Towards Real-World Face De-Identification

Abstract

A wide range of technological advances have helped to make extensive image and video acquisition close to effortless. As a consequence many applications which capture image data of people for either immediate inspection or storage and subsequent sharing have become possible. Along with these improved recording capabilities, however, come concerns about the privacy of people visible in the scene. While algorithms have been proposed to de-identify images, currently available methods are still lacking. In this paper we propose a general framework for the de-identification of images which subsumes a number of previously introduced approaches. Unlike the adhoc methods currently used in the field our algorithms aim at providing privacy guarantees. In experiments on illuminationand expression-variant face datasets we show that the proposed algorithms achieve the desired privacy protection while minimally distorting the data.

Citation:
Ralph Gross and Latanya Sweeney. Towards Real-World Face De-Identification. IEEE Conference on Biometrics (BTAS), Washington, DC, September 2007. (PDF).

Related Links

• Face de-identification (Related TKDE Paper)
• Face de-identification (Related Book Chapter Paper)
• Face de-identification (Related AMIA Paper)
• Face de-identification (Related CVPR Paper)
• Face de-identification (Related ICDE Paper)
• Face de-identification Utility
• Face de-identification Demonstration

• Webcam Surveillance
• Homeland Security
• k-anonymity: a model for protecting privacy

• Other Projects at the Data Privacy Lab
• Other Demonstrations at the Data Privacy Lab
• Data Privacy Laboratory