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This work investigates the impact of fun selfie filters, which are frequentlyused to modify selfies, on face recognition systems. Based on a qualitativeassessment and classification of freely available mobile applications, tenrelevant fun selfie filters are selected to create a database. To this end, theselected filters are automatically applied to face images of public face imagedatabases. Different state-of-the-art methods are used to evaluate theinfluence of fun selfie filters on the performance of face detection usingdlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet andMagFace, and recognition accuracy employing ArcFace and a COTS algorithm. Theobtained results indicate that selfie filters negatively affect facerecognition modules, especially if fun selfie filters cover a large region ofthe face, where the mouth, nose, and eyes are covered. To mitigate suchunwanted effects, a GAN-based selfie filter removal algorithm is proposed whichconsists of a segmentation module, a perceptual network, and a generationmodule. In a cross-database experiment the application of the presented selfiefilter removal technique has shown to significantly improve the biometricperformance of the underlying face recognition systems.

Fun Selfie Filters in Face Recognition: Impact Assessment and Removal