Expression Preserved Face Privacy Protection Based on Multi-mode Discriminant Analysis

Most visual privacy protection methods only hide the identity information of the face images, but the expression, behavior and some other information, which are of great significant in the live broadcast and other scenarios, are also destroyed by the privacy protection process. To this end, this paper introduces a method to remove the identity information while preserving the expression information by performing multi-mode discriminant analysis on the images normalized with AAM algorithm. The face images are decomposed into mutually orthogonal subspaces corresponding to face attributes such as gender, race and expression, each of which owns related characteristic parameters. Then, the expression parameter is preserves to keep the facial expression information while others parameters, including gender and race, are modified to protect face privacy. The experiments show that this method yields well performance on both data utility and privacy protection.