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This paper presents a summary of the 2019 Unconstrained Ear RecognitionChallenge (UERC), the second in a series of group benchmarking efforts centeredaround the problem of person recognition from ear images captured inuncontrolled settings. The goal of the challenge is to assess the performanceof existing ear recognition techniques on a challenging large-scale ear datasetand to analyze performance of the technology from various viewpoints, such asgeneralization abilities to unseen data characteristics, sensitivity torotations, occlusions and image resolution and performance bias on sub-groupsof subjects, selected based on demographic criteria, i.e. gender and ethnicity.Research groups from 12 institutions entered the competition and submitted atotal of 13 recognition approaches ranging from descriptor-based methods todeep-learning models. The majority of submissions focused on ensemble basedmethods combining either representations from multiple deep models orhand-crafted with learned image descriptors. Our analysis shows that methodsincorporating deep learning models clearly outperform techniques relying solelyon hand-crafted descriptors, even though both groups of techniques exhibitsimilar behaviour when it comes to robustness to various covariates, suchpresence of occlusions, changes in (head) pose, or variability in imageresolution. The results of the challenge also show that there has beenconsiderable progress since the first UERC in 2017, but that there is stillample room for further research in this area.

The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix