Face Image Synthesis Driven by Geometric Feature and Attribute Label

Abstract

Aiming at the problems in current face image synthesis,such as the lack of diversity of synthetic appearances and expressions,the low reality of the facial expressions and the low synthesis efficiency,this paper proposes a novel face synthesis network model driven by facial geometric feature and attribute label.Given a source face image,a target face image and the attribute(e.g.,hair color,gender,age) label,the new face synthesis model can generate a highly realistic face image which owns the expression of the source face,the identity of the target face and the specified attribute.The new model consists of two parts:facial landmark generator(FLMG) and geometry and attribute aware generator(GAAG).FLMG uses the facial geometric feature points to encode the expression information,and transfers the expression from the source to the target face in the form of feature points.Combining the transferred feature points,the specified attribute label and the target face image,GAAG generates a face image with specified appearance and expression.A novel soft margin triplet perception loss is introduced to GAAG,which can make the synthesized face more natural and keep the identity of the target face well,and makes the GAAG converge faster.Experimental results show that the face images generated by our approach have more diverse appearances and more realistic expressions.In addition,our model only needs to be trained once to realize the transfer between any arbitrary different expressions,so its efficiency is high.

Keywords

• face image synthesis|expression transfer|face editing|generative adversarial network