With the advent of social media, fun selfie filters have come into tremendous mainstream use affecting the functioning of facial biometric systems as well as image recognition systems. These filters vary from beautification filters and Augmented Reality (AR)-based filters to filters that modify facial landmarks. Hence, there is a need to assess the impact of such filters on the performance of existing face recognition systems. The limitation associated with existing solutions is that these solutions focus more on the beautification filters. However, the current AR-based filters and filters which distort facial key points are in vogue recently and make the faces highly unrecognizable even to the naked eye. Also, the filters considered are mostly obsolete with limited variations. To mitigate these limitations, we aim to perform a holistic impact analysis of the latest filters and propose an user recognition model with the filtered images. We have utilized a benchmark dataset for baseline images, and applied the latest filters over them to generate a beautified/filtered dataset. Next, we have introduced a model FaceFilterNet for beautified user recognition. In this framework, we also utilize our model to comment on various attributes of the person including age, gender, and ethnicity. In addition, we have also presented a filter-wise impact analysis on face recognition, age estimation, gender, and ethnicity prediction. The proposed method affirms the efficacy of our dataset with an accuracy of 87.25% and an optimal accuracy for facial attribute analysis.

翻译：随着社交媒体的兴起，趣味自拍滤镜已广泛应用于主流场景，影响了面部生物特征系统及图像识别系统的功能。这些滤镜涵盖美颜滤镜、基于增强现实（AR）的滤镜以及修改面部关键点的滤镜。因此，有必要评估此类滤镜对现有面部识别系统性能的影响。现有解决方案的局限性在于，它们更侧重于美颜滤镜。然而，当前流行的AR滤镜及扭曲面部关键点的滤镜甚至会使面部难以被肉眼识别。此外，所考虑的滤镜大多已过时且种类有限。为克服这些限制，我们旨在对最新滤镜进行整体影响分析，并提出一个基于滤镜图像的用户识别模型。我们利用基准数据集获取原始图像，并对其应用最新滤镜以生成美化/滤镜数据集。随后，我们引入了一个名为FaceFilterNet的模型用于美化用户识别。在该框架中，我们还利用该模型对人物的多种属性（包括年龄、性别和种族）进行注释。此外，我们分别针对滤镜对人脸识别、年龄估计、性别及种族预测的影响进行了分析。所提方法验证了我们数据集的有效性，在人脸识别中达到了87.25%的准确率，并在面部属性分析中取得了最优性能。