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A comprehensive survey and deep learning-based approach for human recognition using ear biometric.

Aman Kamboj1, Rajneesh Rani1, Aditya Nigam2

  • 1National Institute of Technology Jalandhar, Jalandhar, Punjab 144011 India.

The Visual Computer
|April 28, 2021
PubMed
Summary

This study surveys ear biometrics, highlighting its advantages for security. A new database and deep learning models are introduced for improved unconstrained ear recognition, advancing biometric security solutions.

Keywords:
BiometricDeep learningDetectionEarHandcraftedRecognitionUnconstrainedWild

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Area of Science:

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

Background:

  • Biometric systems are crucial for security and privacy, with the human ear offering unique advantages over other traits.
  • Existing ear biometrics methods excel in constrained environments but struggle with real-world challenges.

Purpose of the Study:

  • To provide a comprehensive survey of ear biometrics, including databases, evaluation metrics, and current approaches.
  • To introduce a novel database (NITJEW) for evaluating unconstrained ear detection and recognition.
  • To propose modified deep learning models for enhanced ear recognition performance.

Main Methods:

  • A novel taxonomy is used for a comprehensive survey of existing ear biometric research.
  • The NITJEW database is introduced for unconstrained ear biometrics.
  • Modified Faster-RCNN and VGG-19 deep learning models are employed for ear detection and recognition, respectively.

Main Results:

  • The survey provides an in-depth analysis of ear biometric databases, performance metrics, and methodologies.
  • The NITJEW database facilitates benchmark comparisons with six existing popular databases.
  • Modified deep learning models demonstrate effectiveness in unconstrained ear detection and recognition tasks.

Conclusions:

  • Ear biometrics presents a promising avenue for secure human recognition, particularly in unconstrained settings.
  • The developed database and models offer a foundation for future research and development in ear biometrics.
  • Open research problems are identified to guide future advancements in the field.