About |
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The task: The participants will be asked to develop their fair face verification method aiming for a reduced bias in terms of gender and skin color (protected attributes). The method developed by the participants will need to output a list of confidence scores given test ID pairs to be verified (either positive or negative matches). Both the bias and accuracy of the method will be evaluated using the provided face dataset (plus individual protected and legitimate attributes and other metadata, as well as identity ground truth annotations). |
Call for Papers |
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The competition will be run on CodaLab platform. Click here to access our 2020 ECCV ChaLearn Fair Face Recognition Challenge. The participants will need to register through the platform, where they will be able to access the data and submit their predicitions on the validation and test data (i.e., development and test phases) and to obtain real-time feedback on the leaderboard. The development and test phases will open/close automatically based on the defined schedule. |
Credits and Sources |
| [1] ChaLearn Looking at People : Looking at People Fair Face Recognition challenge ECCV |
