Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems.
The Bayesian approach provides a unified and logical framework for the analysis of evidence and to provide results in the form of likelihood ratios (LR) from the forensic laboratory to court. In this contribution we want to clarify how the biometric scientist or laboratory can adapt their conventional biometric systems or technologies to work according to this Bayesian approach. Forensic systems providing their results in the form of LR will be assessed through Tippett plots, which give a clear representation of the LR-based performance both for targets (the suspect is the author/source of the test pattern) and non-targets. However, the computation procedures of the LR values, especially with biometric evidences, are still an open issue. Reliable estimation techniques showing good generalization properties for the estimation of the between- and within-source variabilities of the test pattern are required, as variance restriction techniques in the within-source density estimation to stand for the variability of the source with the course of time. Fingerprint, face and on-line signature recognition systems will be adapted to work according to this Bayesian approach showing both the likelihood ratios range in each application and the adequacy of these biometric techniques to the daily forensic work.
Gonzalez-Rodriguez
Joaquin
J
ATVS, Speech and Signal Processing Group, Escuela Politecnica Superior, Universidad Autonoma de Madrid, E-28049 Madrid, Spain. joaquin.gonzalez@uam.es
Fierrez-Aguilar
Julian
J
Ramos-Castro
Daniel
D
Ortega-Garcia
Javier
J
D001499:Bayes Theorem; D001699:Biometry; D003878:Dermatoglyphics; D005145:Face; D005554:Forensic Medicine; D006236:Handwriting; D006801:Humans; D007091:Image Processing, Computer-Assisted