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VGGFace-Ear: An Extended Dataset for Unconstrained Ear Recognition.

Solange Ramos-Cooper1, Erick Gomez-Nieto1, Guillermo Camara-Chavez1,2

  • 1Department of Computer Science, Universidad Catolica San Pablo, Arequipa 04001, Peru.

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|March 10, 2022
PubMed
Summary

This study introduces a novel ear image dataset for unconstrained recognition, addressing the need for large datasets in security applications. Transfer learning with convolutional neural networks (CNNs) shows promise for accurate ear identification.

Keywords:
VGGFaceconvolutional neural networksdeep learningear biometricsear recognitionmask-RCNNtransfer learning

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision
  • Artificial Intelligence

Background:

  • Ear biometrics offer unique advantages for identification, including contactless capture and distance accessibility, making them suitable for surveillance and security.
  • Traditional biometric methods often require large, labeled datasets for training, which are scarce for ear images captured in uncontrolled environments.
  • Convolutional Neural Networks (CNNs) are powerful tools for image recognition but are data-hungry, posing a challenge for ear recognition tasks.

Purpose of the Study:

  • To develop and introduce a new, large-scale dataset of ear images captured under uncontrolled conditions, exhibiting significant inter-class and intra-class variability.
  • To evaluate the effectiveness of transfer learning using pre-trained CNNs for ear recognition on unconstrained datasets.
  • To establish a benchmark for future research in unconstrained ear biometrics.

Main Methods:

  • A novel ear image dataset was constructed by leveraging the existing VGGFace dataset, comprising over 3.3 million images.
  • Transfer learning was employed, utilizing CNN models pre-trained on large-scale image and face recognition tasks.
  • Two distinct experiments were conducted on separate unconstrained ear image datasets to assess recognition performance.

Main Results:

  • The developed ear image dataset demonstrates high variability, reflecting real-world conditions.
  • Transfer learning with pre-trained CNNs achieved promising results in ear recognition tasks on unconstrained datasets.
  • Rank-based metrics were utilized to report performance, providing a quantitative evaluation of the proposed method.

Conclusions:

  • The newly created ear image dataset is a valuable resource for advancing research in unconstrained ear biometrics.
  • Transfer learning offers an effective approach to overcome the data scarcity issue in ear recognition using CNNs.
  • The findings support the potential of ear recognition as a viable biometric modality for security and surveillance applications.