Privacy-Preserving Facial Recognition Using Biometric-Capsules

dc.contributor.advisorZou, Xukai
dc.contributor.authorPhillips, Tyler S.
dc.contributor.otherLi, Feng
dc.contributor.otherHasan, Mohammad Al
dc.date.accessioned2020-05-05T09:53:18Z
dc.date.available2020-05-05T09:53:18Z
dc.date.issued2020-05
dc.degree.date2020en_US
dc.degree.grantorPurdue Universityen_US
dc.degree.levelM.S.en_US
dc.descriptionIndiana University-Purdue University Indianapolis (IUPUI)en_US
dc.description.abstractIn 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.en_US
dc.identifier.urihttps://hdl.handle.net/1805/22695
dc.identifier.urihttp://dx.doi.org/10.7912/C2/2374
dc.language.isoen_USen_US
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectBiometricsen_US
dc.subjectPrivacyen_US
dc.subjectDeep Learningen_US
dc.titlePrivacy-Preserving Facial Recognition Using Biometric-Capsulesen_US
dc.typeThesisen
thesis.degree.disciplineComputer & Information Scienceen
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