Utility Preserved Facial Image De-identification Using Appearance Subspace Decomposition

Automated human facial image deidentification is a much-needed technology for privacy-preserving social media and intelligent surveillance applications. We propose a novel utility preserved facial image de-identification to subtly tinker the appearance of facial images to achieve facial anonymity by creating "averaged identity faces". This approach is able to preserve the utility of the facial images while achieving the goal of privacy protection. We explore a decomposition of an Active appearance model (AAM) face space by using subspace learning where the loss can be modeled as the difference between two trace ratio items, and each respectively models the level of discriminativeness on identity and utility. Finally, the face space is decomposed into subspaces that are respectively sensitive to face identity and face utility. For the subspace most relevant to face identity, a k-anonymity de-identification procedure is applied. To verify the performance of the proposed facial image de-identification approach, we evaluate the created "averaged faces"using the extended Cohn-Kanade Dataset (CK+). The experimental results show that our proposed approach is satisfied to preserve the utility of the original image while defying face identity recognition.