Ear Recognition: More Than a Survey

TL;DR

This paper reviews recent advances in automatic ear recognition from 2D images, discusses open challenges, and introduces a new dataset and toolbox to facilitate future research in the field.

Contribution

It provides a comprehensive overview of descriptor-based ear recognition methods and introduces a new dataset and toolbox to support ongoing research.

Findings

Review of recent descriptor-based methods

Identification of open challenges in ear recognition

Introduction of a new publicly available dataset and toolbox

Abstract

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.