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Advancing Ear Biometrics: Enhancing Accuracy and Robustness through Deep Learning

Youssef Mohamed, Zeyad Youssef, Ahmed Heakl, Ahmed Zaky

TL;DR

This work addresses reliable ear-based identity verification by applying deep learning with targeted preprocessing. It introduces zooming, contour detection, and augmentation to improve robustness across two datasets, AMI and EarVN1.0, and evaluates several CNN backbones under multiple data-enhancement strategies. The findings show that augmentation- and zooming-driven approaches yield substantial accuracy gains, with the highest testing performance around 99.35% on AMI and 98.1% on EarVN1.0, highlighting the potential of ear biometrics for accurate, noninvasive identification. The study also discusses limitations such as dataset size and image quality, and points to future work on larger, more varied datasets and advanced contour-edge techniques to further boost performance.

Abstract

Biometric identification is a reliable method to verify individuals based on their unique physical or behavioral traits, offering a secure alternative to traditional methods like passwords or PINs. This study focuses on ear biometric identification, exploiting its distinctive features for enhanced accuracy, reliability, and usability. While past studies typically investigate face recognition and fingerprint analysis, our research demonstrates the effectiveness of ear biometrics in overcoming limitations such as variations in facial expressions and lighting conditions. We utilized two datasets: AMI (700 images from 100 individuals) and EarNV1.0 (28,412 images from 164 individuals). To improve the accuracy and robustness of our ear biometric identification system, we applied various techniques including data preprocessing and augmentation. Our models achieved a testing accuracy of 99.35% on the AMI Dataset and 98.1% on the EarNV1.0 dataset, showcasing the effectiveness of our approach in precisely identifying individuals based on ear biometric characteristics.

Advancing Ear Biometrics: Enhancing Accuracy and Robustness through Deep Learning

TL;DR

This work addresses reliable ear-based identity verification by applying deep learning with targeted preprocessing. It introduces zooming, contour detection, and augmentation to improve robustness across two datasets, AMI and EarVN1.0, and evaluates several CNN backbones under multiple data-enhancement strategies. The findings show that augmentation- and zooming-driven approaches yield substantial accuracy gains, with the highest testing performance around 99.35% on AMI and 98.1% on EarVN1.0, highlighting the potential of ear biometrics for accurate, noninvasive identification. The study also discusses limitations such as dataset size and image quality, and points to future work on larger, more varied datasets and advanced contour-edge techniques to further boost performance.

Abstract

Biometric identification is a reliable method to verify individuals based on their unique physical or behavioral traits, offering a secure alternative to traditional methods like passwords or PINs. This study focuses on ear biometric identification, exploiting its distinctive features for enhanced accuracy, reliability, and usability. While past studies typically investigate face recognition and fingerprint analysis, our research demonstrates the effectiveness of ear biometrics in overcoming limitations such as variations in facial expressions and lighting conditions. We utilized two datasets: AMI (700 images from 100 individuals) and EarNV1.0 (28,412 images from 164 individuals). To improve the accuracy and robustness of our ear biometric identification system, we applied various techniques including data preprocessing and augmentation. Our models achieved a testing accuracy of 99.35% on the AMI Dataset and 98.1% on the EarNV1.0 dataset, showcasing the effectiveness of our approach in precisely identifying individuals based on ear biometric characteristics.
Paper Structure (20 sections, 8 figures, 3 tables)

This paper contains 20 sections, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Datasets
  4. AMI dataset
  5. EarVN1.0 Dataset
  6. Data Preprocessing
  7. Zooming
  8. Contour Detection
  9. Augmentation
  10. Methodology
  11. VGG16 & VGG19
  12. ResNet50
  13. MobileNet v2
  14. EfficientNet B7
  15. Experment and Result
  16. ...and 5 more sections

Figures (8)

  • Figure 1: Sample images from the AMI dataset.
  • Figure 2: Samples from the EarNV1.0 dataset for the same person.
  • Figure 3: Sample images before and after zooming.
  • Figure 4: Ear biometric preprocessing stages.
  • Figure 5: Ear Biometric Stages
  • ...and 3 more figures