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.

本研究关注耳部生物特征识别，通过利用其独特特征来提高准确性、可靠性和实用性，并证明了耳部生物特征识别在克服面部表情和光照条件变化等局限性方面的有效性。通过数据预处理和增强等技术，我们的模型在 AMI 数据集上实现了 99.35% 的测试准确率，并在 EarNV1.0 数据集上实现了 98.1% 的测试准确率，展示了我们方法在基于耳部生物特征的个体识别方面的有效性。