i.e., preprocessing, feature extraction and vessel matching).…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Method for Automatic Human Recognition Based on Retinal Vascular Analysis

Tuama¹,

George²

2016

RJASET

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Biometric security has become more important because of the Increasing activities of hackers and terrorists. Retinal biometric system is one of the most reliable and stable biometrics for the identification/verification of individuals in high security area rather than other biometric. Also no two people have the same retinal pattern and then cannot be stolen or forget. Due to these reasons this study presents a system for individual recognition based on vascular retina pattern. This approach is robust to brightness variations, noise and it is insensitive to rotation. The proposed method consists of three main stages (i.e., preprocessing, feature extraction and finally matching stage). Preprocessing is done to make the required color band separation, remove the rotation appearance which might occur during the scanning process and modify the image brightness to simplify the process of extracting vascular pattern (region of interesting) from input retina (i.e., feature vector) in the feature extraction stage. Finally, the discrimination process of features is evaluated and the results utilized in matching stage. The proposed method is tested on the two publicly available datasets: (i) DRIVE (Digital Retinal Images for Vessel Extraction) and (ii) STARE (Structured Analysis of the Retina). The achieved accuracy of recognition rate was equal to 100% for all datasets.

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Section: Literature Reviewmentioning

confidence: 99%