  This work investigates the impact of fun selfie filters, which are frequently
used to modify selfies, on face recognition systems. Based on a qualitative
assessment and classification of freely available mobile applications, ten
relevant fun selfie filters are selected to create a database. To this end, the
selected filters are automatically applied to face images of public face image
databases. Different state-of-the-art methods are used to evaluate the
influence of fun selfie filters on the performance of face detection using
dlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet and
MagFace, and recognition accuracy employing ArcFace and a COTS algorithm. The
obtained results indicate that selfie filters negatively affect face
recognition modules, especially if fun selfie filters cover a large region of
the face, where the mouth, nose, and eyes are covered. To mitigate such
unwanted effects, a GAN-based selfie filter removal algorithm is proposed which
consists of a segmentation module, a perceptual network, and a generation
module. In a cross-database experiment the application of the presented selfie
filter removal technique has shown to significantly improve the biometric
performance of the underlying face recognition systems.
