Recently a number of studies in fingerprint verification have combined match scores with quality and liveness measures in order to thwart spoof attacks. However, these approaches do not explicitly account for the influence of the sensor on these variables. In this work, we propose a graphical model that accounts for the impact of the sensor on match scores, quality and liveness measures. The proposed graphical model is implemented using a Gaussian Mixture Model based Bayesian classifier. Effectiveness of the proposed model has been assessed on the LivDet11 fingerprint database using Biometrika and Italdata sensors

A Bayesian Approach for Modeling Sensor Influence on Quality, Liveness and Match Score Values in Fingerprint Verification

RATTANI, AJITA;
2013-01-01

Abstract

Recently a number of studies in fingerprint verification have combined match scores with quality and liveness measures in order to thwart spoof attacks. However, these approaches do not explicitly account for the influence of the sensor on these variables. In this work, we propose a graphical model that accounts for the impact of the sensor on match scores, quality and liveness measures. The proposed graphical model is implemented using a Gaussian Mixture Model based Bayesian classifier. Effectiveness of the proposed model has been assessed on the LivDet11 fingerprint database using Biometrika and Italdata sensors
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/67008
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 11
social impact