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.

深度学习算法在提高人类任务自动化能力方面发挥了重要作用，然而，这些模型性能的巨大提升与其日益复杂性高度相关，限制了它们在以人为本的应用中的实用性，而这些应用通常部署在资源受限的设备上。因此，我们需要压缩技术来大幅减小深度学习模型的计算和内存成本，同时又能保持较高的性能。本文通过对模型压缩技术的综述，特别关注量化、知识蒸馏和修剪等方法在生物特征识别应用中的应用，从而对该领域的研究现状进行了系统化总结。我们对这些技术的比较价值进行了关键分析，着重讨论了它们的优点和缺点，并提出了进一步研究方向的建议，以改进当前的方法。此外，我们还讨论和分析了模型偏见与模型压缩之间的联系，强调了未来的研究需要将压缩技术引向模型公平性的方向。