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.

翻译：生物特征识别是一种基于个体独特的生理或行为特征进行身份验证的可靠方法，为密码或个人识别码等传统方法提供了更安全的替代方案。本研究聚焦于耳部生物特征识别，利用其独特特征以提升准确性、可靠性和可用性。以往研究通常关注人脸识别和指纹分析，而我们的研究证明了耳部生物特征在克服面部表情变化和光照条件限制等方面的有效性。我们使用了两个数据集：AMI（来自100个个体的700张图像）和EarNV1.0（来自164个个体的28,412张图像）。为提升耳部生物特征识别系统的准确性和鲁棒性，我们应用了包括数据预处理和增强在内的多种技术。我们的模型在AMI数据集上取得了99.35%的测试准确率，在EarNV1.0数据集上取得了98.1%的测试准确率，这证明了我们的方法在基于耳部生物特征精确识别个体方面的有效性。