Compressed Models Decompress Race Biases: What Quantized Models Forget
  for Fair Face Recognition
Pedro C. Neto Eduarda Caldeira Jaime S. Cardoso Ana F. Sequeira
With the ever-growing complexity of deep learning models for face
recognition, it becomes hard to deploy these systems in real life. Researchers
have two options: 1) use smaller models; 2) compress their current models.
Since the usage of smaller models might lead to concerning biases, compression
gains relevance. However, compressing might be also responsible for an increase
in the bias of the final model. We investigate the overall performance, the
performance on each ethnicity subgroup and the racial bias of a
State-of-the-Art quantization approach when used with synthetic and real data.
This analysis provides a few more details on potential benefits of performing
quantization with synthetic data, for instance, the reduction of biases on the
majority of test scenarios. We tested five distinct architectures and three
different training datasets. The models were evaluated on a fourth dataset
which was collected to infer and compare the performance of face recognition
models on different ethnicity.