iFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities

• Published in: IEEE access 2022, Vol.10, p.71791-71804
• Main Authors: Mitra, Alakananda, Bigioi, Dan, Mohanty, Saraju P., Corcoran, Peter, Kougianos, Elias
• Format: Article
• Language: English
• Subjects: Authentication; Computer crime; convolutional neural network; Cybersecurity; deepfake; Deepfakes; digital ID; Face recognition; facial authentication system; Internet of Things (IoT); lookup table; Lookup tables; Medical services; presentation attack; Regulation; Smart cities; Smart city; triplet loss; Urban development
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3187686

"Smart Cities" are a viable option to various issues caused by accelerated urban growth. To make smart cities a reality, smart citizens need to be connected to the "Smart City" through a digital ID. A digital ID enables citizens to utilize smart city facilities such as healthcare, transportation, finance, and energy with ease and efficiency. In this paper, we propose a proof-of-concept of a facial authentication-based end-to-end digital ID system for a smart city. Facial authentication systems are prone to various biometric template attacks and cyber security attacks. Our proposed system is designed to detect the first type of attack, especially deepfake and presentation attacks. Users are authenticated each time they use facilities in a smart city. Facial data is stored in the cloud in a lookup table format with an unidentifiable username. The process is very secure as no data leaves the device during authentication. Our proposed solution achieved 97% accuracy in authentication with a False Rejection Ratio of 2% and False Acceptance Ratio of 3%.