Synthetic face generation from in-the-wild face components swapping

Facial identification has recently been a legal con-cern for protecting one's identity and personal confidentiality. Many face synthesis techniques were used to safeguard individual users' data. This work presents a technique for generating synthetic faces from in-the-wild face components. The face components, such as the eyes, eyebrows, nose, and mouth, were extracted from a facial landmark of in-the-wild images and ran-domly replaced with the original image. Generative Adversarial Networks (GANs) for face restoration were then used to denoise the swapped image while preserving the original colorization. The experiments on face swapping with ten thousand of wild images demonstrate an average of 0.723 difference from the source image. The result shows that our face component swapping technique could be an effective lawful way to use facial data in the future.