  A biased dataset is a dataset that generally has attributes with an uneven
class distribution. These biases have the tendency to propagate to the models
that train on them, often leading to a poor performance in the minority class.
In this project, we will explore the extent to which various data augmentation
methods alleviate intrinsic biases within the dataset. We will apply several
augmentation techniques on a sample of the UTKFace dataset, such as
undersampling, geometric transformations, variational autoencoders (VAEs), and
generative adversarial networks (GANs). We then trained a classifier for each
of the augmented datasets and evaluated their performance on the native test
set and on external facial recognition datasets. We have also compared their
performance to the state-of-the-art attribute classifier trained on the
FairFace dataset. Through experimentation, we were able to find that training
the model on StarGAN-generated images led to the best overall performance. We
also found that training on geometrically transformed images lead to a similar
performance with a much quicker training time. Additionally, the best
performing models also exhibit a uniform performance across the classes within
each attribute. This signifies that the model was also able to mitigate the
biases present in the baseline model that was trained on the original training
set. Finally, we were able to show that our model has a better overall
performance and consistency on age and ethnicity classification on multiple
datasets when compared with the FairFace model. Our final model has an accuracy
on the UTKFace test set of 91.75%, 91.30%, and 87.20% for the gender, age, and
ethnicity attribute respectively, with a standard deviation of less than 0.1
between the accuracies of the classes of each attribute.
