<p>This work addresses both race- and age-based imbalances in facial recognition, aiming to mitigate disparate error rates across underrepresented demographics. We refine our original framework through iterative data augmentation, combining standard transformations and GAN-based synthesis while introducing an embedding-based filter to discard artifacts or near-duplicates. Fairness is measured using per-subgroup Equalized Odds and F1-scores, comparing incremental retraining strategies, targeting the worst-performing age-ethnicity subgroups, to larger static merges of UTKFace and FairFace. Results show that boosting a single subgroup can yield local gains but often triggers a cyclical “whack-a-mole” pattern, wherein new disparities emerge elsewhere. Although StyleGAN-generated samples can narrow bias for specific subgroups, they sometimes inherit generative limitations and may not stabilize global performance. In contrast, merging balanced datasets or introducing large, diverse synthetic sets leads to more consistent fairness gains, though complete parity remains elusive. Overall, our findings demonstrate that while targeted data injections can alleviate certain imbalances, true equity requires broader measures such as robust data balancing and refined generation techniques. Future work should further explore adversarial debiasing, conditional GAN training, and dynamic reweighting to sustain fairness improvements across varied demographic settings.</p>