Abstract
Facial filters are now commonplace for social media users around the world. Previous work has demonstrated that facial filters can negatively impact automated face recognition performance. However, these studies focus on small numbers of hand-picked filters in particular styles. In order to more effectively incorporate the wide ranges of filters present on various social media applications, we introduce a framework that allows for larger-scale study of the impact of facial filters on automated recognition. This framework includes a controlled dataset of face images, a principled filter selection process that selects a representative range of filters for experimentation, and a set of experiments to evaluate the filters' impact on recognition. We demonstrate our framework with a case study of filters from the American applications Instagram and Snapchat and the Chinese applications Meitu and Pitu to uncover cross-cultural differences. Finally, we show how the filtering effect in a face embedding space can easily be detected and restored to improve face recognition performance.
Abstract (translated)
面部滤镜现在已成为全球社交媒体用户的一种常见工具。先前的研究表明，面部滤镜可能对自动化面部识别的性能产生负面影响。然而，这些研究主要集中在少数特定风格的手选滤镜上。为了更有效地应对各种社交媒体应用中广泛存在的滤镜问题，我们引入了一个框架，该框架允许进行更大规模地研究面部滤镜对自动识别的影响。此框架包括一个受控的脸部图像数据集、一种有原则的滤镜选择过程（用于为实验选取具有代表性的滤镜范围），以及一系列实验来评估这些滤镜对面部识别性能的影响。 我们通过分析来自美国应用Instagram和Snapchat，以及中国应用美图秀秀(Meitu)和皮土(Pitu)的滤镜进行案例研究，以揭示跨文化差异。最后，我们展示了如何在面部嵌入空间中检测并修复滤镜效果，从而改善面部识别性能。
URL
https://arxiv.org/abs/2507.17729
