The Unconstrained Ear Recognition Challenge
This challenge addresses the problem of person recognition from ear images in uncontrolled conditions for biometrics and security applications, but it is incremental as it benchmarks existing methods rather than introducing new ones.
The Unconstrained Ear Recognition Challenge evaluated existing ear recognition techniques on a large-scale dataset of ear images in uncontrolled conditions, finding that the top performer maintained robust performance on a subset of 180 subjects but experienced a significant drop when tested on the full dataset of 3,704 subjects.
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.