The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Preserving privacy by de-identifying face images
Abstract
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.
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Cites
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k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
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Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 2,961
The FERET database and evaluation procedure for face-recognition algorithms
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Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719
On the design and quantification of privacy preserving data mining algorithms
Agrawal, D., & Aggarwal, C. C. (2001) · Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/375551.375602 · Cited by 1,006
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Cited by
A General Survey of Privacy-Preserving Data Mining Models and Algorithms
Aggarwal, C. C., & Yu, P. S. (2008) · Advances in Database Systems · doi:10.1007/978-0-387-70992-5_2 · Cited by 469
Face swapping: automatically replacing faces in photographs
Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., & Nayar, S. K. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360638 · Cited by 333
Facial Expression Recognition: A Survey
Huang, Y., Chen, F., Lv, S., & Wang, X. (2019) · Symmetry · doi:10.3390/sym11101189 · Cited by 195
Mughaid, A., Obeidat, I., AlZu’bi, S., Elsoud, E. A., Alnajjar, A., Alsoud, A. R., & Abualigah, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14347-8 · Cited by 39
Lee, J. H., & You, S. J. (2024) · IEEE Access · doi:10.1109/access.2024.3352146 · Cited by 29
Metadata
- Authors
- Newton, E. M., Sweeney, L., & Malin, B.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2005-02
- Volume
- 17
- Issue
- 2
- Pages
- 232-243
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2103958416
- Cited by
- 615
- References
- 20
- FWCI
- 21.54
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
