Tyler_Thesis.pdf (2.89 MB)

Privacy-Preserving Facial Recognition Using Biometric-Capsules

Download (2.89 MB)
thesis
posted on 04.05.2020, 12:34 by Tyler Stephen Phillips
In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design.

In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods.

Funding

NSF CICI #1839746

History

Degree Type

Master of Science

Department

Computer and Information Technology

Campus location

Indianapolis

Advisor/Supervisor/Committee Chair

Xukai Zou

Additional Committee Member 2

Mohammad Al Hasan

Additional Committee Member 3

Feng Li

Licence

Exports

Logo branding

Licence

Exports