# The Unconstrained Ear Recognition Challenge

arXiv: https://arxiv.org/abs/1708.06997 · HTML: https://mlsanity.com/paper/1708.06997/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [Multimodal Personal Ear Authentication Using Smartphones](https://mlsanity.com/paper/2103.12575/) (2021)
- [AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio](https://mlsanity.com/paper/2301.12613/) (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)

## Abstract (arXiv)

In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions. The goal of the challenge was to assess the performance of existing ear recognition techniques on a challenging large-scale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques for the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity of the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer of the UERC was found to ensure robust performance on a smaller part of the dataset (with 180 subjects) regardless of image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing.