Ear recognition: More than a survey

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, descriptorbased 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 stateof-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. Index Terms-biometry, dataset, in-the-wild, unconstrained image, descriptor-based method, open-source toolbox, ear recognition• Survey: We present a comprehensive survey on ear recognition, which is meant to provide researchers in this field with a recent and up-to-date overview of the state-of-technology. We introduce a taxonomy of the existing 2D ear recognition approaches, discuss the characteristics of the technology and review the existing state-of-the-art. Most importantly, we elaborate on the open problems and challenges faced by the technology. • Dataset: We make a new dataset of ear images available to the research community. The dataset, named Annotated Web Ears (AWE), contains images collected from the web and is to the best of our knowledge the first dataset for ear recognition gathered "in the wild". The images of the AWE dataset contain a

Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild

“…The goal of this section is to provide the reader with a brief high-level overview of existing techniques in the field of ear recognition and CNN-based recognition models. For a comprehensive overview of the two areas, the reader is referred to in-depth surveys on these topics [10], [19].…”

“…Ear recognition: According to Emeršič et al [19] existing techniques for ear recognition can conveniently be grouped into i) geometric, ii) holistic, iii) local, and iv) hybrid techniques.…”

“…Since only information related to the ear geometry is used, it is straightforward to devise methods that are invariant to geometric distortions, such as rotation, scaling or even small perspective changes. Examples of techniques from this group were presented in [19], [20].…”

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