  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.
