Abstract
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.
Abstract (translated)
随着社交媒体的出现，有趣的自拍滤镜已经进入了 mainstream 使用，对面部生物特征系统和图像识别系统产生了重大影响。这些滤镜从美颜滤镜和基于增强现实 (AR) 的滤镜到修改面部特征的滤镜。因此，有必要评估这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，这些解决方案更关注美颜滤镜。然而，当前的 AR 基滤镜和扭曲面部关键点的滤镜最近很流行，使脸部高度难以识别，甚至对裸眼观察者来说也是如此。此外，考虑的滤镜大多是过时的，且变化有限。为了减轻这些限制，我们旨在对最新滤镜进行全面的评估，并提出了带有滤镜的用户识别模型。我们在基准图像上利用了基准数据集，并应用最新滤镜生成一个美颜/滤镜化数据集。接下来，我们引入了 FaceFilterNet 模型进行美颜用户识别。在这个框架下，我们还利用我们的模型来评论人员的各种属性，包括年龄、性别和种族。此外，我们还对面部识别、年龄估计、性别和种族预测进行了滤镜逐个影响分析。所提出的方法证实了我们的数据集的有效性，准确率为 87.25%，面部属性分析的最优准确率。
URL
https://arxiv.org/abs/2404.08277
