  The development of deep learning algorithms has extensively empowered
humanity's task automatization capacity. However, the huge improvement in the
performance of these models is highly correlated with their increasing level of
complexity, limiting their usefulness in human-oriented applications, which are
usually deployed in resource-constrained devices. This led to the development
of compression techniques that drastically reduce the computational and memory
costs of deep learning models without significant performance degradation. This
paper aims to systematize the current literature on this topic by presenting a
comprehensive survey of model compression techniques in biometrics
applications, namely quantization, knowledge distillation and pruning. We
conduct a critical analysis of the comparative value of these techniques,
focusing on their advantages and disadvantages and presenting suggestions for
future work directions that can potentially improve the current methods.
Additionally, we discuss and analyze the link between model bias and model
compression, highlighting the need to direct compression research toward model
fairness in future works.
