Title :
On the fusion of possibilistic and probabilistic information in biometric decision-making
Author :
Yager, Ronald R.
Author_Institution :
Machine Intell. Inst., Iona Coll., New Rochelle, NY, USA
Abstract :
(Svetlana Yanushkevich Special Session) Information used in decision-making in biometric systems can come from both sensors and human observers. The sensor provided information generally has a probabilistic type of uncertainty whereas human provided linguistic information typically introduces a possibilistic type of uncertainty. Here we are faced with a problem in which we must fuse information with different types of uncertainty. We provide a unified framework for the representation of these different types of information using a set measure approach for the representation of uncertain information.
Keywords :
biometrics (access control); decision making; possibility theory; probability; sensor fusion; set theory; biometric decision making; possibilistic information fusion; probabilistic information fusion; set measure approach; uncertain information representation; Finite element methods; Measurement uncertainty; Pragmatics; Probabilistic logic; Q measurement; Sensors; Uncertainty; fusion; set measure; uncertainty modeling;
Conference_Titel :
Computational Intelligence in Biometrics and Identity Management (CIBIM), 2011 IEEE Workshop on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-9899-4
DOI :
10.1109/CIBIM.2011.5949205