A biometric encryption system for the self-exclusion scenario of face recognition

Abstract

This paper presents a biometric encryption system that addresses the privacy concern in the deployment of the face recognition technology in real-world systems. In particular, we focus on a self-exclusion scenario (a special application of watch-list) of face recognition and propose a novel design of a biometric encryption system deployed with a face recognition system under constrained conditions. From a system perspective, we investigate issues ranging from image preprocessing, feature extraction, to cryptography, error-correcting coding/decoding, key binding, and bit allocation. In simulation studies, the proposed biometric encryption system is tested on the CMU PIE face database. An important observation from the simulation results is that in the proposed system, the biometric encryption module tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate.

Publication
IEEE Systems Journal
Haiping Lu
Haiping Lu
Director of the UK Open Multimodal AI Network, Professor of Machine Learning, and Head of AI Research Engineering

I am a Professor of Machine Learning. I develop translational multimodal AI technologies for advancing healthcare and scientific discovery.