TY - GEN
T1 - The unconstrained ear recognition challenge
AU - Emeršič, Žiga
AU - Štepec, Dejan
AU - Štruc, Vitomir
AU - Peer, Peter
AU - George, Anjith
AU - Ahmad, Adii
AU - Omar, Elshibani
AU - Boult, Terranee E.
AU - Safdaii, Reza
AU - Zhou, Yuxiang
AU - Zafeiriou, Stefanos
AU - Yaman, Dogucan
AU - Eyiokur, Fevziye I.
AU - Ekenel, Hazim K.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85046271221&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046271221&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2017.8272761
DO - 10.1109/BTAS.2017.8272761
M3 - Conference contribution
AN - SCOPUS:85046271221
T3 - IEEE International Joint Conference on Biometrics, IJCB 2017
SP - 715
EP - 724
BT - IEEE International Joint Conference on Biometrics, IJCB 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Joint Conference on Biometrics, IJCB 2017
Y2 - 1 October 2017 through 4 October 2017
ER -