Dense descriptor-based feature extraction techniques represent a popular choice for implementing biometric ear recognition system and are in general considered to be the current state-of-the-art in this area. In this paper, we study the impact of various factors (i.e., head rotation, presence of occlusions, gender and ethnicity) on the performance of 8 state-of-the-art descriptor-based ear recognition techniques. Our goal is to pinpoint weak points of the existing technology and identify open problems worth exploring in the future. We conduct our covariate analysis through identification experiments on the challenging AWE (Annotated Web Ears) dataset and report our findings. The results of our study show that high degrees of head movement and presence of accessories significantly impact the identification performance, whereas mild degrees of the listed factors and other covariates such as gender and ethnicity impact the identification performance only to a limited extent.
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