Abstract
Recent advances in face recognition technology render face-based authentication very attractive due to the high accuracy and ease of use. However, the increased use of biometrics (such as faces) triggered a lot of research on the protection biometric data in the fields of computer security and cryptography.
Unfortunately, most of the face-based systems, and most notably the privacy-preserving mechanisms, are evaluated on small data sets or assume ideal distributions of the faces (that could differ significantly from the real data). At the same time, acquiring large biometric data sets for evaluation purposes is time consuming, expensive, and complicated due to legal/ethical considerations related to the privacy of the test subjects. In this work, we present GenFace, the first publicly available system for generating synthetic facial images. GenFace can generate sets of large number of facial images, solving the aforementioned problem. Such sets can be used for testing and evaluating face-based authentication systems. Such test sets can also be used in balancing the ROC curves of such systems with the error correction codes used in authentication systems employing secure sketch or fuzzy extractors. Another application is the use of these test sets in the evaluation of privacy-preserving biometric protocols such as GSHADE, which can now enjoy a large number of synthetic examples which follow a real-life distribution of biometrics. As a case study, we show how to use GenFace in evaluating SecureFace, a face-based authentication system that offers end-to-end authentication and privacy.
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Notes
- 1.
Such as Aadhaar, the Indian biometric database of the full Indian population.
- 2.
Changes in viewing conditions require the use of a 3D model and will be considered in future work.
- 3.
Hereafter we use the term face-space to mean the space spanned by a set of principal components, derived from a set of training face images.
- 4.
Our experiments, reported in Sect. 3.2 verify these assumptions.
- 5.
It is unclear what should be the training size of a face-space that models all possible faces. However, we note that a face which is not “plausible” in some face-space, i.e., is very far from the surface of the face-space is likely to not “work” properly in a system which relies on the face-space.
- 6.
This feature is more relevant to the “sample within” option, as the distance from each offspring image to the seed could be different.
- 7.
GenFace does not require full Matlab, but the installation package will install the “MATLAB Component Runtime”.
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Acknowledgements
This research was supported by UK Engineering and Physical Sciences Research Council project EP/M013375/1 and by the Israeli Ministry of Science and Technology project 3-11858. We thank Mahmood Sharif for his support in experiments using SecureFace. We thank the anonymous reviewers of this paper for their ideas and suggestions.
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Osadchy, M., Wang, Y., Dunkelman, O., Gibson, S., Hernandez-Castro, J., Solomon, C. (2017). GenFace: Improving Cyber Security Using Realistic Synthetic Face Generation. In: Dolev, S., Lodha, S. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2017. Lecture Notes in Computer Science(), vol 10332. Springer, Cham. https://doi.org/10.1007/978-3-319-60080-2_2
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