Exemplar-based facial attribute manipulation: a review

Facial attribute manipulation gained a lot of attention when deep learning algorithms made amazing achievements during the last few years. Facial attribute manipulation is the process of combining or removing desired facial characteristics for a given image. Recently, generative adversarial networks (GANs) and encoder-decoder architecture have been used to tackle this problem, with promising results. We present a comprehensive overview of deep facial attribute analysis from the perspectives of manipulation using exemplars in this study. The model construction approaches, datasets, and performance evaluation measures that are frequently utilised are discussed. Following this, a review of various homogeneous and heterogeneous exemplar-based facial attribute manipulation algorithms is presented in detail. Furthermore, several other facial attribute-related issues and related applications in the real world, are also discussed. Lastly, we go over some of the issues that can arise as well as some interesting future research directions.