Face De-Identification Using Face Caricature

Abstract

Face privacy concerns revolve around the ethical, social, and technological implications of collecting, storing, and using facial data. With the advancement of deep learning techniques, realistic face privacy involves techniques that obscure or alter facial features effectively without compromising the usability or quality of the visual content. Modern face privacy techniques suffer from three main problems:(1) lack of human perception, (2) indistinguishability, and (3) loss of facial attributes. Modern face privacy techniques generate random, realistic faces to conceal the identifiable features of the original faces but lack the application of human perception to face de-identification. Indistinguishability arises with the highly realistic nature of fake faces used in face privacy, making it difficult to distinguish whether a face has been manipulated or not. Most face-privacy methods fail to retain the facial attributes of the de-identified faces. Our face de-identification method is designed to address all three issues mentioned. We propose a novel face de-identification method that considers both human perception and face recognition models when de-identifying a face. We explore the tradeoff between a user misidentifying the original identity with a well-known celebrity and a facial recognition model that tries to identify the original identity. We generate caricature faces of the de-identified faces to ensure our manipulated faces can be distinguished effortlessly. The face caricatures are the exaggeration of the eyes and mouth region, and we provide different exaggeration scales depending on preference and application. We perform an attribute preservation optimization process to retrieve all the facial attributes. We demonstrate our method through a series of both qualitative and quantitative experiments with numerous user studies.