Makeup Removal via Bidirectional Tunable De-Makeup Network

We present a deep learning-based method for removing makeup effects (de-makeup) in a face image. This problem poses a major challenge due to obscuring of the underlying facial features by cosmetics, which is very important in multimedia applications in the field of security, entertainment, and social networking. To address this task, we propose the bidirectional tunable de-makeup network (BTD-Net), which jointly learns the makeup process to aid in learning the de-makeup process. For tractable learning of the makeup process, which is a one-to-many mapping determined by the cosmetics that are applied, we introduce a latent variable that reflects the makeup style. This latent variable is extracted in the de-makeup process and used as a condition on the makeup process to constrain the one-to-many mapping to a specific solution. Through extensive experiments, our proposed BTD-Net is found to surpass the state-of-art techniques in estimating realistic non-makeup faces that correspond to the input makeup images. We additionally show that applications such as tuning the amount of makeup can be enhanced through the use of this method.