The following report details the potential of using BVP and BCG waveforms as a biometrics means for user authentication using a head-mounted device. We trained a convolutional neural network to retrieve an optimal model that will allow for correct user authentication. We analyzed the results using ROC curves to determine the best performing models. The ROC curve analysis showed us that there were errors with our BVP results and therefore we moved on with only the BCG waveform. This research aimed to find alternative methods of authentication for users while considering the security of their passwords. The waveforms help us achieve this goal of authentication as users provide their subtle head movements which are much harder for adversaries to mimic than a typed password.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Bhatia, Meghana
• Gayoso, Alejandro Soler
• Kalampalikis, Nikolaos
• Abualhaija, Rushdi Mazen

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-040819-150048

Advisor

• Venkatasubramanian, Krishna Kumar

Year

• 2019

Date created

• 2019-04-08

Resource type

• Major Qualifying Project

Major

• Computer Science
• Interdisciplinary

Rights statement

• In Copyright