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Ear recognition based on Gabor features and KFDA.

Li Yuan1, Zhichun Mu2

  • 1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ; Visualization and Intelligent Systems Laboratory, University of California Riverside, Riverside, CA, 92507, USA.

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Summary
This summary is machine-generated.

This study introduces an effective ear recognition system using 2D ear images. The proposed method accurately detects, normalizes, and extracts features for reliable ear authentication.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Biometric systems are crucial for security.
  • Ear recognition offers a unique and stable biometric trait.
  • Existing ear recognition methods face challenges with complex backgrounds and feature extraction.

Purpose of the Study:

  • To develop a robust 2D ear recognition system.
  • To improve ear detection and normalization accuracy.
  • To enhance feature extraction and classification for reliable ear authentication.

Main Methods:

  • Ear detection using an improved Adaboost algorithm with cascaded classifiers.
  • Ear segmentation and normalization via Active Shape Model.
  • Gabor filter for spatial local feature extraction.
  • Kernel Fisher Discriminant Analysis (KFDA) for dimensionality reduction.
  • Distance-based classifier for final recognition.

Main Results:

  • The proposed ear detection effectively handles complex backgrounds.
  • Active Shape Model ensures consistent ear image normalization.
  • Gabor filters provide discriminative ear features.
  • KFDA successfully reduces feature dimensionality.
  • Experimental results on USTB and UND datasets demonstrate high accuracy and feasibility.

Conclusions:

  • The integrated ear recognition system is feasible and effective.
  • The proposed approach offers a reliable solution for ear-based authentication.
  • This method shows promise for real-world biometric applications.