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Advanced Workflow for Taking High-Quality Increment Cores - New Techniques and Devices
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CBAM-DenseNet with multi-feature quality filtering: advancing accuracy in small-sample iris recognition.

Yongheng Pang1,2, Zishen Wang2, Nan Jiang2

  • 1Shanghai Key Laboratory of Forensic Medicine and Key Laboratory of Forensic Science, Ministry of Justice, Shenyang, Liaoning, China.

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|February 12, 2026
PubMed
Summary

Traditional security methods are insufficient. This study introduces a multi-feature fusion iris recognition method, enhancing accuracy and robustness for secure authentication in the information age.

Keywords:
CBAMDenseNetattention mechanismdeep learningimage quality assessmentiris recognitionmulti-feature fusion

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Area of Science:

  • Biometrics
  • Computer Science
  • Information Security

Background:

  • Traditional password and key-based authentication are inadequate for modern information security needs.
  • Iris recognition offers high security and uniqueness but current methods suffer from feature information loss.
  • Single feature extraction in existing iris recognition techniques limits recognition accuracy.

Purpose of the Study:

  • To propose a novel multi-feature fusion-based iris recognition method.
  • To enhance the accuracy and robustness of iris recognition systems.
  • To address the limitations of single feature extraction in current iris recognition approaches.

Main Methods:

  • Implemented a comprehensive quality evaluation scheme for iris image filtering.
  • Utilized an improved CAN network for effective image noise removal.
  • Employed DenseNet for iris feature extraction, combined with a fusion space and attention mechanism (CBAM) for feature expressiveness.

Main Results:

  • Validated significant improvements in recognition accuracy through experiments.
  • Demonstrated enhanced robustness of the proposed iris recognition method.
  • Achieved superior performance on small sample sizes and public iris databases.

Conclusions:

  • The proposed multi-feature fusion method significantly improves iris recognition accuracy and robustness.
  • The integration of quality evaluation, noise reduction, and advanced feature extraction enhances system performance.
  • This approach offers a more secure and reliable authentication solution for the information age.