In biometric recognition systems, biometric samples (images of faces, finger- prints, voices, gaits, etc.) of people are compared and classifiers (matchers) indicate the level of similarity between any pair of samples by a score. If two samples of the same person are compared, a genuine score is obtained. If a comparison concerns samples of different people, the resulting score is called an impostor score. The scope of this thesis is about biometric verification (also known as authentication) in the sense that two biometric samples are com- pared to find out if they originate from the same person or stem from different people, without making any identity claim. Except when stated specifically, the random variables genuine score Sgen and impostor score Simp are assumed to be continuous, taking values in the real line R = (−∞, ∞) with distribution functions Fgen and Fimp and density functions fgen and fimp, respectively.
|Award date||11 Oct 2016|
|Place of Publication||Amsterdam|
|Publication status||Published - 11 Oct 2016|
- Semiparametric Copula Models
- Biometric Score Level
- NWO 727.011.008