Augmented Reality or AR filters on selfies have become very popular on social media platforms for a variety of applications, including marketing, entertainment and aesthetics. Given the wide adoption of AR face filters and the importance of faces in our social structures and relations, there is increased interest by the scientific community to analyze the impact of such filters from a psychological, artistic and sociological perspective. However, there are few quantitative analyses in this area mainly due to a lack of publicly available datasets of facial images with applied AR filters. The proprietary, close nature of most social media platforms does not allow users, scientists and practitioners to access the code and the details of the available AR face filters. Scraping faces from these platforms to collect data is ethically unacceptable and should, therefore, be avoided in research. In this paper, we present OpenFilter, a flexible framework to apply AR filters available in social media platforms on existing large collections of human faces. Moreover, we share FairBeauty and B-LFW, two beautified versions of the publicly available FairFace and LFW datasets and we outline insights derived from the analysis of these beautified datasets.
翻译:由于广泛采用AR脸过滤器以及脸部在我们社会结构和关系中的重要性,科学界越来越有兴趣从心理、艺术和社会学的角度分析这些过滤器的影响。然而,这一领域几乎没有数量分析,这主要是由于缺乏公开的面部图像数据集和应用的AR过滤器。大多数社交媒体平台的专有性、近质不允许用户、科学家和从业者获得现有AR脸部过滤器的代码和细节。从这些平台上提取面部以收集数据的做法在道德上是不可接受的,因此,在研究中应当避免。在本文件中,我们介绍了OpenFilter,一个灵活框架,将社会媒体平台上现有的抗逆转录病毒过滤器应用于现有的大量人类面部。此外,我们分享了FairBeauty和B-LFW,两个公开提供的FairFace和LFW数据集的美化版本,我们概述了从这些分析中得出的见解。