Publication Details
Biometric-Enabled Watchlists Technology
Kanich Ondřej, Ing., Ph.D. (DITS FIT BUT)
Dvořák Michal, Ing. (DITS FIT BUT)
Drahanský Martin, prof. Ing., Dipl.-Ing., Ph.D. (DITS FIT BUT)
Yanushkevich Svetlana, Dr. (UCalgary)
Shmerko Vlad. P. (UCalgary)
biometrics (access control), risk management, face recognition, fingerprint identification, Doddington metric, e-borders, risk assessment technique, risk control, risk categorisation, systematic approach, watchlist landscape modelling, fingerprint databases, entry-exit technologies, biometric-enabled watchlists technology, high-quality biometric traits, large-scale facial databases
For Entry-Exit technologies, such as US VISIT and Smart Borders (e-borders), a watchlist normally contains high-quality biometric traits and is checked only against visitors. The situation can change drastically if low-quality images are added into the watchlist. Motivated by this fact, we introduce a systematic approach to assessing the risk of travellers using a biometric-enabled watchlist where some latency of the biometric traits is allowed. The main results presented herein include: (1) a taxonomical view of the watchlist technology, and (2) a novel risk assessment technique. For modelling the watchlist landscape, we propose a risk categorisation using the Doddington metric. We evaluate via experimental study on large-scale facial and fingerprint databases, the risks of impersonation and mis-identification in various screening scenarios. Other contributions include a study of approaches to designing a biometric-enabled watchlist for e-borders: a) risk control and b) improving performance of the e-border via integrating the interview supporting machines.
@ARTICLE{FITPUB11362, author = "K. Kenneth Lai and Ond\v{r}ej Kanich and Michal Dvo\v{r}\'{a}k and Martin Drahansk\'{y} and Svetlana Yanushkevich and P. Vlad. Shmerko", title = "Biometric-Enabled Watchlists Technology", pages = "163--172", journal = "IET Biometrics", volume = 7, number = 2, year = 2018, ISSN = "2047-4938", doi = "10.1049/iet-bmt.2017.0036", language = "english", url = "https://www.fit.vut.cz/research/publication/11362" }