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Online 3D Ear Recognition by Combining Global and Local Features.

Yahui Liu1, Bob Zhang2, Guangming Lu1

  • 1Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.

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|December 10, 2016
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Summary

This study introduces a new 3D ear scanning method for biometric authentication. Fusing global and local ear features achieved a 2.2% equal error rate, demonstrating high accuracy in identifying individuals.

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision and Image Processing
  • Human-Computer Interaction

Background:

  • The unique and permanent 3D shape of the human ear makes it a promising biometric for authentication.
  • Existing biometric systems may face challenges with universality, uniqueness, or permanence, highlighting the need for robust alternatives.
  • Accurate and efficient 3D ear data acquisition is crucial for developing reliable ear-based recognition systems.

Purpose of the Study:

  • To describe a specialized laser scanner for online 3D ear data acquisition.
  • To define and extract novel global and local feature classes from 3D ear images.
  • To evaluate the effectiveness of combining these global and local features for 3D ear recognition.

Main Methods:

  • Development and utilization of a custom laser scanner for high-fidelity 3D ear data capture.
  • Definition of two distinct feature sets: global features (empty centers, angles) and local features (points, lines, areas).
  • Optimal fusion strategy for combining global and local features to enhance recognition accuracy.

Main Results:

  • A large dataset of 2,000 3D ear samples was successfully collected using the developed scanner.
  • Experimental validation demonstrated the superior performance of the fused feature approach over individual feature sets.
  • An equal error rate (EER) of 2.2% was achieved, indicating high accuracy in 3D ear recognition.

Conclusions:

  • The proposed 3D ear scanning system and feature extraction methodology are effective for biometric authentication.
  • The fusion of global and local 3D ear features significantly improves recognition performance.
  • The achieved low equal error rate validates the potential of 3D ear biometrics for secure identification.