Recognition of Image-Orientation-Based Iris Spoofing

Czajka, Adam; Bowyer, Kevin W.; Krumdick, Michael; VidalMata, Rosaura G.

doi:10.1109/tifs.2017.2701332

Cited by 35 publications

(19 citation statements)

References 16 publications

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“…There are two major aspects of developing SVM as a classifier. The first aspect is to determine the optimal hyperplane in between two separate classes of data and the another aspect is to transform the non-linearly separable classification problem into linearly separable problem (Czajka et al, 2017). Figure 7 shows an example of Linearly separable classification problem.…”

Section: Support Vector Machinementioning

confidence: 99%

A fast iris recognition system through optimum feature extraction

Rana

Azam

Akhtar

et al. 2019

PeerJ Computer Science

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With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person’s lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

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Section: Support Vector Machinementioning

confidence: 99%

A fast iris recognition system through optimum feature extraction

Rana

Azam

Akhtar

et al. 2019

PeerJ Computer Science

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“…Biometric technologies such as fingerprints, facial recognition, and retinal scans produce accurate and unique identifiers. The identification using such modalities are reliable [9][10][11].…”

Section: Biometric Approaches For Web Control Accessmentioning

confidence: 99%

BiometricAccessFilter: A Web Control Access System Based on Human Auditory Perception for Children Protection

et al. 2020

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Along with internet growth, security issues come into existence. Efficient tools to control access and to filter undesirable web content are needed all the time. In this paper, a control access method for web security based on age estimation is proposed, where the correlation between human age and auditory perception is taken into account. In particular, access is denied if a person’s age is not appropriate for the given web content. Unlike existing web access filters, our biometric approach offers greater security and protection to individual privacy. From a technical point of view, the machine-learning regression model is used to estimate the person’s age. The primary contributions of this paper include an age estimation module based on human auditory perception and provision of an open-source web filter to prevent adults from accessing children web applications. The proposed system can also be used to limit the access of children to a webpage specially designed for adults. Our system is evaluated with a dataset collected from 201 persons with different ages from 06 to 60 years old, where it considered 109 male and 82 female volunteers. Results indicate that our system can estimate the age of a person with an accuracy of 97.04% and a root mean square error (RMSE) of 4.2 years. It presents significant performances in the verification scenario with an Equal Error Rate (EER) of 1.4%.

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“…This optimization method combines the benefits of RMSprop and AdaGrand optimization methods. The more details on this optimization is given in [15].…”

Section: ) Adam Optimizationmentioning

confidence: 99%

Multi-modal Iris Recognition System based on Convolution Neural Network

Choudhari¹,

Mehra²,

Shallu³

2019

IJITEE

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Iris is most promising bio-metric trait for identification or authentication. Iris consists of patterns that are unique and highly random in nature .The discriminative property of iris pattern has attracted many researchers attention. The unimodal system, which uses only one bio-metric trait, suffers from limitation such as inter-class variation, intra-class variation and non-universality. The multi-modal bio-metric system has ability to overcome these drawbacks by fusing multiple biometric traits. In this paper, a multi-modal iris recognition system is proposed. The features are extracted using convolutional neural network and softmax classifier is used for multi-class classification. Finally, rank level fusion method is used to fuse right and left iris in order to improve the confidence level of identification. This method is tested on two data sets namely IITD and CASIA-Iris-V3.

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Recognition of Image-Orientation-Based Iris Spoofing

Cited by 35 publications

References 16 publications

A fast iris recognition system through optimum feature extraction

A fast iris recognition system through optimum feature extraction

BiometricAccessFilter: A Web Control Access System Based on Human Auditory Perception for Children Protection

Multi-modal Iris Recognition System based on Convolution Neural Network

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