Publications

@inproceedings{emervsivc2017unconstrained,

title = {The unconstrained ear recognition challenge},

author = {Žiga Emeršič and Dejan Štepec and Vitomir Štruc and Peter Peer and Anjith George and Adii Ahmad and Elshibani Omar and Terrance E. Boult and Reza Safdaii and Yuxiang Zhou and others Stefanos Zafeiriou and Dogucan Yaman and Fevziye I. Eyoikur and Hazim K. Ekenel},

url = {https://arxiv.org/pdf/1708.06997.pdf},

year  = {2017},

date = {2017-09-01},

booktitle = {2017 IEEE International Joint Conference on Biometrics (IJCB)},

pages = {715--724},

organization = {IEEE},

abstract = {In this paper we present the results o f the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem o f person recognition from ear images captured in uncontrolled conditions. The goal o f the challenge was to assess the performance of existing ear recognition techniques on a challenging largescale 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 fo r 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 o f the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer o f the UERC was found to ensure robust performance on a smaller part o f the dataset (with 180 subjects) regardless o f image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing.

},

keywords = {biometrics, competition, ear recognition, IJCB, uerc, unconstrained ear recognition challenge},

pubstate = {published},

tppubtype = {inproceedings}

}