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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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

Yung-Hui Li¹, Wenny Ramadha Putri¹, Muhammad Saqlain Aslam¹

¹Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan.

Sensors (Basel, Switzerland)

|March 6, 2021

Summary

This summary is machine-generated.

This study introduces IRUNet, a novel neural network for accurate iris segmentation, improving iris recognition systems even in challenging conditions like blur and low resolution. The method achieves high accuracy in localizing iris boundaries.

Keywords:

biometrics deep convolution and deconvolution neural network image segmentation iris recognition iris segmentation

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Area of Science:

Computer Vision
Biometric Security
Artificial Intelligence

Background:

Accurate iris segmentation is crucial for reliable iris recognition systems.
Traditional methods struggle with non-cooperative environments due to factors like occlusion, blur, and low resolution.
These challenges significantly degrade segmentation accuracy.

Purpose of the Study:

To develop a novel iris segmentation algorithm for robust localization of inner and outer iris boundaries.
To introduce the Interleaved Residual U-Net (IRUNet) for semantic segmentation and iris mask synthesis.
To enhance the accuracy and robustness of iris segmentation in adverse conditions.

Main Methods:

Proposed a neural network model, Interleaved Residual U-Net (IRUNet), for semantic segmentation.
Utilized K-means clustering to select saliency points for outer boundary recovery.
Employed a separate set of saliency points for inner boundary recovery.

Main Results:

Achieved a mean Intersection over Union (IOU) of 98.9% for inner boundary estimation.
Achieved a mean IOU of 97.7% for outer boundary estimation.
Demonstrated superior performance compared to existing methods on the CASIA-Iris-Thousand database.

Conclusions:

The proposed IRUNet algorithm offers a significant advancement in iris segmentation accuracy and robustness.
The method effectively addresses challenges posed by non-ideal imaging conditions.
This contributes to more reliable iris recognition systems, particularly in real-world applications.