Preserving privacy by de-identifying face images

In the context of sharing video surveillance data, a significant threat to privacy is face recognition software, which can automatically identify known people, such as from a database of drivers' license photos, and thereby track people regardless of suspicion. This paper introduces an algorithm to protect the privacy of individuals in video surveillance data by deidentifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to deidentifying faces involves blacking out each face. This thwarts any possible face recognition, but because all facial details are obscured, the result is of limited use. Many ad hoc attempts, such as covering eyes, fail to thwart face recognition because of the robustness of face recognition methods. This work presents a new privacy-enabling algorithm, named k-Same, that guarantees face recognition software cannot reliably recognize deidentified faces, even though many facial details are preserved. The algorithm determines similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels (k-Same-Pixel) or eigenvectors (k-Same-Eigen). Results are presented on a standard collection of real face images with varying k.

[1]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[2]  Latanya Sweeney,et al.  Computational disclosure control: a primer on data privacy protection , 2001 .

[3]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Vassilios S. Verykios,et al.  Disclosure limitation of sensitive rules , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[5]  L. Sweeney,et al.  Preserving Privacy by De-identifying Facial Images , 2003 .

[6]  Ralph Gross,et al.  Quo vadis Face Recognition , 2001 .

[7]  Jouko Lampinen,et al.  Distortion tolerant pattern recognition based on self-organizing feature extraction , 1995, IEEE Trans. Neural Networks.

[8]  Charu C. Aggarwal,et al.  On the design and quantification of privacy preserving data mining algorithms , 2001, PODS.

[9]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[10]  Yücel Saygin,et al.  Privacy preserving association rule mining , 2002, Proceedings Twelfth International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems RIDE-2EC 2002.

[11]  C. Atkeson,et al.  Toward the Automatic Assessment of Behavioral Distrubances of Dementia , 2003 .

[12]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[13]  John D. Woodward,et al.  Army Biometric Applications: Identifying and Addressing Sociocultural Concerns , 2001 .

[14]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  Eric W. Weisstein,et al.  The CRC concise encyclopedia of mathematics , 1999 .

[16]  Chris Clifton,et al.  Using unknowns to prevent discovery of association rules , 2001, SGMD.

[17]  Jayant R. Haritsa,et al.  Maintaining Data Privacy in Association Rule Mining , 2002, VLDB.

[18]  H Moon,et al.  Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms , 2001, Perception.

[19]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[20]  Takeo Kanade,et al.  Computer recognition of human faces , 1980 .

[21]  Yanxi Liu,et al.  Facial asymmetry quantification for expression invariant human identification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[22]  E. Weisstein.  CRC Concise Encyclopedia of Mathematics, Second Edition , 2002 .

[23]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2002, Journal of Cryptology.