SelfIs: Self-Sovereign Biometric IDs

Luis Bathen, German H. Flores, Gabor Madl, Divyesh Jadav, Andreas Arvanitis, Krishna Santhanam, Connie Zeng, Alan Gordon; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0


We live in a connected world that requires us to identify ourselves every time we want to access our emails, work stations, bank accounts, health care records, etc. Every system we interact with requires us to remember a username/password combination, have access to some private/public key pair, a hardware token, or some third party authentication software. Our digital identity is owned by the services we are trying to access, no longer under our control. Self-Sovereign Identity promises to give back control of his or her identity to the user. It is in this context that we explore the use of biometrics in order to empower users to be their own passwords, their own keys, their own means to authenticate themselves. We propose Self-Sovereign Biometric IDs (SelfIs), a novel approach that marries the concepts of decentralization, cancelable biometrics, bloom filters, and machine learning to develop a privacy-first solution capable of allowing users to control how their biometrics are used without risking their raw biometric templates.

Related Material

author = {Bathen, Luis and Flores, German H. and Madl, Gabor and Jadav, Divyesh and Arvanitis, Andreas and Santhanam, Krishna and Zeng, Connie and Gordon, Alan},
title = {SelfIs: Self-Sovereign Biometric IDs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}