Improved fast iris localization algorithm

2008 ◽  
Vol 28 (3) ◽  
pp. 674-676 ◽  
Author(s):  
Liu-liu ZHU
Keyword(s):  
Localization Algorithm ◽  
Iris Localization

A ROBUST IRIS LOCALIZATION ALGORITHM VIA RADIAL SYMMETRY FOR NONIDEAL CAPTURING CONDITION

2009 ◽  
Vol 23 (01) ◽  
pp. 45-57 ◽  
Author(s):  
WEN-CONG ZHANG ◽  
BIN LI ◽  
XUE-YI YE ◽  
ZHEN-QUAN ZHUANG ◽  
KONG-QIAO WANG
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Radial Symmetry ◽  
Precise Localization ◽  
Localization Algorithm ◽  
Time System ◽  
Iris Localization ◽  
Real Time System ◽  
The Ideal

Iris localization is a key component of practical iris recognition system. Previous algorithms show good localization performances for iris images captured in the ideal conditions. However, in practice, the quality of iris image is greatly influenced by luminance, eyelashes, hair or glasses frame, which will cause mislocalization. In order to improve the robustness of iris localization, this paper proposes a new localization algorithm based on the radial symmetry transform, in which the radial symmetry characteristic of the pupil is utilized to realize iris localization. Experimental results show that the proposed algorithm can efficiently avoid the interference of luminance and other bad conditions, and realize robust precise localization in a real-time system.

An Improved Iris Localization Algorithm Based on Curve Fitting

2013 ◽  
Vol 380-384 ◽  
pp. 1176-1179
Author(s):  
Yi Huang ◽  
Xiao Ping Zeng
Keyword(s):  
Curve Fitting ◽  
Outer Boundary ◽  
Regions Of Interest ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Iris Image ◽  
The Third ◽  
Boundary Points ◽  
Maximum Variance ◽  
Improved Algorithm

Iris localization is to detect outer-and-inner boundaries of iris in an iris image. In the paper, an improved algorithm was proposed to quickly and effectively locate outer-and-inner boundaries. As for this algorithm, the first is to block an iris image and extract its sub-image blocks which cover pupil; the second is to set a binary threshold of pupil by adopting the method of Maximum Variance between Clusters; the third is to get the value outer-boundary-points of iris, on the basis of gray gradient of key Regions-of-interest; the last is to select some characteristic pixels in regions of interest respectively and fit outer-and-inner boundaries of iris according to curve fitting.

A Fast Iris Localization Algorithm Based on Hough Transformation

2008 ◽  
Author(s):  
Qin Huang ◽  
Xiaofeng Gu ◽  
Jianping Li ◽  
Jianming Liao ◽  
Huamin Liu
Keyword(s):  
Localization Algorithm ◽  
Iris Localization ◽  
Hough Transformation

scholarly journals Efficient Iris Localization via Optimization Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qi Wang ◽  
Zhipeng Liu ◽  
Shu Tong ◽  
Yuqi Yang ◽  
Xiangde Zhang
Keyword(s):  
Optimization Model ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Descent Method ◽  
Least Square ◽  
Experimental Result ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Supervised Descent Method

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

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