Background/Motivation:
Face-based age estimation is central to many applications, such as crime prevention, identity verification, youth protection, and also in the medical field. Age estimation systems often show different performance on subgroups (e.g., regarding age, gender, ethnic affiliation). Reasons include, on the one hand, the availability or balance of training data and, on the other hand, classical training methods that optimise global metrics and ignore problems in certain subgroups.
Techniques such as oversampling or probabilistic sampling attempt to create a balance in the training data through statistical analysis in advance, with the hope that this will result in uniform performance across all subgroups. However, the result of the measure usually does not ...