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Noisy iris smoothing and segmentation scheme based on improved Wildes method.

Anchal Kumawat¹, Sucheta Panda¹

¹Department of Computer Application, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur, Odisha 768018 India.

Multidimensional Systems and Signal Processing

|October 3, 2022

Summary

This summary is machine-generated.

This study introduces an improved Wildes method (IWM) for accurate iris segmentation in automated iris recognition systems. The new approach enhances edge detection and noise removal for reliable performance.

Keywords:

CHT DWT Edge detection Fusion filter Iris segmentation

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Area of Science:

Computer Science
Biometrics
Image Processing

Background:

Accurate iris segmentation is crucial for reliable iris recognition systems.
Traditional methods struggle with precise boundary detection and are sensitive to noise.
Existing techniques are often time-consuming and less effective in noisy environments.

Purpose of the Study:

To propose an improved Wildes method (IWM) for enhanced iris segmentation.
To increase the accuracy and reliability of automated iris recognition systems.
To address limitations of traditional iris segmentation techniques.

Main Methods:

Developed an improved Wildes method (IWM) incorporating novel preprocessing steps.
Implemented an improved Canny with fuzzy logic (ICWFL) for edge detection.
Utilized a hybrid restoration fusion filter (HRFF) for noise removal.

Main Results:

The ICWFL method demonstrated superior edge detection compared to other techniques.
The IWM algorithm showed high efficiency across various noise densities (10-95 dB).
Comparative analysis confirmed the effectiveness of HRFF over existing smoothing filters.

Conclusions:

The proposed IWM significantly improves iris segmentation accuracy and robustness.
The enhanced edge detection and noise removal contribute to reliable iris recognition.
Experimental results validate the proposed method's efficiency using the IIT-Delhi iris database.