The Unconstrained Ear Recognition Challenge

TL;DR

This paper reports on the Unconstrained Ear Recognition Challenge, benchmarking ear recognition methods on a large, uncontrolled dataset to identify strengths and open challenges in the field.

Contribution

It provides a comprehensive evaluation of multiple ear recognition techniques on a large-scale, unconstrained dataset, highlighting current limitations and future research directions.

Findings

Top method performs well on small datasets but drops significantly on larger datasets.

Recognition accuracy is sensitive to head rotation and image conditions.

The challenge identifies key open problems in unconstrained ear recognition.

Abstract

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.