Emeršić, Ž. ; Ohki, T. ; Akasaka, M. ; Arakawa, T. ; Maeda, S. ; Okano, M. ; Sato, Y. ; George, A. ; Marcel, S. ; Ganapathi, I. I. ; Ali, S. S. ; Javed, S. ; Werghi, N. ; Işık, S. G. ; Sarıtaş, E. ; Ekenel, H. K. ; Hudovernik, V. ; Kolf, J. N. ; Boutros, F. ; Damer, N. ; Sharma, G. ; Kamboj, A. ; Nigam, A. ; Jain, D. K. ; Cámara-Chávez, G. ; Peer, P. ; Štruc, V. (2023)
The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias*.
International Joint Conference on Biometrics 2023. Ljubljana, Slovenia (25.-28.9.2023)
doi: 10.1109/IJCB57857.2023.10449062
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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/
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Emeršić, Ž. ; Ohki, T. ; Akasaka, M. ; Arakawa, T. ; Maeda, S. ; Okano, M. ; Sato, Y. ; George, A. ; Marcel, S. ; Ganapathi, I. I. ; Ali, S. S. ; Javed, S. ; Werghi, N. ; Işık, S. G. ; Sarıtaş, E. ; Ekenel, H. K. ; Hudovernik, V. ; Kolf, J. N. ; Boutros, F. ; Damer, N. ; Sharma, G. ; Kamboj, A. ; Nigam, A. ; Jain, D. K. ; Cámara-Chávez, G. ; Peer, P. ; Štruc, V. |
| Art des Eintrags: | Bibliographie |
| Titel: | The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias* |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Verlag: | IEEE |
| Buchtitel: | 2023 IEEE International Joint Conference on Biometrics (IJCB) |
| Veranstaltungstitel: | International Joint Conference on Biometrics 2023 |
| Veranstaltungsort: | Ljubljana, Slovenia |
| Veranstaltungsdatum: | 25.-28.9.2023 |
| DOI: | 10.1109/IJCB57857.2023.10449062 |
| Kurzbeschreibung (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/ |
| Freie Schlagworte: | Biometrics, Machine learning, Deep learning, Ear recognition, Fairness |
| Zusätzliche Informationen: | *This research was supported in parts by the ARRS Research Programmes P2-0250(B) “Metrology and Biometric Systems”, P2-0214 “Computer Vision”, and TUBITAK Research Programmes 120N011, 2210 “Graduate Scholarship Program” and Turkcell Research Scholarship Program |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Apr 2024 10:38 |
| Letzte Änderung: | 12 Apr 2024 10:38 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum