Retinal Identification

Rahman, Noor A’in A.; Mohamed, A.S.A.; Rasmy, M.E.

doi:10.1109/cibec.2008.4786039

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Most commonly used non-vascular features are corner points, statistical features, texture features, Fourier coefficients, moment based features, luminance, and contrast. Rahman et al [21] have proposed a method for person identification based on the non-vascular features extracted from the retinal fundus image. The flow diagram is given in Fig.…”

Section: B Non-vascular Based Methodsmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Elangovan

Nath

2019

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In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90%.

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Section: B Non-vascular Based Methodsmentioning

confidence: 99%