Abstract
Original language  Undefined 

Awarding Institution 

Supervisors/Advisors 

Thesis sponsors  
Award date  19 Jun 2014 
Place of Publication  Enschede 
Publisher  
Print ISBNs  9789036536899 
DOIs  
Publication status  Published  19 Jun 2014 
Keywords
 SCSSafety
 METIS303759
 Biometric
 EWI25054
 Calibration
 Forensic
 Face Recognition
 IR91252
Cite this
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Biometric Score Calibration for Forensic Face Recognition. / Ali, Tauseef.
Enschede : Universiteit Twente, 2014. 133 p.Research output: Thesis › PhD Thesis  Research UT, graduation UT › Academic
TY  THES
T1  Biometric Score Calibration for Forensic Face Recognition
AU  Ali, Tauseef
PY  2014/6/19
Y1  2014/6/19
N2  When two biometric specimens are compared using an automatic biometric recognition system, a similarity metric called “score‿ can be computed. In forensics, one of the biometric specimens is from an unknown source, for example, from a CCTV footage or a fingermark found at a crime scene and the other biometric specimen is obtained from a known source, for example, from a suspect. Automatic biometric recognition systems are gradually replacing the forensic examiners’ manual comparison of the two biometric specimens. In forensics, there is a huge interest to use a suitable measure to report the output of the comparison of the two biometric specimens. This has led to the use of the likelihoodratio, P(sHp)P(sHd), where s is the score computed by an automatic biometric recognition system, Hp is the hypothesis of the prosecution (which states that the two biometric specimens are obtained from a samesource) and Hd is the hypothesis of the defense (which states that the two biometric specimens are obtained from different sources). Generally, two sets of training scores, one under Hp and the other under Hd, are needed to compute a likelihoodratio from a score. In this thesis, we review several methods of likelihoodratio computation focusing mainly on the issues of the sampling variability in the sets of training scores and the specific conditioning imposed on the pairs of the biometric specimens to compute them. Three different methods are considered in detail: Kernel density estimation, Logistic regression and Pool adjacent Violators. The effect of the sampling variability is quantified varying : 1) the shapes of the probability density functions which model the distributions of the scores under Hp and under Hd; 2) the sizes of the training sets under Hp and under Hd; 3) the actual value of the score for which the likelihoodratio is computed. The study proposes a simulation framework which can be used to study several properties of a likelihoodratio computation method and to quantify the effect of the sampling variability in a likelihoodratio. This is useful for an appropriate and informed choice of a likelihoodratio computation method. It is shown that sampling variability is a serious concern when small sets of the training scores are available for likelihoodratio computation. Our study of likelihoodratio computation also focuses on the specific conditioning imposed on the pairs of biometric specimens used for computation of the sets of the training scores. In general, the two sets of training scores are viii Summary obtained from a samesource and differentsources comparisons of biometric specimens. However, the samesource and differentsources conditions can be anchored to a specific suspect in a forensic case or it can be generic samesource and differentsources comparisons independent of the suspect involved in the case. This results in two likelihoodratios which differ in the nature of the training scores they use and therefore consider slightly different interpretations of the two hypotheses. An empirical study is carried out to quantify how much and how frequently the two likelihoodratios vary considering a speaker, a face and a fingerprint recognition system. Study showed that there is significant variations in the two likelihoodratios and therefore explicit definition of the training sets and the hypotheses implied by them is very important. The stateoftheart towards automated forensic face recognition is reviewed and the concept of likelihoodratio is applied to several existing biometric face recognition systems. In forensic situations, e.g., when an image from a crime scene is compared with an image from a suspect, forensic face recognition is currently a manual process referred to as “forensic facial comparison‿ and performed by forensic examiners based on their experience and a limited set of guidelines. A step is taken towards automation of forensic face recognition by studying the discriminating powers of different facial features such as eyes, eye brows, nose, etc. This kind of regional comparison is the essence of forensic facial comparison and prove very useful in situations where a part of the face is available for comparison. Besides the automation, it might also be feasible to use existing automatic face recognition systems for forensic comparison and reporting. To this end, several face recognition systems are calibrated so that they produce likelihoodratios and their performance is evaluated based on the likelihoodratios assessment tools.
AB  When two biometric specimens are compared using an automatic biometric recognition system, a similarity metric called “score‿ can be computed. In forensics, one of the biometric specimens is from an unknown source, for example, from a CCTV footage or a fingermark found at a crime scene and the other biometric specimen is obtained from a known source, for example, from a suspect. Automatic biometric recognition systems are gradually replacing the forensic examiners’ manual comparison of the two biometric specimens. In forensics, there is a huge interest to use a suitable measure to report the output of the comparison of the two biometric specimens. This has led to the use of the likelihoodratio, P(sHp)P(sHd), where s is the score computed by an automatic biometric recognition system, Hp is the hypothesis of the prosecution (which states that the two biometric specimens are obtained from a samesource) and Hd is the hypothesis of the defense (which states that the two biometric specimens are obtained from different sources). Generally, two sets of training scores, one under Hp and the other under Hd, are needed to compute a likelihoodratio from a score. In this thesis, we review several methods of likelihoodratio computation focusing mainly on the issues of the sampling variability in the sets of training scores and the specific conditioning imposed on the pairs of the biometric specimens to compute them. Three different methods are considered in detail: Kernel density estimation, Logistic regression and Pool adjacent Violators. The effect of the sampling variability is quantified varying : 1) the shapes of the probability density functions which model the distributions of the scores under Hp and under Hd; 2) the sizes of the training sets under Hp and under Hd; 3) the actual value of the score for which the likelihoodratio is computed. The study proposes a simulation framework which can be used to study several properties of a likelihoodratio computation method and to quantify the effect of the sampling variability in a likelihoodratio. This is useful for an appropriate and informed choice of a likelihoodratio computation method. It is shown that sampling variability is a serious concern when small sets of the training scores are available for likelihoodratio computation. Our study of likelihoodratio computation also focuses on the specific conditioning imposed on the pairs of biometric specimens used for computation of the sets of the training scores. In general, the two sets of training scores are viii Summary obtained from a samesource and differentsources comparisons of biometric specimens. However, the samesource and differentsources conditions can be anchored to a specific suspect in a forensic case or it can be generic samesource and differentsources comparisons independent of the suspect involved in the case. This results in two likelihoodratios which differ in the nature of the training scores they use and therefore consider slightly different interpretations of the two hypotheses. An empirical study is carried out to quantify how much and how frequently the two likelihoodratios vary considering a speaker, a face and a fingerprint recognition system. Study showed that there is significant variations in the two likelihoodratios and therefore explicit definition of the training sets and the hypotheses implied by them is very important. The stateoftheart towards automated forensic face recognition is reviewed and the concept of likelihoodratio is applied to several existing biometric face recognition systems. In forensic situations, e.g., when an image from a crime scene is compared with an image from a suspect, forensic face recognition is currently a manual process referred to as “forensic facial comparison‿ and performed by forensic examiners based on their experience and a limited set of guidelines. A step is taken towards automation of forensic face recognition by studying the discriminating powers of different facial features such as eyes, eye brows, nose, etc. This kind of regional comparison is the essence of forensic facial comparison and prove very useful in situations where a part of the face is available for comparison. Besides the automation, it might also be feasible to use existing automatic face recognition systems for forensic comparison and reporting. To this end, several face recognition systems are calibrated so that they produce likelihoodratios and their performance is evaluated based on the likelihoodratios assessment tools.
KW  SCSSafety
KW  METIS303759
KW  Biometric
KW  EWI25054
KW  Calibration
KW  Forensic
KW  Face Recognition
KW  IR91252
U2  10.3990/1.9789036536899
DO  10.3990/1.9789036536899
M3  PhD Thesis  Research UT, graduation UT
SN  9789036536899
PB  Universiteit Twente
CY  Enschede
ER 