Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development.

由于对安全和隐私的日益关注，基于生物特征的人类识别系统的需求量很大。人耳是独一无二的，对识别很有用。与流行的生物特征特征面部、虹膜和指纹相比，它具有许多优势。很多工作都归功于耳朵生物识别，现有的方法在受限数据库上取得了显着的成功。然而，在不受约束的环境中，随着图像经历各种挑战，观察到了显着的难度。在本文中，我们首先使用一种新的分类法对耳朵生物特征进行了全面调查。该调查包括数据库、性能评估参数和现有方法的深入细节。我们引入了一个新的数据库 NITJEW，用于评估不受约束的耳朵检测和识别。改进的深度学习模型 Faster-RCNN 和 VGG-19 分别用于耳朵检测和耳朵识别任务。我们的数据库的基准比较评估是使用六个现有的流行数据库进行的。最后，我们提供了对在不久的将来值得研究的开放式研究问题的见解。我们希望我们的工作将成为耳部生物识别技术新研究人员的垫脚石，并有助于进一步发展。