Retaining Expressions on De-identified Faces

Abstract

The extensive use of video surveillance along with advances in face recognition has ignited concerns about the privacy of the people identifiable in recorded documents. Prior research into face de-identification algorithms has successfully proposed k-anonymity methods that guarantee to thwart face recognition software. However, there has been little investigation into the preservation of the data utility such as gender and expression in the original images. To address this challenge, a new algorithm based on the Active Appearance Model is proposed here. The main attraction of the approach is that of the preservation of the data utility in terms of facial expression, whilst maintaining privacy protection. The former includes not only the preservation of the expression category (e.g. happy or sad), but also the details of the original expression (e.g. the intensity of a smile and movements of the lips). This is considered to be of significant value in real applications of face de-identification, where the given video contains facial images of the same expression with various degrees of intensity.