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 de-identifying faces such that many facial characteristics remain but the face cannot be reliably recognized. A trivial solution to de-identifying 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 paper presents a new privacy-enabling algorithm, named k-Same, that guarantees face recognition software cannot reliably recognize de-identified 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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Traditional criteria used in biometric performance evaluation do not cover all the performance aspects of biometric template protection (BTP) and the lack of well-defined metrics inhibits the proper evaluation of such methods. Previous work in the literature focuses, in general, on a limited set of criteria and methods. This paper provides the first holistic approach to the evaluation of biometric template protection that is able to cover a whole range of methods. We present a selection of well-defined criteria and some metrics that are compliant with the reference architecture for template protection as defined in the recently adopted standard ISO/IEC 24745 (2011), which is applicable to nearly all known BTP methods. The criteria have been grouped in three categories of performance: technical, protection, and operational.
This publication has been developed by NIST to further its statutory responsibilities under the Federal Information Security Management Act (FISMA), Public Law (P.L.) 107-347. NIST is responsible for developing information security standards and guidelines, including minimum requirements for Federal information systems, but such standards and guidelines shall not apply to national security systems without the express approval of appropriate Federal officials exercising policy authority over such systems. This guideline is consistent with the requirements of the Office of Management and Budget (OMB) Circular A-130, Section 8b(3), Securing Agency Information Systems, as analyzed in Circular A-130, Appendix IV: Analysis of Key Sections.
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