Ear biometrics is an emerging area of research that aims to develop accurate and reliable systems for identifying individuals based on the unique features of their ears. Soft computing techniques, such as machine learning, artificial intelligence, fuzzy logic, and neural networks, are increasingly being used in ear biometrics to extract and analyze ear features, patterns, and shapes. The use of soft computing techniques has led to the development of more accurate and robust ear recognition systems, with applications in security, authentication, and identification. The use of deep learning algorithms, coupled with large datasets of ear images, has shown promising results in improving the accuracy of ear biometrics, as well as reducing the impact of noise and other variations. Applications of soft computing in ear biometrics have important implications for privacy, as ear recognition systems can potentially be used for mass surveillance. Therefore, the development of accurate and reliable ear biometrics systems must be balanced with ethical considerations and legal regulations. Overall, the use of soft computing techniques in ear biometrics is an important area of research with significant potential for improving security and identification in various domains.