Piecewise affine warp based frontal face synthesizing and application on face recognition
Shanshan Wang, Xiaobin Yi, Ying Chen · 2017
In this paper, a frontal face synthesizing strategy based on Poisson image fusion and piecewise affine warp (PAW) is proposed to solve the problem of large-scale computation cost or transformation distortion in general synthesizing methods. The multiple non-frontal input images are warped to the frontal face template with PAW. The corresponding weight matrixes are calculated according to the magnitude of deformation which can be used to obtain the foreground mask for Poisson fusion. Iterative fusion strategy is designed to synthesize one frontal image from multiple non-frontal images. In each step, the PAW image is used as foreground image, the deformation mask is used as foreground mask, and the fusion image of the previous step is used as background. Experiments show that the synthesized frontal image can perfectly preserve personal facial details and outperforms others in both subjective and objective evaluations, and the synthesizing strategy can greatly improve the accuracy of face recognition.