Face de-identification with expressions preservation

This paper addresses an application that has not been much explored, the de-identification of faces with expressions preservation in images. With the huge amount of images and videos shared on the Internet, protecting the identity of people in the data becomes crucial. Removing the identity information is often referred as de-identification. In this paper, we propose a novel de-identification process that preserves the important clues on the face for further behavior or emotions analysis. It is a difficult problem because obtaining the anonymity implies deteriorating the main face components. At the opposite, analyzing the expressions requires keeping enough information on the face such as, for instance, the gaze or the corners of the lips. Our approach relies on face and key points detection, followed by a variational adaptive filtering. Experimental results show the potential of the proposed method and open new insights for image dissemination or video broadcasting.