  This paper presents a summary of the 2019 Unconstrained Ear Recognition
Challenge (UERC), the second in a series of group benchmarking efforts centered
around the problem of person recognition from ear images captured in
uncontrolled settings. The goal of the challenge is to assess the performance
of existing ear recognition techniques on a challenging large-scale ear dataset
and to analyze performance of the technology from various viewpoints, such as
generalization abilities to unseen data characteristics, sensitivity to
rotations, occlusions and image resolution and performance bias on sub-groups
of subjects, selected based on demographic criteria, i.e. gender and ethnicity.
Research groups from 12 institutions entered the competition and submitted a
total of 13 recognition approaches ranging from descriptor-based methods to
deep-learning models. The majority of submissions focused on ensemble based
methods combining either representations from multiple deep models or
hand-crafted with learned image descriptors. Our analysis shows that methods
incorporating deep learning models clearly outperform techniques relying solely
on hand-crafted descriptors, even though both groups of techniques exhibit
similar behaviour when it comes to robustness to various covariates, such
presence of occlusions, changes in (head) pose, or variability in image
resolution. The results of the challenge also show that there has been
considerable progress since the first UERC in 2017, but that there is still
ample room for further research in this area.
