scholarly journals A Problem with the Assessment of an Iris Identification System

SIAM Review ◽  
2009 ◽  
Vol 51 (2) ◽  
pp. 417-422 ◽  
Author(s):  
Michel Dekking ◽  
André Hensbergen
Keyword(s):  
Identification System ◽  
Iris Identification

Iris Identification System

2016 ◽  
pp. 232-249
Author(s):  
N. Poonguzhali ◽  
M. Ezhilarasan
Keyword(s):  
Performance Metrics ◽  
Texture Features ◽  
Recognition System ◽  
Medical Diagnostics ◽  
Multilayered Structure ◽  
Identification System ◽  
Geometric Features ◽  
Emerging Trends ◽  
Human Iris ◽  
Iris Identification

Recent research on iris is not only on recognition; emerging trends are also in medical diagnostics, personality identification. The iris based recognition system rely on patterns/textures present in the iris, the color of the iris, visible features present in the iris, geometric features of the iris and the SIFT features. An overview of biometric generation is presented. Human iris can be viewed as a multilayered structure in its anterior view. The iris consists of three zones, the pupillary zone, collarette and the ciliary zone. The texture features present in the pupillary zone and collarette are used for identification. As these features are closer to the pupil they are not affected by the occlusion caused by eyelid or eyelashes. The geometric features of the iris can also be used for human identification. The structure of the iris is more related to the geometric shape and hence the extraction of these features is also possible. An overview of the performance metrics to evaluate a biometric system is also presented.

A Field Study of an Iris Identification System

2008 ◽  
Author(s):  
Simon McLaren ◽  
Simson L. Garfinkel
Keyword(s):  
Field Study ◽  
Identification System ◽  
Iris Identification

Sensar...secure/sup TM/ Iris identification system

2002 ◽  
Author(s):  
T. Camus ◽  
U.M. Cahn von Seelen ◽  
G.G. Zhang ◽  
P.L. Venetianer ◽  
M. Salganicoff
Keyword(s):  
Identification System ◽  
Iris Identification

scholarly journals Second-Order Statistical Methods GLCM for Authentication Systems

2021 ◽  
Vol 17 (1) ◽  
pp. 1-6
Author(s):  
Mohammed Taha ◽  
Hanaa Ahmed
Keyword(s):  
Iris Recognition ◽  
Near Infrared ◽  
Color Image ◽  
Visible Spectrum ◽  
Second Order ◽  
Infrared Light ◽  
Identification System ◽  
Extraction Technology ◽  
Occurrence Matrix ◽  
Iris Identification

For many uses, biometric systems have gained considerable attention. Iris identification was One of the most powerful sophisticated biometrical techniques for effective and confident authentication. The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without collaboration among the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion, and reflection. This work aims to use the Gray-Level Co- occurrence Matrix (GLCM) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. GLCM is second-order Statistical-Based Methods for Texture Analysis. The GLCM-based extraction technology was applied after the preprocessing method to extract the pure iris region's characteristics. The Energy, Entropy, Correlation, homogeneity, and Contrast collection of second-order statistical features are determined from the generated co-occurrence matrix, Stored as a vector for numerical features. This approach is used and evaluated on the CASIA v1and ITTD v1 databases as NIR iris image and UBIRIS v1 as a color image. The results showed a high accuracy rate (99.2 %) on CASIA v1, (99.4) on ITTD v1, and (87%) on UBIRIS v1 evaluated by comparing to the other methods

Iris Feature Extraction Algorithm Valuation Based on the Fuzzy Clustering Evaluation Model

2014 ◽  
Vol 513-517 ◽  
pp. 555-558
Author(s):  
Jin Liu ◽  
Cang Ming Liu ◽  
Lei Zhao
Keyword(s):  
Feature Extraction ◽  
Fuzzy Clustering ◽  
Evaluation Model ◽  
Identification System ◽  
Evaluation Scheme ◽  
Clustering Model ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Iris Feature Extraction ◽  
Iris Identification

Iris Feature Extraction Algorithm Evaluation is an important link of evaluation on iris identification system. For the need of optimal feature extraction algorithm according to the identification, this thesis provides a evaluation scheme based on the fuzzy clustering model, and builds a relevant evaluation model, which proves to make a scientific evaluation on the algorithm in the phase of feature extraction.

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