  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.
