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@INPROCEEDINGS{George_IJCB2023-3_2023,
         author = {George, Anjith and Marcel, S{\'{e}}bastien},
       projects = {Idiap},
          title = {The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias},
      booktitle = {IEEE International Joint Conference on Biometrics (IJCB 2023)},
           year = {2023},
       abstract = {The paper provides a summary of the 2023 Uncon- strained Ear Recognition Challenge (UERC), a benchmark- ing effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the chal- lenge was to evaluate the effectiveness of current ear recog- nition techniques on a challenging ear dataset while ana- lyzing the techniques from two distinct aspects, i.e., veri- fication performance and bias with respect to specific de- mographic factors, i.e., gender and ethnicity. Seven re- search groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to en- semble techniques that relied on multiple data representa- tions to maximize performance and minimize bias. A com- prehensive investigation into the performance of the submit- ted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differ- ences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capa- ble of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://ears.fri.uni-lj.si/.},
            pdf = {https://publications.idiap.ch/attachments/papers/2023/George_IJCB2023-3_2023.pdf}
}