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By embracing Generative Adversarial Networks (GAN), several face-related applications have significantly benefited and achieved unparalleled success. Inspired by the latest advancement in GAN, we propose the PlasticGAN which is a holistic framework for generating images of post-surgery faces as well as reconstruction of faces after surgery completion. This preliminary model works as a helping hand in assisting surgeons, biometric researchers, and practitioners in clinical decision-making by identifying patient cohorts that require building up of confidence with the help of vivid visualizations prior to treatment. It helps them better provide the tentative alternatives by simulating aging patterns. We used the face recognition system for evaluating the same individual with and without masks on surgery face, keeping the current trends in mind such as forensic and security application and recent worldwide COVID scenario. The experimental results suggested that plastic surgery-based synthetic cross-age face recognition (PSBSCAFR) is an arduous research challenge, and state-of-art face recognition systems can negatively affect face recognition performance. This can present a new dimension for the research community.

PlasticGAN: Holistic generative adversarial network on face plastic and aesthetic surgery