A Contourlet Transform Based for Features Fusion in Retina and Iris Multimodal Biometric System

Modarresi, Morteza; Oveisi, Iman Sheikh

doi:10.1007/978-3-319-13386-7_7

Cited by 3 publications

(6 citation statements)

References 15 publications

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“…To exploit publicly available biometric databases, the authors combined three (big) databases containing iris images, built for developing recognition systems, but only one (small, even though well‐prepared and commonly used) of the retina with a very limited number of users. Instead of using data augmentation procedures, sometimes exploited in biometric system experiments [26], the authors preferred to use small yet publicly available dataset accessible by the scientific community allowing for result reproducibility and comparability. Furthermore, since in the literature there are no databases combining together retina and iris images of the same person, three datasets were composed by combining single‐feature belonging to different databases as follows: an image from the retina database was coupled with an image from the iris database, thus creating a new ‘virtual person’ with both biometric traits.…”

Section: Resultsmentioning

confidence: 99%

“…Latha et al [19] were the only one proposing a score level fusion with a hybrid approach using frequency and spatial characteristics for iris and for retina, respectively. Choras [18], Modarresi et al [26], Sen and Islam [27] and Saha et al [12] proposed frequency-based approaches and feature-level fusion, nevertheless some important aspects have to be highlighted (Table 12). Choras [18] did not report any information on either the databases and the number of images used or the scores obtained.…”

Section: Discussion and Comparisonsmentioning

confidence: 99%

“…Choras [18] did not report any information on either the databases and the number of images used or the scores obtained. Modarresi et al [26] used 40 iris images from the CASIA database that became 400 after a data augmentation process by means of 10 random rotations. Sen and Islam [27] reported an unusual 'operating point' in their work.…”

Section: Discussion and Comparisonsmentioning

confidence: 99%

“…Considering that FAR > FRR, this represent a represents an anomalous 'operating point' with respect to biometric good practices. Both Modarresi et al [26] and Sen and Islam [27] used the Chinese Academy of Sciences, Institute of Automation (CASIA) and DRIVE databases for iris and retina.…”

Section: Related Workmentioning

confidence: 99%

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A multimodal retina‐iris biometric system using the Levenshtein distance for spatial feature comparison

et al. 2020

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The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and retina, and the fusion is obtained relying upon the comparison score via the Levenshtein distance. We evaluated our approach by testing several combinations of publicly available biometric databases, namely one for retina images and three for iris images. To provide comprehensive results, detection error trade‐off‐based metrics, as well as statistical analyses for assessing the authentication performance, were considered. The best achieved False Acceptation Rate and False Rejection Rate indices were and 3.33%, respectively, for the multimodal retina‐iris biometric approach that overall outperformed the unimodal systems. These results draw the potential of the proposed approach as a multimodal authentication framework using multiple static biometric traits.

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Section: Resultsmentioning

confidence: 99%

Section: Discussion and Comparisonsmentioning

confidence: 99%

Section: Discussion and Comparisonsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A multimodal retina‐iris biometric system using the Levenshtein distance for spatial feature comparison

et al. 2020

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“…A biometric framework measures at least one physical or behavioral attributes (characteristics) to decide or check the individual personality by utilizing for instance, unique mark, palm print, confront, iris, retina, ear, voice, signature, walk, hand vein, smell, or the DNA data of an individual [2]. Such applications have been demonstrated as secure and have been utilized as a part of managing an account, welfare payment and security [3]. Biometrics have been proposed as a substitution for passwords, conquering some inalienable blemishes in secret key innovation [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Retinal Recognition System for Pathological Retina To Enhance Security

Rani¹,

Rani²

2018

IJET

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Biometric acknowledgment gives a characteristic and solid answer for the issue of individual distinguishing proof. One of the biometric ID framework utilized with high exactness is the retinal verification as a result of its many-sided quality in spoofing. However in these frameworks, acknowledgment rate is enormously influenced by the vasculature multifaceted nature of retinal images. This vascular example turns out to be exceptionally perplexing in sick retinal pictures because of the nearness of obsessive signs. In this manner, we require a computerized productive calculation which can evacuate these abnormalities before matching and decision making. The proposed novel hybrid clustering algorithm Adaptive Weighted Neighbour (AWN) Classification Algorithm classifies the input retinal image based on the features extracted and match the features with the trained features. The proposed feature vector consists of blood vessel structure, bifurcation points, bifurcation angles and vessel width. Results from the AWN are compared with state of the art classifier. It enables high security, good performance and greater accuracy. Also it provides better FAR, FRR and decreases the error rate.

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Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy

Porwal¹,

Pachade²,

Kokare³

et al. 2018

Biomedical Signal and Image Processing in Patient Care

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Diabetic Retinopathy, a condition in the person affected by diabetes, is most common cause of blindness in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer aided disease diagnosis in retinal image analysis could ease mass screening of population with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. With proper self-care, management, and medical professional support, individuals with diabetes can live a healthy and long life.

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A Contourlet Transform Based for Features Fusion in Retina and Iris Multimodal Biometric System

Cited by 3 publications

References 15 publications

A multimodal retina‐iris biometric system using the Levenshtein distance for spatial feature comparison

A multimodal retina‐iris biometric system using the Levenshtein distance for spatial feature comparison

A Novel Retinal Recognition System for Pathological Retina To Enhance Security

Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy

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