Retina Based Biometric Authentication System: A Review

Mazumdar, Jarina B.; Nirmala, S. R.

doi:10.26483/ijarcs.v9i1.5322

Cited by 19 publications

(11 citation statements)

References 19 publications

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“…Survey-type contributions on retina recognition can be found in [97,166] where especially the latter manuscript is a very recent one. Fundus imagery exhibits very different properties as compared to the sample data acquired from hand-related vasculature as shown in Fig.…”

Section: Retina Recognition Toolchainmentioning

confidence: 99%

“…This pattern has to be made visible and captured by a suitable biometric scanner device in order to be able to conduct biometric recognition. Two parts of the human body (typically not covered by clothing in practical recognition situations) are the major source to extract vascular patterns for biometric purposes: The human hand [151,275] (used in finger vein [59,120,234,247,250,300] as well as in hand/palm/wrist vein [1,226] recognition) and the human eye (used in retina [97,166] and sclera [44] recognition), respectively.…”

Section: Introductionmentioning

confidence: 99%

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State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

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Section: Retina Recognition Toolchainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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“…Biometrics can be divided into two classes, physiological biometrics and behavioral biometrics [1]. Physiological biometrics usually includes fingerprints [2], facial features [3], palm prints [4], retinas [5], ears [6], and irises [7]. Behavioural biometrics usually consists of keystrokes [8], signatures [9], and gaits [10].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures

et al. 2021

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Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using ‘authentication code’. The ‘authentication code’ is an ‘n’ digit numeric code and the digits are hand gestures of sign language digits. We propose a memory-efficient deep learning model to classify the hand gestures of the sign language digits. The proposed deep learning model is based on the bottleneck module which is inspired by the deep residual networks. The model achieves classification accuracy of 99.1% on the publicly available sign language digits dataset. The model is deployed on a Raspberry pi 4 Model B edge computing system to serve as an edge device for user verification. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and stores it in the buffer. In the second step, the model classifies the digit with the inference rate of 280 ms, by taking the first image in the buffer as input.

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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.…”

mentioning

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%

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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Retina Based Biometric Authentication System: A Review

Cited by 19 publications

References 19 publications

State of the Art in Vascular Biometrics

State of the Art in Vascular Biometrics

Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures

Human Retina Based Identification System Using Gabor Filters and GDA Technique

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