Automatic identity recognition from ear images represents an active field of
research within the biometric community. The ability to capture ear images from
a distance and in a covert manner makes the technology an appealing choice for
surveillance and security applications as well as other application domains.
Significant contributions have been made in the field over recent years, but
open research problems still remain and hinder a wider (commercial) deployment
of the technology. This paper presents an overview of the field of automatic
ear recognition (from 2D images) and focuses specifically on the most recent,
descriptor-based methods proposed in this area. Open challenges are discussed
and potential research directions are outlined with the goal of providing the
reader with a point of reference for issues worth examining in the future. In
addition to a comprehensive review on ear recognition technology, the paper
also introduces a new, fully unconstrained dataset of ear images gathered from
the web and a toolbox implementing several state-of-the-art techniques for ear
recognition. The dataset and toolbox are meant to address some of the open
issues in the field and are made publicly available to the research community.

本论文介绍了自动耳识别技术的最新进展，主要讨论了基于描述符的方法。除此之外，该论文还引入了一个包含多种现代技术的工具箱和一个完全自由的耳图像数据集，目的是解决领域中的一些问题并向研究社区公开提供。