In this paper we present the results o f the Unconstrained Ear Recognition Challenge (UERC), a group benchmark ing effort centered around the problem o f person recogni tion from ear images captured in uncontrolled conditions. The goal o f the challenge was to assess the performance o f 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 recog nition techniques fo r the evaluation, while multiple base lines were made available fo r 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 en tire dataset comprising 3,704 subjects was used fo r testing.
Recently, a competition, unconstrained ear recognition challenge (UERC), was organized REF .
The unconstrained ear recognition challenge
2017 IEEE International Joint Conference on Biometrics (IJCB)
2017 IEEE International Joint Conference on Biometrics (IJCB)
Economics Computer Science