Phillips, TylerZou, XukaiLi, FengLi, Ninghui2020-07-172020-07-172019Phillips, T., Zou, X., Li, F., & Li, N. (2019). Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning. Proceedings of the 24th ACM Symposium on Access Control Models and Technologies, 141–146. Retrieved from: https://par.nsf.gov/biblio/10097229https://hdl.handle.net/1805/23265In 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 authentication 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 BioCapsule method. The BioCapsule method is provably secure, privacy-preserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsule-embedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques.enPublisher PolicybiometricsprivacyauthenticationConference proceedings