The paper provides a summary of the 2023 Unconstrained Earrecognition Challenge (UErC), a benchmarking effort focused on earrecognition from images acquired in uncontrolled environments. The objective of the challen...
The paper provides a summary of the 2023 Unconstrained Earrecognition Challenge (UErC), a benchmarking effort focused on earrecognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current earrecognition techniques on a challenging eardataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods anddeep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences 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 capable 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 furtherdevelopment of unbiased and effective earrecognition models, the starter kit of UErC 2023 together with the baseline model, and training and test data is made available from: http://***/
The paper provides a summary of the 2023 Unconstrained Earrecognition Challenge (UErC), a benchmarking effort focused on earrecognition from images acquired in uncontrolled environments. The objective of the challen...
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ISBN:
(纸本)9798350337266
The paper provides a summary of the 2023 Unconstrained Earrecognition Challenge (UErC), a benchmarking effort focused on earrecognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current earrecognition techniques on a challenging eardataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods anddeep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences 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 capable 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 furtherdevelopment of unbiased and effective earrecognition models, the starter kit of UErC 2023 together with the baseline model, and training and test data is made available from: http://***/.
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