The use of Biometric Facial Recognition (FR) systems have become increasingly widespread, especially since the advent of deep neural network-based architectures. Although FR systems provide substantial benefits in terms of security and safety, the use of these systems also raises significant privacy concerns. This article discusses recent advances in facial identity hiding techniques, focusing on privacy protection approaches that hide or protect facial biometric data before camera devices capture the data. Moreover, we also discuss the state-of-the-art methods used to evaluate such privacy protection techniques. The primary motivation of this survey is to assess the relative performance of facial privacy protection methods and identify open challenges and future work that needs to be considered in this research area.

生物特征面部识别（FR）系统的使用已经变得越来越广泛，特别是自从基于深度神经网络的架构出现以来。尽管 FR 系统在安全性和安全性方面提供了巨大的好处，但这些系统的使用也引起了严重的隐私问题。本文讨论了面部身份隐藏技术的最新进展，重点关注在摄像头设备捕获数据之前隐藏或保护面部生物特征数据的隐私保护方法。此外，我们还讨论了用于评估此类隐私保护技术的最先进方法。这项调查的主要动机是评估面部隐私保护方法的相对性能，并确定该研究领域需要考虑的开放挑战和未来工作。