A Novel Frontal Facial Synthesis Algorithm Based on Individual Residual Face

The frontal facial synthesis results of current main methods tend to be smooth and lack personal characteristics, which greatly influence the subjective impression. Especially aiming at large-scale deflecting faces, the shielding side just provides few features for reconstructing and the deformation of facial elements enhance the difficulty to obtain exact features of target frontal face, making the synthesis result seem to be same and as mean face of database. In this paper, to solve these problems, we propose a novel two-step face synthesis method. In the first step, we utilize the basic symmetry of human face to predict the missing patches according to the other side and generate interim facial image. And in the following step, we introduce the individual residual facial image between interim result and mean face to compensate for the lost personal features of the synthesis result because the residual image carries more individual characteristic of the input face. We show that experimental results of proposed method outperform in the objective and subjective effects with other state-of-the-art methods.