A novel iris segmentation algorithm based on small eigenvalue analysis

2015 ◽  
Author(s):  
B. S. Harish ◽  
S. V. Aruna Kumar ◽  
D. S. Guru ◽  
Minh Ngoc Ngo
Keyword(s):  
Small Eigenvalue ◽  
Segmentation Algorithm ◽  
Eigenvalue Analysis ◽  
Iris Segmentation

An iris segmentation algorithm based on edge orientation for off-angle iris recognition

2013 ◽  
Author(s):  
Mahmut Karakaya ◽  
Del Barstow ◽  
Hector Santos-Villalobos ◽  
Christopher Boehnen
Keyword(s):  
Iris Recognition ◽  
Segmentation Algorithm ◽  
Iris Segmentation ◽  
Edge Orientation

Fast and reliable iris segmentation algorithm

2013 ◽  
Vol 7 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Abduljalil Radman ◽  
Nasharuddin Zainal ◽  
Kasmiran Jumari
Keyword(s):  
Segmentation Algorithm ◽  
Iris Segmentation

scholarly journals Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1434
Author(s):  
Yung-Hui Li ◽  
Wenny Ramadha Putri ◽  
Muhammad Saqlain Aslam ◽  
Ching-Chun Chang
Keyword(s):  
Iris Recognition ◽  
Outer Boundary ◽  
Semantic Segmentation ◽  
Recognition System ◽  
Segmentation Algorithm ◽  
Iris Segmentation ◽  
Specular Reflections ◽  
Boundary Estimation ◽  
Segmentation Methods ◽  
The Mean

Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

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