"A reasonable probability is the only certainty."

Edgar Watson Howe

The T-Model integrates standard probability theory and statistics into traditional "ACE-V Methodology" and provides fingerprint examiners with a tool to define conservative fingerprint match probabilities (FMP) for amounts of corresponding ridge formations present in two impressions based on the relevant population for the case at hand. 

Basic foundations for the T-Model which have been previously supported by the FBI are as follows:

1. Utilizes the ridge unit approach to illustrate a ridge (as opposed to compound ridge formations), and

2. Defines numerical values for ridge features based on their frequency of occurrence in terms of ridge formation type [in position], and

3. Considers relevant fingerprint population.

These basic foundations are supported by position statements published by the Federal Bureau of Investigation, U.S. Department of Justice, in the June 1972 FBI Law Enforcement Bulletin "An Analysis of Standards in Fingerprint Identification" [49] as follows:

1. “From the general working standpoint, the types of characteristics can be narrowed to the ridge ending, the bifurcation, and dot.  Two additional characteristics are commonly distinguished or mentioned because of the ease with which they are visually discernible as distinctive formations.  These are the short ridge and the enclosure or island.  Technically, however, unless these formations are extremely short length or extent, they can be used legitimately as two ridge endings instead of a short ridge and as two bifurcations instead of an enclosure.”

2. “It is possible to assign each [ridge characteristic based solely on frequency of occurrence data] a relative numerical (weight) value which as a result could be used as a minimum standard to establish identity.”

3. “From a theoretical standpoint any fingerprint, when it is identified, needs to be distinguished from every other fingerprint existent in the world.  A little reflection will show, however, that this greatly exceeds the practical aspects of identification, since in the average case a crime scene print is initially an investigative tool to identify a suspect who was in the same country, State, or locality at the time the crime was committed.”

It is significant to note here the FBI also reported that statistical figures failed to correspond with the values assigned for characteristics based on their frequency and as a result indicated it was not a reliable method to be used to define a minimum standard to establish identity.  However this non-correspondence was [likely] the result of false calculation.  The report states that ridge formation values were “added”.  Ridge formation values should have been subject to the probability rule for independent variables, or product rule, and therefore not added, but multiplied.  As a result, their products and not sums should have been calculated and applied to the statistical figures.   The T-Model makes this correction and applies the product rule, similar to how random match probability is calculated for DNA profiles, in order to define match probabilities for amounts of ridge formations based on the relevant  population for the case at hand.

In addition to using a ridge unit approach, ridge unit frequency, and relevant population, to define discrimination values for individual amounts of ridge features, and define match probabilities for amounts of corresponding ridge features found in two impressions, the following additional variables serve to complete the foundations for the T Model:

Pattern Force [Link]

Friction Ridge Skin Elasticity Threshold [Link]

Intervening Ridge Count to Nearest Neighbor (Level II) [Link]

Ridge Unit Clarity and Reliability [Link]

Ridge Unit Quality of Agreement [Link]

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The T-Model is intended to serve as a tool for latent fingerprint examiners to express criteria to assert sufficiency to infer identification in a more precise form than has been done in the past, and to reliably ferret out and identify look-alikes in order to help prevent erroneous identification. 

The T-Model is designed to provide quality control for examiner work performance in which examiner interpretation of ridge formation types, assignment of quantitative weights, and assessment of ridge formation clarity and reliability, and quality of agreement between pairs of corresponding ridge formations in two impressions can be inspected critically. 

The T-Model is also meant to be used as a way to scientifically validate problem, complex or borderline latent fingerprints as sufficient to infer positive identification and invalidate previous latent fingerprint identifications bearing amounts that are insufficient to infer positive identification with a degree of probability that borders on certainty, and as a result potentially exonerate innocent persons wrongly convicted.

The following high profile erroneous fingerprint identifications illustrate the need to minimize the likelihood for expert fingerprint examiners to make wrong conclusions:

Brandon Mayfield [22] [58]

Shirley McKie [22] [57]

Stephen Cowans [52]

As a result of these wrong conclusions and inability of expert  examiners to elaborate on criteria for sufficiency to establish positive identification, judges have ruled to limit and disallow fingerprint evidence in the courtroom, i.e. Maryland v. Bryan Rose [55] [64] and Maryland v. Lamont Anthony Johnson [56].

There is an ever increasing pool of researchers, scientists and fingerprint examiners who do not accept the current non-statistical method of “professional judgment” to establish valid basis for sufficiency to individualize [59]. 

The T-Model is the author’s best response at this time for a possible way to express criteria for sufficiency to individualize in a more precise manner than any non-statistical or fixed-numerical approach that is currently used throughout the fingerprint community.