A framework of face synthesis based on multilinear analysis
Yuhao Song, Hui Zhang · 2016
This paper addresses the problem of privacy protection in face synthesis. We propose a new face synthesis approach based on tensor decomposition. By using the mathematical properties of tensor analysis, we decompose a face image into multiple factors so that the synthesis process could concentrate only on privacy related information. Then, we generate a new face image by altering the privacy related factors and keeping the other ones untouched. Compared to previous algorithms, our approach has the advantage in producing a synthetic face image without the risk of privacy leaking. We conduct the experiments in different datasets and factors to show the flexibility of the proposed approach. After gaining the synthesis images, we apply the automatic recognition algorithms for both expressions and faces to them. The experiment results demonstrate the effectiveness of our approach.