Advancing Ear Biometrics: Enhancing Accuracy and Robustness through Deep Learning

TL;DR

This paper enhances ear biometric identification by applying deep learning techniques, achieving high accuracy and robustness across multiple datasets, and demonstrating its potential as a reliable alternative to face and fingerprint recognition.

Contribution

The study introduces a deep learning-based ear biometric system that significantly improves accuracy and robustness over previous methods, using data augmentation and preprocessing techniques.

Findings

Achieved 99.35% accuracy on AMI dataset

Achieved 98.1% accuracy on EarNV1.0 dataset

Demonstrated robustness against variations in lighting and expressions

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.