Retinal Image Processing in Biometrics

Kachouri, Rostom; Akil, Mohamed; Elloumi, Yaroub

doi:10.1007/978-981-13-0956-4_10

Cited by 4 publications

(3 citation statements)

References 27 publications

(33 reference statements)

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“…defined as the automated authentication of individuals based on their physiological or behavioral characteristics. A high security level can be achieved by using this scheme as mentioned in references [1][2][3][4][5][6][7][8][9][10][11]. In this paper, the research concentrates on the use of retinal scans as means of authentication and human retina as the biometric.…”

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confidence: 99%

“…So, it is called 'Eye Print' [5,12], these unique blood vessels form the foundation of the retinal recognition system. Blood vessels of retina remain stable over a person's lifetime unless the person subjects to a severe eye surgical operation or injury [1] [6][7][8][9] [ [13][14][15][16]. So, among all other biometrics, retina has the most fixed features for individual recognition.…”

mentioning

confidence: 99%

“…Blood vessels pattern around OD distributes with less randomness. As blood vessels go away from this region, they become thinner and more random in their distribution [9]. Also; after compensating undesired rotation of the retinal image there is a need to solve the translation problem as well.…”

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confidence: 99%

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Human Retina Based Identification System Using Gabor Filters and GDA Technique

Sultan

Ghanim

2020

J. commun. softw. syst. (Online)

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A biometric authentication system provides an automatic person authentication based on some characteristic features possessed by the individual. Among all other biometrics, human retina is a secure and reliable source of person recognition as it is unique, universal, lies at the rare end of the eye and hence it is unforgeable. The process of authentication mainly includes pre-processing, feature extraction and then features matching and classification. Also authentication systems are mainly appointed in verification and identification mode according to the specific application. In this paper, pre-processing and image enhancement stages involve several steps to highlight interesting features in retinal images. The feature extraction stage is accomplished using a bank of Gabor filter with number of orientations and scales. Generalized Discriminant Analysis (GDA) technique has been used to reduce the size of feature vectors and enhance the performance of proposed algorithm. Finally, classification is accomplished using k-nearest neighbor (KNN) classifier to determine the identity of the genuine user or reject the forged one as the proposed operate in identification mode.

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