PREAMBLE

This web site presents a fingerprint match probability (FMP) model for fingerprints that defines standard values for individual and aggregate arrangements of fingerprint ridge features.  The model takes into consideration all levels of fingerprint ridge detail, e.g., Level I, II and III.  It uses simple statistics to assign match probabilities based on population distributions of "friction ridge feature shapes in position".   It has matured past mere counts of corresponding minutiae by taking into consideration ridge feature rarity, ridge edge contour and path, ridge clarity, reliability and quality of agreement.  The model combines these variables to produce a total quantitative-qualitative weight or value standard for individual and, subsequently, aggregate amounts of fingerprint ridge features.  This "total" quantitative-qualitative statistical probability model for fingerprints is the "T-Model".

The T-Model is designed to conservatively estimate the discrimination power of fingerprint evidence as well as predict the number of friction ridge close matches or “look-alikes” likely to exist in a given population group for a criminal case at hand.  When the model predicts the number of friction ridge look-alikes for an arrangement of fingerprint ridge features to be less than 1, e.g., when the final FMP is less than the reciprocal of the relevant population for the case at hand, then it establishes inference for identification.  The model guards against bias because it removes the decision-making process to make fingerprint identifications from the examiner.  It is the T-Model that declares a match, not the examiner.

The T-Model has been (and continues to be) subjected to the most difficult proficiency tests possible.  It has been tested against the most notable known erroneous fingerprint identifications ever made and it has been pitted against the largest and best amounts of  friction ridge look-alikes ever produced by an automated search, published, or otherwise found during the course of routine casework.  So far the T-Model has not been fooled into making an erroneous identification. 

The T-Model was first published online August 2008.  Since that time it has undergone refinement, continuous updates based on new experimentation and critical scrutiny.  It remains freely available to the broad scientific and fingerprint community for peer review and extended validation study.

 

“Don't believe, trust or accept this model.  Simply do the same experiments and find out for yourself if it has any validity or not.”

Henry Templeman

(See Validation Study and Solicitation)

INTRODUCTION

When a number of friction ridge formations in two fingerprint impressions are found to correspond in shape and position, the probability to find even a very small amount of the most common occurring ridge formations that match is usually in the millions or billions to 1.   As a result, a small amount of highly discriminating corresponding friction ridge formations found in two fingerprint impressions can be defined such that the reciprocal of the match probability exceeds the world fingerprint population many fold.  Once the rarity of a small amount of corresponding ridge formations is estimated, objective criteria can be used to report that with reasonable scientific certainty the amount is significant enough to establish inference for source attribution to a particular individual.

The term “source attribution” should not be confused with “uniqueness” or one and only one to the exclusion of all others that exist, have existed, or will exist in the world.  When deducing source attribution there often is little need to establish that a fingerprint sample is found in only one person in the entire world.  Instead, source attribution is considered in the context of the case, and rarely would the entire world’s population be considered as the pool of plausible contributors of any fingerprint evidence sample. 

In practice, fingerprint match probability is calculated for a number of relevant population groups residing in the geographic area where the crime was committed.  When there is no reason to believe that a smaller population group is relevant, the model sets the fingerprint population group to 300 million, roughly equivalent to a total human population in the United States, the same population group set by the FBI to match DNA profiles for routine casework [84].*

*Note: The plausible number of people who could be the source of any latent print for any crime is always restricted to a number less than the total number of people on earth based on the time and location for a crime at hand.  It is illegitimate to set [a priori] the size of the relevant population at its maximum, e.g., the total number of persons on earth [34] and therefore is determined, or may be refined, on a case by case basis (see Relevant Fingerprint Population).

Fingerprint Source Attribution

"Professionals forget the following reality.  It is not the estimate or forecast that matters so much as the degree of confidence with the opinion." [88]

Nassim Nicholas Taleb

The "Individualization to the Exclusion of All Others" Opinion

The "individualization to the exclusion of all other sources" opinion traditionally used by latent print examiners has been qualified as inherently subjective and unscientific by the most eminent scientific organization in the United States, the National Academy of Sciences (NAS).  The NAS Report rejects the idea that ACE-V has a zero error rate or can be used to reliably establish fingerprint source attribution.   In addition the report points out the need for scientific research regarding the rarity of ridge features and sufficiency thresholds to infer positive identification, e.g., based on experiment, and the application of the science of probability to justify conclusions [91].

NAS Report

The T-Model uses the science of probability to define the rarity of ridge features types in position and sufficiency thresholds to infer positive identification for amounts of corresponding fingerprint ridge features found in two impressions based on the relevant population for the case at hand.  The model calculates the fingerprint match probability (FMP) utilizing the relevant population (RP) for the case at hand such that when the FMP is less than RP, e.g., FMP < RP, then inference for positive identification is justified.  In essence any FMP found to be greater than RP is a qualification of the acceptable level of reasonable doubt tantamount to a judgment of moral certainty [34].   

The Qualified Probability Opinion

Traditional statements for fingerprint source attribution are "identification, exclusion and inconclusive" and have excluded gray-scale probability statements as "highly likely", "likely", unlikely" and so on [34].   The gray-scale probability statement or "qualified opinion" attempts to assess the value of evidence with amounts of corresponding ridge features that fails to exceed a posterior probability of 1, however is deemed significant enough to report if it tends to make inference for positive identification more or less probable that otherwise.

The greatest strength, or weakness, of any probability based fingerprint evidence (PBFE) model is its ability, or inability, to reliably establish a posterior probability greater than or equal to 1 for amounts of matching ridge features in two impressions when utilizing Automated Fingerprint Identification System (AFIS) technology.   All AFIS technology is designed to find the largest and best friction ridge arrangement "look-alikes" that exist in its database.  As a result, any PBFE model that uses likelihood ratios to measure the aggregate weight of friction ridge arrangements present in two impressions must successfully pass validation testing against the largest and best fragmentary friction ridge look-alikes ever recorded in a given fingerprint population.  The model must either demonstrate zero specificity, e.g., a zero false positive error rate, or at the very least show an improved error rate over that found in human decision-making or in a competing model.       

The T-Model discards qualified probability opinion when the Fingerprint Match Probability (FMP) for an amount of corresponding ridge features found in two impressions (based on an AFIS search) is found to be greater then the reciprocal of the relevant population for the case at hand.  Only a model that demonstrates zero specificity, e.g., a zero false positive error rate when pitted against the largest and best friction ridge look-alikes AFIS can find, should be considered reliable enough for use in criminal casework.

It is significant to note that the T-Model has shown to be robust in its ability to calculate the fingerprint match probability (FMP) for the largest and best arrangement of fingerprint ridge feature AFIS look-alikes ever recorded as greater then the reciprocal of the relevant population for the case at hand, and therefore is able to correctly identify such look-alikes as insufficient to infer positive identification (see Error Rate in Terms of Best Look-alikes and  Error Rate in Look-alikes Calculated).

The Justified Opinion

The T-Model sets the demarcation for sufficiency to infer positive identification at 1, where if the Fingerprint Match Probability (FMP) divided by the Relevant Population (RP) for the case at hand is greater than 1, then there is valid basis to infer positive identification.  As a result, the inference for positive identification is considered a justified opinion. 

The justified opinion to infer positive identification may be considered to bridge the gap between the individualization to the exclusion of all others opinion and the qualified opinion by setting the demarcation for sufficiency to absolute 1. 

The justified opinion for fingerprint source attribution can be made on partial, fragmented parts of whole, complete fingerprints.  For purposes of routine casework, the default number of parts in the average fingerprint (each sufficient to establish source attribution) is set at 22 (see The Formulae), which is multiplied against the fingerprint population group of 3 billion (i.e. 10 fingers multiplied by 300 million people -- the rough total United States human population).  Subsequently the total fingerprint-part population for 300 million people in the United States is set at 66 billion.  As a result the fingerprint match probability (FMP) needed to infer positive identification for routine casework must be less than (e.g., smaller than) 1/66 billion*.

*It is significant to note here that 1/66 billion is the rough equivalent of the match probability used by the FBI to match DNA profiles for routine casework [84].  

NAS and IAI Recommendations

As a result of the National Academy of Sciences (NAS) report released on 2/18/2009 titled Strengthening Forensic Science in the United States:  A Path Forward [Link], the International Association for Identification (IAI) made the following recommendations to members on 2/19/2009 [Link]:

"It is suggested that members not assert 100% infallibility (zero error rate) when addressing the reliability of fingerprint comparisons." 

and,

"Although the IAI does not, at this time, endorse the use of probabilistic models when stating conclusions of identification, members are advised to avoid stating their conclusions in absolute terms when dealing with population issues." 

The IAI acknowledgment of a non-zero error rate for ACE-V methodology and that fingerprint identification conclusions should not be stated in absolute terms supports the use of a probabilistic approach to fingerprints. 

It is significant to note here that the use of subjective probability in fingerprint identification was supported by forensic scientists Joshua Bergeron, Glenn Langenburg and Cedric Neumann during the State of Minnesota v. Jeremy Jason Hull Frye-Mack hearing [Link] in 2008.

NEXT PAGE >>>

Historically, latent print examiners have testified to conclusions of identification or exclusion with absolute certainty and a confidence level of 100%.   However, notable mistakes have been made in the field of fingerprints, e.g., Madrid Error,  which means a confidence level of 100% cannot exist.

As a result, latent print examiners who testify to their conclusions with a confidence level of 100% or absolute certainty exaggerate the strength of the evidence and therefore mislead the trier of fact.

Although the T-Model has a tested zero error rate to make erroneous conclusions, the theoretical possibility for error always exists.  Therefore statements regarding the significance of a fingerprint match, or non-match, are expressed as an inference and with a confidence level that borders on certainty.

On February 18, 2009 the National Academy of Sciences (NAS) publicly released its report to Congress recommending fundamental scientific research and standards to strengthen the forensic sciences in the United States, including fingerprints (see below links).

Information on NAS Committee Members  1 

Executive Summary of the NAS Report  2 

Summary of NAS Report's Recommendations   3

Absent fundamental scientific research and national standards for fingerprints, the fingerprint community is in need of an interim solution that moves toward a probabilistic approach for the discipline which at the same time provides scientific support and help to defend it, if necessary in court.