Face Recognition after Plastic Surgery: A Comprehensive Study

It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery, which thus poses a new challenge to automatic face recognition. This paper first gives a comprehensive study on Face Recognition After Plastic Surgery (FRAPS), with careful analysis of the effects of plastic surgery on face appearance and its challenges to face recognition. Then, to address FRAPS problem, an ensemble of Gabor Patch classifiers via Rank-Order list Fusion (GPROF) is proposed, inspired by the assumption of the interior consistency of face components in terms of identity. On the face database of plastic surgery, GPROF achieves much higher face identification rate than the best known results in the literature. Furthermore, with our impressive results, we suggest that plastic surgery detection should be paid more attend to. To address this problem, a partial matching based plastic surgery detection algorithm is proposed, aiming to detect four distinct types of surgery, i.e., the eyelid surgery, nose surgery, forehead surgery and face lift surgery. Our experimental results demonstrate that plastic surgery detection is a nontrivial task, and thus deserves more research efforts.