Human Identification using Iris Features

Nomir, Omaima; Radwan, Elsayed

doi:10.2316/p.2010.689-023

Cited by 1 publication

(4 citation statements)

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“…The third segment of an individual represents the p-parameter value of the norm entropy , equation 3. In norm entropy, p values should be ranged in [1,2) [14]. Sensitive p-parameter is considered to be 1/7, though p-parameter is represented by three bits for each individual chromosome (ranged from 000 to 111).…”

Section: A) Chromosome Representationmentioning

confidence: 99%

“…Unfortunately, iris recognition suffers from the scale and rotation invariant problems, a certain fixed resolution, and non-regarding iris features during the stage of feature extraction. Moreover, the time complexity is taken in training by the iris classifier [2,3,4,5]. In the recent past, some iris verification and recognition techniques have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…The human iris, the part between the pupil and the sclera, has an extraordinary structure and provides many interlacing minute characteristics. The process of iris recognition depends on two consecutive phases, localization of the iris domain, as depicted in Figure1 [2], and generation of the feature set of iris images. Hence, a convenient iris classifier should be used.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, Gabor filter is limited because there is no single filter resolution at which one can localize a spatial structure in iris texture. Because of the wide range of wavelet functions, Wavelet Transforms (WTs) have various resolutions that allow researchers to represent iris texture at the most suitable scale [14,15,2,16]. But WT is still nonsupportive to directionality and anisotropy [17].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed Optimization Model of Wavelet Neuron for Human Iris Verification

Radwan¹,

Tarek²

2015

ijacsa

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Abstract-Automatic human iris verification is an active research area with numerous applications in security purposes. Unfortunately, most of feature extraction methods in human iris verification systems are sensitive to noise, scale and rotation. This paper proposes an integrated hybrid model among Discrete Wavelet Transform, Wavelet Neural Network and Genetic Algorithms for optimizing the feature extraction and verification methods. For any iris image, the wavelet features are extracted by Discrete Wavelet Transform without any dependency on scale and pixels' intensity. Besides, Wavelet Neural Network classifier is integrated as a local optimization method to solve the orientation problem and increase the intrinsic features. In solving the down sample process caused by DWT, each human iris should be characterized by a set of parameters of its optimal wavelet analysis function at a determined analysis level. Thus, distributed Genetic Algorithms, meta-heuristic algorithm, is introduced as a global optimization searching technique to discover the optimal parameter values. The details and limitation of this paper will be discussed where a comparative study should appear. Moreover, conclusions and future work are described.

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Section: A) Chromosome Representationmentioning

confidence: 99%