Biometric iris recognition system using a fast and robust iris localization and alignment procedure

Ganeshan, Balaji; Theckedath, Dhananjay; Young, Rupert; Chatwin, Chris

doi:10.1016/j.optlaseng.2005.03.010

Cited by 31 publications

(12 citation statements)

References 10 publications

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“…Score t = 0.5 • score frag1 + 0.5 • score frag2 (3) where Score t is the total score for the classification. Table 1 shows the accuracy rate , variance and time obtained in the identification proceses for with fragmentation and without fragmentation.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, we use the procedure described in [3], which creates a circle of radius R (r p < R) concentric to the pupil that is used to clean the image. Thus, the portion of the iris considered for the recognition is the area located in between the pupil and the circle of radius R. Everything outside this circle is eliminated by reseting the pixeles to 0.…”

Section: Localizationmentioning

confidence: 99%

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Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

Campos

Salas

Allende

et al. 2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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This work presents a new method for feature extraction of iris images to improve the identification process. The valuable information of the iris is intrinsically located in its natural texture, and preserving and extracting the most relevant features is of paramount importance. The technique consists in several steps from adquisition up to the person identification. Our contribution consists in a multimodal algorithm where a fragmentation of the normalized iris image is performed and, afterwards, regional statistical descriptors with Self-Organizing-Maps are extracted. By means of a biometric fusion of the resulting descriptors, the features of the iris are compared and classified. The results with the iris data set obtained from the Bath University repository show an excellent accuracy reaching up to 99.867%.

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Section: Resultsmentioning

confidence: 99%

Section: Localizationmentioning

confidence: 99%

Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

Campos

Salas

Allende

et al. 2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…The next step involves determining the centre of the pupil [17]. This is done by finding the row and column having the maximum number of pixels of gray level "0", horizontally, vertically and diagonally.…”

Section: Iris Segmentationmentioning

confidence: 99%

A Selective Feature Matching Approach for Iris Recognition

Dalal¹,

Sahoo²

2012

IJCA

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Reliable automatic recognition of persons has long been an attractive goal of many researchers. Thus the recognition of an individual based on iris pattern is gaining more popularity due to the uniqueness of the pattern among the people which are highly stable starting from about one year past the date of birth, until death. The probability for the existence of two irises that are same has been theoretically estimated to be very high, i.e. one in 10 72 which counts for the unique characterization of the iris. Although many approaches for iris recognition have been proposed by many researchers in the last few years, in this paper a selective iris feature matching method for iris recognition based on optimized wavelet decomposition of normalized iris image has been proposed. Comparing the average normalised correlation of the wavelet coefficients of optimised level and its adjacent levels improved matching is obtained, thus performing uniqueness verification of a person.

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“…The human iris contains around 266 visible patterns, which forms the basis of several recognition algorithms [9,18]. Even on the same person, left and right irises are different.…”

Section: Introductionmentioning

confidence: 99%

An iris matching algorithm for reliable person identification optimized level of decomposition

Dalal

Sahoo

Bhoi

2012

2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET)

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Human iris provides a unique structure suitable for non-invasive biometric assessment. So many researchers have been trying to improve the algorithm for reliable iris recognition. This paper presents a new iris matching method to control the misclassification error. Daubechies wavelet transform is used to extract the textural features. Combining the normalized correlation of the wavelet coefficients of optimized decomposition level with existing approach, efficient matching is obtained, thus performing person identification with much ease and evidenced.

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Biometric iris recognition system using a fast and robust iris localization and alignment procedure

Cited by 31 publications

References 10 publications

Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

A Selective Feature Matching Approach for Iris Recognition

An iris matching algorithm for reliable person identification optimized level of decomposition

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