Fun Selfie Filters in Face Recognition: Impact Assessment and Removal
This addresses a practical problem for biometric security and social media applications by mitigating filter-induced errors in face recognition, though it is incremental as it builds on existing GAN and segmentation techniques.
The study assessed how fun selfie filters degrade face recognition systems, finding that filters covering large facial areas like the mouth, nose, and eyes cause significant performance drops, and proposed a GAN-based removal method that improved recognition accuracy in cross-database tests.
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.