Watchlist Risk Assessment using Multiparametric Cost and Relative Entropy
This work addresses security and privacy issues in e-border systems for travelers, but it is incremental as it builds on existing biometric watchlist technology.
The paper tackled the problem of designing risk detectors for facial biometric-enabled watchlists to detect threats and avoid false alarms, proposing multiparametric cost assessment and relative entropy measures, and experimentally demonstrated their effects under various scenarios.
This paper addresses the facial biometric-enabled watchlist technology in which risk detectors are mandatory mechanisms for early detection of threats, as well as for avoiding offense to innocent travelers. We propose a multiparametric cost assessment and relative entropy measures as risk detectors. We experimentally demonstrate the effects of mis-identification and impersonation under various watchlist screening scenarios and constraints. The key contributions of this paper are the novel techniques for design and analysis of the biometric-enabled watchlist and the supporting infrastructure, as well as measuring the impersonation impact on e-border performance.