The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias∗

Z. Emersic*, T. Ohki, M. Akasaka, T. Arakawa, S. Maeda, M. Okano, Y. Sato, A. George, S. Marcel, I. I. Ganapathi, S. S. Ali, S. Javed, N. Werghi, S. G. Isik, E. Saritas, H. K. Ekenel, V. Hudovernik, J. N. Kolf, F. Boutros, N. DamerG. Sharma, A. Kamboj, A. Nigam, D. K. Jain, G. Camara-Chavez, P. Peer, V. Struc

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://ears.fri.uni-lj.si/

Original languageEnglish
Title of host publication2023 IEEE International Joint Conference on Biometrics, IJCB 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350337266
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Joint Conference on Biometrics, IJCB 2023 - Ljubljana, Slovenia
Duration: 25 Sept 202328 Sept 2023

Publication series

Name2023 IEEE International Joint Conference on Biometrics, IJCB 2023

Conference

Conference2023 IEEE International Joint Conference on Biometrics, IJCB 2023
Country/TerritorySlovenia
CityLjubljana
Period25/09/2328/09/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Fingerprint

Dive into the research topics of 'The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias∗'. Together they form a unique fingerprint.

Cite this