A secure personal identification system based on human retina

Fatima, Joddat; Syed, Adeel M.; Akram, M. Usman

doi:10.1109/isiea.2013.6738974

Cited by 23 publications

(16 citation statements)

References 14 publications

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“…The neural network is trained by backpropagation. Fatima et al [17] used a recursive supervised multilayered thresholding for accurate segmentation. Vascular ending and bifurcation are used as features.…”

Section: Related Workmentioning

confidence: 99%

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Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

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Section: Related Workmentioning

confidence: 99%

“…In phase-based level set methods, vessel width plays an important role in the wavelet response. Thick blood vessels give a high response in contrast to thin vessels [17]. Due to varying wavelet response, thin vessels may get discarded.…”

Section: B Segmentationmentioning

confidence: 99%

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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“…Joddat et al, 28 performs an extraction of the vascular pattern using input retinal image. In doing so, he utilized wavelets and multi-layered thresholding technique and subsequently extracted all possible feature points and denoted each feature point with a unique feature vector.…”

Section: Related Workmentioning

confidence: 99%

Biometric retinal authentication based on multi– resolution feature extraction using mahalanobis distance

Asem¹

2018

BBIJ

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Since the fundamental features of retinal images are comprised different orders and resolution, in this paper, we attempt to analyze retinal images by means of a multiresolution method, which is based on contourlet transform. This impressive transform method is a relatively newly-introduced two-dimensional extension or equivalent of the wavelet transform making use of multi-scale and directional filter banks. Its corresponding expansion, also known as The Contourlet Expansion, consists of basis images being oriented in various directions and in multiple scales with potentially flexible aspect ratios. Movement effects of the eyeball occurring during the scanning process encouraged us to employ a method based on the Radial Tchebichef Moments so as to estimate and eliminate the effects rotation angle of the head or eyeball movement may introduce in the scanning process. After this, localizing the optic disc and eliciting the Region of Interest (ROI) intended to acquire similar parts within different retinal images from the same person, a rotation invariant template can be achieved from each ROI retinal sample. The Mahalanobis distance is utilized in the proposed method to assess the biometric pattern similarity; human identification is achievable though solving the maximization for the matching scores. The experimental results encompassing 5500 images resulted from 550 subjects show EER = 0.0032 in retinal identification. These results are a testimony of the power and efficacy of the proposed approach.

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“…It performs analysis of data to identify patterns and finds patterns to reduce dimensions of the dataset with the minimal loss of information [21]. It projects data into the direction with maximum variances where data variance is greater [4].…”

Section: Region Based Feature Extractionmentioning

confidence: 99%

“…Commonly used biometric systems are fingerprint, iris, face, and speech recognition [4]. Among all biometric systems, retinal recognition is the most stable system due to high stability and reliability of human retina as it lies at the back of human eye.…”

Section: Introductionmentioning

confidence: 99%

Removal of False Blood Vessels Using Shape Based Features and Image Inpainting

Waheed

Akram

et al. 2015

Journal of Sensors

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Automated quantification of blood vessels in human retina is the fundamental step in designing any computer-aided diagnosis system for ophthalmic disorders. Detection and analysis of variations in blood vessels can be used to diagnose several ocular diseases like diabetic retinopathy. Diabetic Retinopathy is a progressive vascular disorder caused due to variations in blood vessels of retina. These variations bring different abnormalities like lesions, exudates, and hemorrhages in human retina which make the vessel detection problematic. Therefore, automated retinal analysis is required to cater the effect of lesions while segmenting blood vessels. The proposed framework presents two improved approaches to carry out vessel segmentation in the presence of lesions. The paper mainly aims to extract true vessels by reducing the effect of abnormal structures significantly. First method is a supervised approach which extracts true vessels by performing region based analysis of retinal image, while second method intends to remove lesions before extracting blood vessels by using an inpainting technique. Both methods are evaluated on STARE and DRIVE and on our own database AFIO. Experimental results demonstrate the excellence of the proposed system.

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A secure personal identification system based on human retina

Cited by 23 publications

References 14 publications

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Biometric retinal authentication based on multi– resolution feature extraction using mahalanobis distance

Removal of False Blood Vessels Using Shape Based Features and Image Inpainting

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