Iris recognition using artificial neural networks

Sibai, Fadi N.; Hosani, Hafsa I.; Naqbi, Raja M.; Dhanhani, Salima; Shehhi, Shaikha

doi:10.1016/j.eswa.2010.11.029

Cited by 62 publications

(17 citation statements)

References 12 publications

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“…For example, artificial neural networks (i.e., systems that learn from data) have been used in different biometric applications involving pattern classification and identification (of a human (Dinkar andSambyal 2012, Melin et al 2012), of driver (Wu and Ye 2009), of finger-vein patterns (Wu and Liu 2011), of iris recognition (Sibai et al 2011), of human action (Youssef and Asari 2013), of gait (Zeng and Wang 2012), of the face (Connolly et al 2013;Kuo et al 2011;Choi et al 2012;Banerjee and Datta 2013;Lin and Lin 2013;Müller et al 2013), of the hand (Michael et al 2008), of the skin (Zaidan et al 2014), by keystroke (Uzun and Bicakci 2012) and by gesture, speech, handwritten text recognition and the like). Various biometric systems are being developed in such a manner (face recognition, fingerprint identification, hand geometry biometrics, retina scan, iris scan, signature, voice analysis, palm vein authentication and others).…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…al. [11] uses CIE Lab color space, Monaco [12] uses RGB, CIE lab, HSV, CMYK, YcbCrSun [13], Al-Quanieer [14] , Tan [15] and Sibai [16] used RGB color models for preprocessing. RGB and nine different color models, i.e.…”

Section: Iris Feature Extraction Using Histogram Of Color Modelsmentioning

confidence: 99%

Color Iris Authentication using Color Models

Narote¹,

Waghmare²

2017

IJCA

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Iris authentication is a popular method where persons are accurately authenticated. During authentication phase the features are extracted which are unique. Iris authentication uses IR images for authentication. This proposed work uses color iris images for authentication. Experiments are performed using ten different color models. This paper is focused on performance evaluation of color models used for color iris authentication. This proposed method is more reliable which cope up with different noises of color iris images. The experiments reveals the best selection of color model used for iris authentication. The proposed method is validated on UBIRIS noisy iris database. The results demonstrate that the accuracy is 92.1%, equal error rate of 0.072 and computational time is 0.039 seconds.

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“…What is important in developing neural networks is their useful behavior by learning to recognize and apply relationships between objects and patterns of objects specific to the real world. In this respect neural networks are tools that can be used to solve difficult problems [8], [9], [10]. Artificial neural networks are inspired by the architecture of the biological nervous system, which consists of a large number of relatively simple neurons that work in parallel to facilitate rapid decision-making [11].…”

Section: Neural Network Based Modelmentioning

confidence: 99%

IRIS Recognition System using Neural Network and Genetic Algorithm

SaishanmugaRaja¹,

Rajagopalan²

2013

IJCA

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In this paper, the author proposes a method for personal identification based on iris recognition using Genetic algorithm and Neural Network. The process of iris recognition consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition. A Neural Network is used to reduce the low recognition rate, low accuracy and increased time of recovery. Here the genetic algorithm is used to optimize the Neural Networks parameters. The simulation results show a good identification rate and reduced training time.

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Iris recognition using artificial neural networks

Cited by 62 publications

References 12 publications

Intelligent Decision Support Systems

Intelligent Decision Support Systems

Color Iris Authentication using Color Models

IRIS Recognition System using Neural Network and Genetic Algorithm

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