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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Feature and score fusion based multiple classifier selection for iris recognition.

Md Rabiul Islam1

  • 1Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Computational Intelligence and Neuroscience
|August 13, 2014
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Summary
This summary is machine-generated.

This study introduces a novel iris recognition system using a voting method to combine multiple classifiers. The proposed multimodal approach enhances recognition accuracy compared to existing methods.

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision
  • Artificial Intelligence

Background:

  • Iris recognition is a key biometric technology for identity verification.
  • Multimodal systems often outperform unimodal systems by integrating diverse data sources.
  • Combining multiple classifiers can improve the robustness and accuracy of recognition systems.

Purpose of the Study:

  • To propose a novel feature and score fusion based iris recognition approach.
  • To enhance iris recognition accuracy by applying a voting method on a Multiple Classifier Selection technique.
  • To evaluate the performance of the proposed system using the CASIA-IrisV4 database.

Main Methods:

  • Utilized four Discrete Hidden Markov Model (HMM) classifiers: left iris unimodal, right iris unimodal, left-right iris feature fusion multimodal, and left-right iris likelihood ratio score fusion multimodal.
  • Combined outputs of the four HMM classifiers using a voting method for final recognition.
  • Evaluated system performance across various dimensions using the CASIA-IrisV4 database.

Main Results:

  • The proposed system demonstrated versatility with four different classifiers and various dimensions.
  • Experimental results confirmed the effectiveness of the voting-based fusion approach.
  • The system achieved competitive recognition accuracy compared to existing fusion methods.

Conclusions:

  • The proposed feature and score fusion based iris recognition approach using a voting method is effective.
  • Multimodal iris recognition combining diverse classifier outputs yields superior performance.
  • The system offers a robust and accurate solution for identity verification.