ExgFair: A Crowdsourcing Data Exchange Approach to Fair Human Face Datasets Augmentation

Human face images represent a rich set of visual data information that is utilized by various big data driven human facial applications. However, the performance of these applications is usually biased towards the majority demographic group due to the data imbalance issue. In this paper, we focus on a fair human face data exchange problem where the goal is to exchange visual features of human face images between different human face datasets and obtain a set of augmented datasets that improve the fairness and performance of human facial applications. Our problem is motivated by the limitations of current fairness approaches that only focus on a single human face dataset from a particular application and require a large amount of pre-annotated demographic attribute labels to develop fair human facial models. To address these limitations, we develop ExgFair, a crowdsourcing-based fair data exchange framework to generate a set of augmented fair face image datasets by leveraging the crowdsourced demographic attribute labels of human face images. We evaluate ExgFair using a set of real-world human face image datasets with different demographic distributions. The results show that ExgFair not only reduces demographic biases of the datasets but also improves the accuracy of human facial applications trained on the augmented fair datasets.