# Ear Recognition: More Than a Survey

arXiv: https://arxiv.org/abs/1611.06203 · HTML: https://mlsanity.com/paper/1611.06203/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2016.

## Built on by (most cited first, 15 of 15)

- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Depth as Attention for Face Representation Learning](https://mlsanity.com/paper/2101.00652/) (2021)
- [Deep Learning Approach for Ear Recognition and Longitudinal Evaluation in Children](https://mlsanity.com/paper/2408.01588/) (2024)
- [Improved Ear Verification with Vision Transformers and Overlapping Patches](https://mlsanity.com/paper/2503.23275/) (2025)
- [CBRW: A Novel Approach for Cancelable Biometric Template Generation based on](https://mlsanity.com/paper/2404.16739/) (2024)
- [Multimodal Personal Ear Authentication Using Smartphones](https://mlsanity.com/paper/2103.12575/) (2021)
- [FaceAtlasAR: Atlas of Facial Acupuncture Points in Augmented Reality](https://mlsanity.com/paper/2111.14755/) (2021)
- [Transfer learning using deep neural networks for Ear Presentation Attack Detection: New Database for PAD](https://mlsanity.com/paper/2112.05237/) (2021)
- [Deep Learning for Diagonal Earlobe Crease Detection](https://mlsanity.com/paper/2210.11582/) (2022)
- [Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation](https://mlsanity.com/paper/2211.02799/) (2022)
- [Deep Ear Biometrics for Gender Classification](https://mlsanity.com/paper/2308.08797/) (2023)
- [ProtoN: Prototype Node Graph Neural Network for Unconstrained Multi-Impression Ear Recognition](https://mlsanity.com/paper/2508.04381/) (2025)
- [How Does Bilateral Ear Symmetry Affect Deep Ear Features?](https://mlsanity.com/paper/2508.04614/) (2025)
- [PaW-ViT: A Patch-based Warping Vision Transformer for Robust Ear Verification](https://mlsanity.com/paper/2601.19771/) (2026)
- [Diffusion for De-Occlusion: Accessory-Aware Diffusion Inpainting for Robust Ear Biometric Recognition](https://mlsanity.com/paper/2601.19795/) (2026)

## Abstract (arXiv)

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.