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Biometric recognition using 3D ear shape.

Ping Yan1, Kevin W Bowyer

  • 1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame. In 46556, USA. pyan@cse.nd.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 15, 2007
PubMed
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This study introduces an automated ear biometrics system for identification, overcoming previous manual preprocessing limitations. The novel system achieves high accuracy in recognizing individuals using ear biometrics.

Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • The human ear is a viable biometric trait for identification.
  • Prior ear biometrics research faced challenges with manual image preprocessing, hair, and earrings.
  • Automated systems are needed to improve robustness and efficiency.

Purpose of the Study:

  • To develop and evaluate a complete, automated system for ear biometrics.
  • To address limitations of previous ear image preprocessing and recognition algorithms.
  • To establish a new benchmark in ear biometrics performance.

Main Methods:

  • Automated segmentation of the ear from profile view images.
  • 3D shape matching algorithms for ear recognition.
  • Evaluation on the largest experimental dataset in ear biometrics to date.

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Main Results:

  • Achieved a 97.8% rank-one recognition rate in an identification scenario.
  • Obtained an equal error rate of 1.2% in a verification scenario.
  • Tested on a database comprising 415 subjects and 1,386 total probes.

Conclusions:

  • The proposed automated ear biometrics system demonstrates high accuracy and robustness.
  • This system effectively handles challenges posed by hair and earrings.
  • The findings support the ear as a reliable biometric modality for security applications.