  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.
