Fine-Grained Rebalancing of Datasets for Correct Demographic Classification

The use of face biometrics to automatically recognize people, though fascinating, often raises ethical concerns related to the composition of the datasets used for performance evaluation of the recognition approaches and the bias that stems from the possible demographic imbalance of age, gender, and ethnicity classes during the training phase. This study tackles such imbalance in face datasets and proposes an approach to fair age, gender, and ethnicity classification by training on finely rebalanced cohorts of face images. Special attention is devoted to the ethical aspects related to having face samples of real people vs. having synthetic face samples (generated with a Generative Adversarial Network - GAN - model). Therefore, the dataset rebalancing approach exploits synthetic images instead of real ones in order to decrease the possible privacy concerns raised by new image captures. The work further aims to demonstrate that gross re-balancing is insufficient to solve all the problems related to fair demographic classification, but a finer strategy is worth adopting. The experiments compare rebalancing the single demographic classes with a finer strategy considering classes characterized by combinations of features. This entails analyzing the imbalance of the different cohorts in the dataset and appropriately rebalancing them to evaluate the new performance.