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Colloquium - Boult

Improving Biometric System Privacy, Security and Accuracy
University of Colorado at Colorado Springs

The talk will start with a motivation for the need for improved Security/Privacy in Biometrics. This section of the talk will briefly review the privacy/security issues with biometric, explain why standard encryption does not solve the problems and quickly review the state of the art in privacy preserving biometrics. It will then present the key ideas behind the patent-pending Biotopes®, and present performance results where existing face and fingerprint algorithms were extended with Biotopes to improve security/privacy. We discuss why the Biotopes and associated transforms, actually improved the accuracy of the underlying algorithms. We conclude this segment on privacy/security with some examples highlighting the important of accuracy and why it has such a strong impact on privacy/security concerns.

The second aspect of the presentation will introduce our unique work looking at predicting the failure of a biometric recognition system. The approach, Feature Analysis of Similarity Surface Theory (FASST), conjectures that the the similarity scores used in recognition contain information which can, in general, predict when the system is failing. AdaBoost is used to combine the features computed from the similarity surface to produce a patent pending system that predicts the failure of a biometric system. Face-system Failure prediction, using a leading leading commercial face recognition system, is presented as an example to show how to use the approach. On outdoor weathered face data, the system demonstrated the ability to predict 90% of the underlying facial recognition system failures with only a 15% false alarm rate.

The final component of the talk presents our Random-Eyes® approach, a novel technique for improving face recognition performance by predicting system failure, and, if necessary, perturbing eye coordinate inputs and re-predicting failure as a means of selecting the a perturbation that provides correct classification. This relies on a method that can accurately identify patterns that can lead to more accurate classification, without modifying the classification algorithm itself. Showing the generality of FASST, this time we use a neural network trained on wavelet transforms of similarity score distributions from an analysis of the gallery. Face images with a high likelihood of having been incorrectly matched are reprocessed using perturbed eye coordinate inputs, and the best results used to "correct" the initial results. Results for both commercial and research face-based biometrics are presented using both simulated and real data. The statistically significant results show the strong potential for this to improve system performance, especially with uncooperative subjects.

The talk will end with a brief five-minute overview of the VAST Lab at UCCS and our many other projects ranging from video surveillance networks, to distributed steganalysis, to low-power high-bandwidth sensor networks, to embedded systems.

Hosted by Jane Mulligan.

Department of Computer Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
May 5, 2012 (14:13)