The AgeDB-30M Dataset: Melanated Faces for Age-Invariant Face Recognition

For the task of evaluating face recognition algorithms, the research community has adopted a set of de facto standard datasets. These datasets tend to emphasize “difficult pairs” – paired images chosen for differences in factors like age (as in AgeDB-30 and CALFW) and pose (as in CPLFW and CFP-FP). Difficult pairs allow for a more robust evaluation of algorithms, offering granular insight into the factors that are problematic for specific algorithms. However, the existing datasets ignore one factor that has historically proven highly challenging for many face recognition algorithms: race. The faces in these datasets are overwhelmingly White.In this work, we address this demographic gap with the curation of an all-Black dataset for evaluation: AgeDB-30M, where “M” indicates “melanated”. It is the first publicly-available dataset of difficult cross-age image pairs solely from the Black demographic. We hope that AgeDB-30M is a valuable tool for the research community, supporting continued efforts toward more robust algorithmic evaluation, particularly with respect to issues of bias and fairness.