A multi-biometric iris recognition system based on a deep learning approach

Al-Waisy, Alaa S.; Qahwaji, Rami; Ipson, Stanley S.; Al-Fahdawi, Shumoos; Nagem, Tarek A M Hamad

doi:10.1007/s10044-017-0656-1

Cited by 176 publications

(112 citation statements)

References 57 publications

Supporting

Mentioning

111

Contrasting

Unclassified

Order By: Relevance

“…The proposed method can predict gender and race from a single iris image with an accuracy of 86% and 90%, respectively. Figure 1 illustrates the architecture of VGG16 adopted in this work, which has been proven to be a powerful and accurate CNN for classification of image datasets [22,23]. During training, the input is a Red-Green-Blue (RGB) image of fixed-size 512 × 32 pixels.…”

Section: Related Workmentioning

confidence: 99%

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Fang

Zhou

et al. 2019

Electronics

View full text Add to dashboard Cite

As one of the most reliable biometric identification techniques, iris recognition has focused on the differences in iris textures without considering the similarities. In this work, we investigate the correlation between the left and right irises of an individual using a VGG16 convolutional neural network. Experimental results with two independent iris datasets show that a remarkably high classification accuracy of larger than 94% can be achieved when identifying if two irises (left and right) are from the same or different individuals. This exciting finding suggests that the similarities between genetically identical irises that are indistinguishable using traditional Daugman’s approaches can be detected by deep learning. We expect this work will shed light on further studies on the correlation between irises and/or other biometric identifiers of genetically identical or related individuals, which would find potential applications in criminal investigations.

show abstract

Section: Related Workmentioning

confidence: 99%

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Fang

Zhou

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

“…Waisy et al [4] set up a profound learning strategy named as IrisConvNet, Iris recognition is utilized in many applications, like national ID cards, database access and financial services. Various tactices employed in the iris recognition system are triple A technique, fuzzy logic edge estimation technique, active contour technique, deep learning tactic then state-of-the-art recognition algorithm.…”

Section: Litrature Serveymentioning

confidence: 99%

“…The biometric system can recognize the person by both behaviorally and physically, namely fingerprints, palm prints, face, signature, veins and voice [3]. Biometric system is a promising and constantly evolving technology used in the automatic system to identify the person"s identity efficiently and uniquely without the need to remember or carry anything, like Ids and passwords [4]. Most of recent studies proved that the features of an iris image encompasses a number of qualities than other biometric system (face and finger print).…”

mentioning

confidence: 99%

“…In the multimodal biometric system, when the system operates under identification type, the output of the classifier is observed as the slope of positions obtained by the contenders, which signifies the possible matches. Designing and implementing the multimodal biometric system requires a number of factors, which influences the overall concert of the system [4]. To identify the particular person, the iris recognition with biometric feature is used in the most of the paper [5].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Iris Recognition at-a-Distance by Means of Chronological MBO-Based DBN

Shirke¹,

Rajabhushnam²

2019

IJITEE

View full text Add to dashboard Cite

Now a days, Iris recognition is wieldy used for the identification of person. The superior bit of 1 countries exploits biometric system for safety reason with the conclusion goal that in runway boarding, custom freedom, gathering passage, etc. The Iris detection at-a-Distance (IAAD) framework is generally used to identify the person in most of the applications. In this system, different features of iris image are extracted in addition enhances the superiority of iris image. Over the span of the most recent ages there consume raised various structures to design and finish iris affirmation systems which works at longer separation going from one meter to sixty meter. Because of such long scope of iris detection schemes in addition iris attainment scheme provides for the best applications to the client. Therefore, It is necessary to design an effective algorithm for IAAD is necessary. In this article, an actual method for iris recognition is presents. A Chronological Monarch Butterfly Optimization -based Deep Belief Network (Chronological MBO-based DBN) technique is anticipated for iris detection.This technique algorithm is the combination of Chronological theory with the Monarch Butterfly Optimization. It is utilized to mastermind the sequential presumption of an iris picture. Additionally, the Hough Transform calculation is utilized for discovery of iris circle and edge. To enhance the accuracy of anticipated iris recognition system ScatT-Loop descriptor and the Local Gradient Pattern (LGP) are fed to the Chronological MBO-based DBN algorithm and these are castoff to abstract the dissimilar features of an iris picture. The dataset used for these tactices are UBIRIS.v1 For the normalization and segmentation of an iris image is done by by means of Dougman's rubber sheet model. This system is established on MATLAB for executing the Hough transform procedures also for reading the iris images. The simulation results shows that this system successfully recognize the iris at a distance 4 to 8 meter. Different performance parameters like as FAR accuracy, too FRR shows better results in this anticipated work.

show abstract

“…Recognizing and confirming a man is indispensable in most secure applications. Conventional techniques like ID card, passwords can be effortlessly copied, lost or stolen [1]. Additionally these strategies are helpful for confirmation just and not for distinguishing proof.…”

Section: Introductionmentioning

confidence: 99%

A Multi Biometric IRIS Recognition System based on a Profound Learning Method

2019

IJRTE

View full text Add to dashboard Cite

Biometrics is the estimation of natural qualities which are one of a kind to a person for recognizing and confirming the person. The estimations incorporate fingerprints, retinal outputs, iris checks, voice designs, facial qualities, palm prints, and so forth.., Biometric frameworks have been especially effective in distinguishing an obscure individual via looking through a database of attributes and by confirming the case of a person by contrasting his/her trademark with that put away in a database. To expand the heartiness of the framework and to make it more secure, different attributes of a similar individual are utilized. This is alluded to as multimodal biometrics. In this paper we talked about a portion of the multimodal biometric frameworks. Here a bi-modular biometric acknowledgment framework in light of iris, palm-print. Wavelet and curve let change and Gabor-edge channel are utilized to extricate includes in various weighing machine moreover introductions starting iris as well as palm print, finer points taking out in addition to arrangement is utilized in favour of coordinating. diverse combination calculations together with achieve based, positionbased plus choice depend on techniques are utilized to-join the consequences of two constituents. We additionally recommend another rank-based combination calculation Bio Maximum Inverse Rank (BMIR) which is vigorous as for varieties in scores and furthermore awful positioning from a module. IITD iris databases and CASIA datasets for palm print and unique mark are utilized in this investigation. The examinations demonstrate the adequacy of our combination strategy, profound learning, neural systems and our Bi-modular biometric acknowledgment framework in contrast with existing multi-modular acknowledgment frameworks.

show abstract

A multi-biometric iris recognition system based on a deep learning approach

Abstract: identification rate of 100% on all the employed databases and a recognition time less than one second per person.

Cited by 176 publications

References 57 publications

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Iris Recognition at-a-Distance by Means of Chronological MBO-Based DBN

A Multi Biometric IRIS Recognition System based on a Profound Learning Method

Contact Info

Product

Resources

About