Any fingerprint match probability (FMP) model that anyone designs, proposes, or even applies to criminal casework, should be considered as nothing more than an "idea", and the sole test of the validity of any idea is experiment.

Regardless how interesting, nice, or appealing any particular fingerprint model may appear, and regardless of the authority, credentials or educational background of the individuals responsible for it's design, if the model does not agree with experiment, then it's wrong.  

"The sole test of any idea is experiment"

Richard P. Feynman, Six Easy Pieces

Video of Richard P. Feynman:  Take the World from Another Point of View

 Video of Richard P. Feynman:  Key to Science

The Experiments

The reader is encouraged to perform the same below experiments to find out for themselves how many close matches for a given arrangement of "ridge features types in position" are present in a relatively clear 1000 flat fingerprint population group.  The experiments are easy, fun to do, and a great way to learn about the extent friction ridge look-alikes exist in fingerprints. 

The author applied, in large part, the following suggestions by Glenn Langenburg and Christophe Champod how to commence validation studies involving friction ridge close matches or "look-alikes" by experiment :

“I would recommend that you do calculate some very simple probabilities for 3-5 minutiae in arrangements using your approach.  Predict the value based on your model, and then go and search and see how often they appear.”

Glenn Langenburg  1/1/2008

‘What is required [for validation] is a clear definition of a “close match”.  This can only be done in relation to distortion tolerances.  My advice would be as a first health check:  pick 10 configurations of 3-4 minutiae from marks of known donors.  Define the tolerances associated with them (using multiple known impressions from these donors). The tolerances fix the search parameters.  Look in a large collection (say 10,000) how many close matches as defined can be found.  If you match probability is in the order of 1/1,000, on average you expect to find 10 close matches.  If that sample test fails, then it is not a good sign for the modeling assumptions.”

Christophe Champod   1/6/2008

Previously, the author performed 50 experiments in order to test the ability of the T-Model to estimate relatively accurate numbers of fingerprint friction ridge close matches or look-alikes present in fixed fingerprint population groups.  This type of test was considered extremely important because if the model failed to estimate, with relative accuracy, how many friction ridge close matches or look-alikes would likely occur in a given fingerprint population, then it could never establish basis for inference of identity (e.g., inference for identification depends on fingerprint population because as fingerprint population increases so does the number of close matches or look-alikes present in that population.  Only when the examiner, or fingerprint model, can best predict there will be less than 1 close match or look-alike in the relevant fingerprint population for the case at hand can there be valid basis to infer identification to a single source.). 

The 50 experiments previously performed by the author utilized the following small fingerprint population groups:  126, 37.5, 208, 263 and 113.  The results of these experiments were for the most part quite encouraging and helped to develop T-Model v. 7.0. 

In February 2010, the author performed similar fingerprint experiments involving a fingerprint population size of 1000, e.g., a much more definitive test.  The results of these experiments caused significant modification to the T-Model 7.0 formulae and resulted in the present version:  T-Model v. 8.0.

As a side experiment, the author also tested the ability of latent print examiners to "estimate numbers of fingerprint friction ridge look-alikes present in fixed fingerprint populations" using  solely professional judgment, e.g., training and experience.  The results from these tests were compared with estimates made the T-Model v. 8.0 utilizing the new formulae.  The estimate made by human latent print examiners and T-Model v. 8.0 were then compared to results from experiment.  The results from these tests are as follows: 

Number of Look-alikes Estimated by Latent Print Examiners

Experiment No. 1

In February 2010, 12 latent print examiners (6 CLPEs) were asked to examine an arrangement of "3 ending ridge features located in a funnel with 0 intervening ridge counts to each nearest neighbor" (see below image no. 4 derived from images nos. 1-3).  The examiners were then asked to give their best estimate of the number of “close matches” likely to occur in a fingerprint population of 1000 relatively clear flat fingerprints selected at random based only on their professional judgment, e.g., training and experience.

The examiners were instructed not to consider matching continuous ridge unit shapes, e.g., ridge unit edge contours and thicknesses of the continuous ridge units attached to the ending ridge units and which form to make the whole ridges.  For this particular Level II fingerprint ridge feature  experiment, the examiners were asked to only consider the ending ridge unit types located in a funnel (see below 4 images) and how many "close matches" would be in 1000 flat fingerprints.

Based on results from previous friction ridge skin elasticity experiment, a “close match” was roughly defined as an arrangement of ridge features bearing the same ridge feature types, with the same orientation, with the same intervening ridge count to each nearest neighbor, and with a distal measurement between ridge features that does not to exceed a difference beyond 20% (see Friction Skin Elasticity).  

  Image No. 1

Original arrangement of 3 ending ridges in a funnel with 0 intervening ridge counts to each nearest neighbor(Experiment No. 1)

  Image No. 2

Arrangement of 3 ending ridges in a funnel with the 3 ending ridge units marked in red (Experiment No. 1)

Image No. 3

Arrangement of 3 ending ridges in a funnel with the 3 ending ridge units and ridges showing the funnel pattern marked in red (Experiment No. 1)

Image No. 4

12 latent print examiners (6 IAI certified latent print examiners (CLPE) and 6 non-certified latent print examiners) were asked to give their "best estimate" for how many close matches for the above arrangement of "3 ending ridges in a funnel" are in 1000 flat fingerprints (Image no. 4).  The results were as follows:

6 CLPEs:  333, 200, 56, 500, 650, and 300 (average  ≈ 340)

6 non-CLPEs:  70, 20, 100, 550, 150 and 700 (average  ≈ 265)

Cumulative average ≈ 302

Number of Look-alikes Estimated by T-Model v. 7.0 and T-Model v. 8.0

(Experiment No. 1)

Two (2) fingerprint match probability models were tested to estimate the number of close matches for the arrangement of 3 ending ridges in a funnel present in a flat fingerprint population of 1000:  T-Model V. 7.0 and and T-Model v. 8.0.

The number of close matches estimated as follows:

T-Model v. 7.0:  80

T-Model v. 8.0:  40

T-Model v. 8.0 defines the value for the average flat fingerprint using Level II ridge detail only (not values for pores and not values for contours and widths of continuous ridge units).  As a result the formula T^P=10^240 changes to T^P=10^120.  Subsequently, the number of estimated close matches is cut in half (see The Formulae). 

Note:  The value 10^240 for the average flat fingerprint, e.g.,  inclusive of Level I, II and III ridge detail, should only be applied to fingerprints in which Level III is relied upon to establish inference for identification.

Number of Look-alikes Found by Experiment (Experiment No. 1)

In order to test which method produces more accurate results, e.g., latent print examiner professional judgment or statistical probability modeling, the following experiments were performed:

The author selected 1000 flat fingerprints (from 100 ten-print records) at random from criminal ten-print files at the San Jose Police Department Central Identification Unit.  The criteria for selection included the condition that all 10 flat fingerprints on each ten-print record were visually clear (not faint), absent distortion markers (e.g., minimal /no smudging, minimal/no smearing, etc.), minimal/no obstruction due to lettering on the ten-print card, and minimal/no cut off due to the edges of the card.  The author examined each of the 1000 flat fingerprints and searched for “close matches” for the above arrangement of "3 ending ridges in a funnel" (Image no. 4) based on the above criteria for a “close match”. 

The number of “close matches” found by the author was 39.

(Note:  The author found 21 “near” close matches.  However, each of these arrangements were close to but failed to fall within the 20% friction ridge skin elasticity threshold requirement and as a result were not counted.)

Experiment No. 2

In order to check the accuracy of the results from Experiment No. 1, the author randomly selected a 2nd different arrangement of 3 ending ridges in a funnel with 0 intervening ridge counts to each nearest neighbor (the arrangement differed only in terms of spatial relationship with respect to each other).  See Image No. 8. 

   Image No.5

Original arrangement of 3 ending ridges in a funnel with 0 intervening ridge counts to each nearest neighbor(Experiment No. 2)

  Image No.6

Arrangement of 3 ending ridges in a funnel with the 3 ending ridge units marked in red (Experiment No. 2)

 Image No.7

Arrangement of 3 ending ridges in a funnel with the 3 ending ridge units and ridges showing the funnel pattern marked in red (Experiment No. 2)

 Image No.8

For Experiment No. 2, the author examined each of the 1000 flat fingerprints and searched for “close matches” for the above arrangement (Image No. 8).   The number of close matches found was 30.

Note: The author found 22 “near” close matches.  Each of these arrangements were close to but clearly failed to fall within the 20% friction ridge skin elasticity threshold requirement and as a result were not counted.

Experiment No. 3

In order to check the accuracy of the results from Experiment No. 1 and No 2, the author randomly selected a 3rd different arrangement of 3 ending ridges in a funnel with 0-1 intervening ridge counts to each nearest neighbor (the arrangement differed only in terms of spatial relationship with respect to each other).  See Image No. 12. 

   Image No.9

Original arrangement of 3 ending ridges in a funnel with 0-1 intervening ridge counts to each nearest neighbor(Experiment No. 3)

Arrangement of 3 ending ridges in a funnel with the 3 ending ridge units marked in red (Experiment No. 3)

  Image No. 11

Image No. 12

For Experiment No.3, the author examined each of the 1000 flat fingerprint and searched for “close matches” for the above arrangement (Image No. 12).   The number of close matches found was 33. 

Note: The author found 28 “near” close matches.  Each of these arrangements were close to but clearly failed to fall within the 20% friction ridge skin elasticity threshold requirement and as a result were not counted.

See below "conclusions" regarding interpretation of data gathered as a result of these 3 experiments.

Extended Validation Study (Currently in Progress)

For purposes of extended validation study, fingerprint experiments involving numbers of close matches of different arrangements of different fingerprint ridge feature types in 1000 flat fingerprints were used.  In each case, estimates made by T-Model v. 8.0 will be compared again to actual numbers of close matches found by experiment.

Number of Look-alikes Estimated by Latent Print Examiners

Experiment No. 4

In February/March 2010, 12 latent print examiners (6 CLPEs) were asked to examine the below arrangement of 3 ending ridge features not located in a funnel (see below image no. 16).  The examiners were then asked to give their best estimate of the number of “close matches” likely to occur in a fingerprint population of 1000 relatively clear flat fingerprints selected at random based only on their professional judgment, e.g., training and experience.

The examiners were instructed not to consider matching continuous ridge unit shapes, e.g., ridge unit edge contours and thicknesses of the continuous ridge units attached to the ending ridge units and which form to make the whole ridges.  For this particular Level II fingerprint ridge feature  experiment, the examiners were asked to only consider the ending ridge unit types not located in a funnel (see below 4 images) and how many "close matches" would be in 1000 flat fingerprints.

Based on results from previous friction ridge skin elasticity experiment, a “close match” was roughly defined as an arrangement of ridge features bearing the same ridge feature types, with the same orientation, with the same intervening ridge count to each nearest neighbor, and with a distal measurement between ridge features that does not to exceed a difference beyond 20% (see Friction Skin Elasticity).    

The below arrangement of "3 ending ridge features not in a funnel with 0-1 intervening ridge counts to each nearest neighbor" (Image No. 16) used to serve as the original “latent” impression.   Note:  The same 1000 flat fingerprint sample used in the previous 3 experiments was used to search for “close matches” based on same criteria for a close match.

Image No.13

Original arrangement of 3 ending ridges not in a funnel with 0-1 intervening ridge counts to each nearest neighbor (Experiment No. 4)

  Image No. 14

Arrangement of 3 ending ridges not in a funnel with the 3 ending ridge units marked in red (Experiment No. 4)

  Image No. 15

Image No. 16

The best estimates by the 12 latent print examiners (6 IAI certified latent print examiners (CLPE) and 6 non-certified latent print examiners)  for how many close matches for the above arrangement of "3 ending ridges not in a funnel" (e.g., Image No. 16) are in 1000 flat fingerprints were as follows:

6 CLPEs:  100, 38, 200, 400, 730, and 250 (average  ≈ 286.3)

6 non-CLPEs:  95, 48, 2, 30, 20, 5 (average  ≈ 33.3)

Cumulative average ≈ 159.8

Number of Look-alikes Estimated by T-Model v. 7.0 and T-Model v. 8.0

(Experiment No. 4)

The same two (2) fingerprint match probability models were tested to estimate the number of close matches for the arrangement of "3 ending ridges not in a funnel" present in a flat fingerprint population of 1000:  T-Model V. 7.0 and and T-Model v. 8.0.

The number of close matches estimated are as follows:

T-Model v. 7.0:  24

T-Model v. 8.0:  12 

Calculation:  The number of close matches for 3 ending ridges not in a funnel is estimated by T-Model v. 8.0 as follows (see The Formula):

(T) ^ (P) = F

Where,

T = T-Value for the arrangement of ridge features.
P = Number of Parts in the average flat fingerprint (F)
F = 10^120 (the T-Value for the average flat fingerprint

L = (R)(P) / T

Where,

L = Number of Look-alikes
R = Relevant Fingerprint Population
P = Number of parts in the whole average flat fingerprint
T = T-Value for the particular arrangement of ridge features in a single impression or in agreement in two impressions

Calculation

T^P = 10^120

2893.64 ^ P = 10^120

P = 34.6676

L = (1000)(34.6676) /2893.64

L =  34667.6 / 2893.64

L = 11.98

Number of Look-alikes Found by Experiment (Experiment No. 4)

The author searched the same 1000 flat fingerprints used in the previous 3 experiments for 'close matches" for the above arrangement of 3 ending ridges not in a funnel (Image No. 16).  The number of close matches found was 12. 

Note: The author also found 14 “near” close matches in the same 1000 flat fingerprints.  Each of these arrangements were close to but clearly failed to fall within the 20% friction ridge skin elasticity threshold requirement and as a result were not counted.

Experiments  No. 5

A 2nd different arrangements of "3 ending ridges not in a funnel" was used to further test the results from experiment no. 4.

Image No.17

Original arrangement of 3 ending ridges not in a funnel with 0-1 intervening ridge counts to each nearest neighbor (Experiment No. 5)

  Image No. 18

Arrangement of 3 ending ridges not in a funnel with the 3 ending ridge units marked in red (Experiment No. 5)

  Image No. 19

Image No. 20

Number of Look-alikes Found by Experiment (Experiment No. 5)

The author searched the same 1000 flat fingerprints used in the previous 3 experiments for "close matches" for the above arrangement (Image No. 20).  The number of close matches found was 7 or 12*.

*5 of the 12 close matches involved what the T-Model describes as a "ridge break unit", e.g., a separation between a continuous ridge of approximately .5mm.  If arrangements bearing these ridge types are counted, then the number of close matches equal 12.  If they are not counted then the number of close matches equal 7.  

Note: The author also found 17 “near” close matches in the same 1000 flat fingerprints.  Each of these arrangements were close to but clearly failed to fall within the 20% friction ridge skin elasticity threshold requirement and as a result were not counted.  

Conclusions (Based on 5 completed fingerprint experiments)
 
The following conclusions can be drawn from the results from the above 5 experiments:

1.  Clusters of close matches or look-alike amounts of ridge feature types exist in fingerprints made by different individuals.

2.  The average number of look-alikes found in each group was equal to or slightly less than the results predicted by T-Model v 8.0. 

Conclusion (Based on results from Experiment No. 1 - No. 3)

3.  The average number of close matches for "3 ending ridges in a funnel" that latent print examiners estimate will be present in 1000 flat fingerprints is roughly as follows:  340 for CLPEs, 265 for non-CLPEs, 302 for both CLPEs and non-CLPEs, 80 for T-Model v. 7.0, and 40 for T-Model v 8.0.  The average number of close matches found by experiment in 1000 flat fingerprints for arrangements of 3 ending ridges in a funnel is approximately 34.

Results from these 3 experiments supports the following theories:

• T-Model v. 8.0 is the most accurate tool compared to other methods noted to estimate numbers of close matches for ending ridge units in a funnel in a fingerprint population group of 1000. 

• Any arrangement of "3 ending ridge units in a funnel" bears a T-Value, e.g., aggregate  quantitative-qualitative value, of 1000, or a fingerprint match probability of 1/1000.

• 1 ending ridge unit in a funnel bears a standard value of 10 (e.g., the cube root of 1000) or a fingerprint match probability of 1/10.

Let’s say there are three (3) different latent fingerprints with 11, 12, and 13 excellent ending ridges in a funnel that are in excellent agreement with 3 different exemplar impressions.  The T-Values for these amounts of corresponding ridge formations are defined as 10^11, 10^12 and 10^13 respectively (given that there are no corresponding pores or continuous ridge edge contours and widths in any of the latents v. exemplars).  Using the T-Model, based on an upper bound conservative 18-65 year old world fingerprint population of 24,552,000,000, the number of look-alikes likely to exist for each is 5.35, 0.49, and 0.04 respectively.  A 15% error rate translates to 4.54, 0.41 and 0.03 actual look-alikes respectively.  As a result, the number of look-alikes calculated by the T-Model are barely, yet conservatively, just above the actual or true number of look-alikes found. 

Based on “identification” defined as the number of look-alikes likely to occur in a given fingerprint population as less than or equal to 1, the above values mean there is no change in terms of whether or not any of the 3 amounts are sufficient or insufficient to infer positive identification.  In other words a 15% difference or error rate is small enough to be considered of little or no consequence when defining whether or not amounts of corresponding ridge formations in two impressions are sufficient or insufficient, especially in terms of performing routine casework which for the most part utilizes the presence of matching Level II ridge formations in sequence only.

7.  The T-Model is able to reliably predict relatively accurate conservative upper bound numbers of look-alikes for clusters of ridge formation types based on their aggregate weight and precise fingerprint populations.   These results are particularly appealing for purposes of criminal casework since conservative upper bound numbers of look-alikes provide better quality assurance to not make an erroneous individualization and are sufficiently close to the ID/No ID threshold to insure minimal numbers of valid identifications are not missed.

8.  Based on results from Experiments No. 1 - No. 3 and an observed mean of 34.33 close matches, a standard deviation of 3.68, a sample size of 3 experiments (e.g., the 3 experiments to count close matches in a flat fingerprint sampling of 1000 for an arrangement of 3 ending ridges in a funnel), the following was calculated:  A confidence interval of ± 5.47, a true population mean range of 28.86 - 39.8 with a 99% confidence level.  A "confidence interval for means calculator" was used to determine these values [Link].  The  true population range of 28.86 - 39.8 with a 99% confidence level is  extremely appealing because it is both close to and under the T-Model estimate of 40.

9.  Based on a results from the first 3 experiments, the 99% confidence level for the range of the true number of look-alikes of 28.86 – 39.8 compared to the predicted 40, provides significant support that the T-Model is a relatively accurate statistical model able to estimate conservative upper bound numbers of close matches likely to exist in a given fixed sized fingerprint population group.

10.  In order to test the likelihood (confidence level) that the results obtained from the two experiments (which resulted in the most widespread results, e.g., 39 look-alikes found in Experiment No. 1 versus 30 look-alikes found in Experiment No. 2) are significantly different, a "Z-test for two proportions calculator" was used to determine these values [Link].  This calculator is used to compare the proportions from two independent experiments to determine if they are significantly different from one another. Based on a confidence level of 99%, a sample size of 1000 fingerprints, the Z-Value is calculated to be 0.98.

(Note:  If you are testing the null hypothesis that the two proportions are equal, a two-tailed test result is used.  A one-tailed tests is used if you are trying to determine if one proportion is greater (or lower) than another.)

Based on a sample size of 1000 fingerprints, and 39 and 30 close matches found in each experiment, the actual confidence level for a 1-Tail test is 83.6% and for a 2-Tail test is 67.3%.  As a result for either the 1-Tail or 2-tail teststhe values of 30 and 39 close matches found in experiments nos 1 and 2 are not significantly different .

Corroboration of the T-Model by Independent Experiment

The following experiment corroborates, in part, the use of the Osterburg study to define frequency values for ridge features, and corroborates the idea that the ratio of the most common, significantly weighted friction ridge features used in fingerprint identification, e.g., the ending ridge, bifurcation and dot, are the same across all fingerprint regions, and corroborates the assumption that Osterburg consolidated immature, e.g., incipient, ridge features with mature, e.g., normal ridge features in his frequency study:

The Michel–Tallerico–Verceluz (MTV) Experiment

Dawn Michel, Frances Tallerico and Cesar Verceluz, Latent Print Examiners at San Jose Police Department Central Identification Unit, performed an independent frequency study of 218 fingerprints that were largely comprised of right flat thumbs from different individuals (the experiment was conducted as a classroom training exercise for new examiner trainees).  The impressions were selected at random from criminal ten-print records.  Only clear, reliable, e.g., absent distortion marker, flat fingerprint impressions were used.  Individual mature and incipient friction ridge features, e.g., ending ridge units, bifurcating ridge units and single ridge units (dots), were combined and counted in each sample.  The results were then verified in the rolled fingerprint sample on the same ten-print record. 

The results from the MTV study were compared to the extrapolated results from the Osterburg study (based on a "ridge unit" approach). The ratio of the frequency results for the different ridge feature types were as follows:

 

Osterburg Study

Ratio of bifurcations to ending ridges - 1 : 1.88           
Ratio of bifurcations to dots - 1 : 3.89
Ratio of dots to ending ridges - 1 : 6.42

MTV Study

Ratio of bifurcations to ending ridges - 1 : 1.87           
Ratio of bifurcations to dots -  1 : 3.84
Ratio of dots to ending ridges - 1 : 6.64


Conclusions

The MTV study corroborates the theory that numerical ratios for the most frequently used friction ridge features used in fingerprint identification defined by the Osterburg study are valid.

The Osterburg study utilized roughly a 221mm2 surface area, e.g., roughly 13mm x 17mm, to count the number of ridge features observed in each fingerprint sample.  The area used to place the millimeter grid was the center portion of each fingerprint.  The center portion of fingerprints generally contains the core and not the periphery regions of fingerprints.  The MTV study utilized predominantly right thumbs which are known to contain significant amounts of distal periphery regions of the thumb, e.g., the tip area.  Subsequently, the MTV study corroboratesthe theory that the ratios of the most common, significantly weighted fingerprint ridge feature types, e.g., the ending ridge unit, the bifurcating ridge unit and the single ridge unit, e.g., dot, are the same across all fingerprint regions.

Osterburg did not distinguish between the immature, e.g., incipient, ridge unit type and the mature or normal ridge unit type in his frequency table (see Osterburg Frequency Table). The MTV study corroborates the theory that Osterburg consolidated these ridge feature types.

The T-Model is More Accurate than ACE-V

The following experiment was performed to test the accuracy of expert fingerprint examiners to correctly identify amounts of corresponding ridge features in a look-alike as insufficient to identify:

Experiment

The Chesapeake IAFIS Non-Match, the largest and best look-alike ever seen, was used for this experiment (see Chesapeake IAFIS Non-Match) . The

look-alike images were rotated 90 degrees clockwise and horizontally mirrored (to help guard against recognition).  Nine (9) sections of the look-alike were cropped using Adobe Photoshop in a manner so that only corresponding ridge features were left showing.  Each section displayed incrementally larger numbers of corresponding Level II ridge features, so that in each image there were from 4 to 12 matching Level II ridge features.

Then, nine (9) expert fingerprint examiners (including 6 CLPEs) were shown each image in succession and asked whether or not the amount of matching ridge detail present in the two images was enough to establish positive identification based on conventional "ACE" fingerprint methodology, e.g., utilizing only human intuition and subjective judgment to render an "expert opinion".

Note:  The fingerprint examiners tested conformed to no pre-determined minimum standard needed to establish positive fingerprint identification.  In addition they were not given any prior knowledge regarding the latent v exemplar in terms of relevant population (the fact that the "match" was found as a result of an FBI IAFIS search of a fingerprint database containing roughly 530 million fingerprints was not revealed to the examiners).  

The results for the above experiment were as follows: 

At different stages during the experiment, three (3) expert latent print examiners (including at least 1 CLPE) were prepared to establish "positive identification" based on the amount of matching ridge features displayed in the look-alike images.  As a result, 1 out of 3, or 33.3%, of the expert fingerprint examiners tested were fooled by the look-alike.   

Conclusion

The 33.3% error rate for "ACE" alone reflects significant inability for expert latent print examiners to reliably and accurately identify amounts of matching ridge features in two impressions as insufficient to establish positive identification when faced with the largest and best look-alike ever recorded. 

As a result, the error rate for "ACE-V", which represents  the error rate for 2 expert examiners performing an independent examination when faced with the largest and best look-alike ever seen, is calculated as 1/3 x 1/3 = 1/9, or 11.1%.

Note:  It is significant to note here that sufficiency to establish positive identification with a reasonable degree of scientific certainty depends on relevant population because as the population increases so does the number of look-alikes.  

In contrast to the above study, the T-Model was able to accurately and reliably identify each successive amount of matching ridge features in this look-alike, including every largest and best look-alike ever recorded as well as the the most notable erroneous fingerprint identifications ever recorded, as insufficient to establish positive identification [Link].  

As a result, when faced with the largest and best look-alikes ever recorded, the T-Model has a 0% error rate.

Prediction

The T-Model empirical probability approach to fingerprint identification is more accurate than conventional ACE-V fingerprint methodology, e.g., fingerprint expert decision-making based solely on human intuition and subjective judgment, to reliably and accurately identify amounts of matching ridge features in two impressions as insufficient to establish positive identification when faced with the largest and best look-alike ever recorded.

Based on deductively testing both theories, the T-Model theory, while unfalsified, is better than conventional ACE-V fingerprint methodology, e.g., an ACE-V which fails to define pre-determined minimum threshold probability estimates needed in two impressions in order to establish inference for positive identification and also fails to consider what is the relevant population for the case at hand.

Based on the above results, T-Model Theory has greater predictive power than conventional ACE-V fingerprint methodology.  This idea is consistent with the fact that the T-Model is grounded in empirical content, e,g., simple experimentation.  For Karl Popper, one of the greatest philosophers of science in the 20th century, the following statement supports this idea:

"[For Popper] any theory X is better than a ‘rival’ theory Y if X has greater empirical content, and hence greater predictive power, than Y."

[Stanford Encyclopedia of Science]

It is significant to note here that conventional ACE-V is not entirely replaced by T-Model theory but rather refines it with statistical probability theory with the following significant exception:

The "V" for "verification" in "ACE-V" comes from the word "verify" which means "to prove true" or "authenticate".  That is the root meaning and true implication of the word.  Not only can no scientific theory be proved true or authenticated (this idea is supported by Karl Popper and noble prize scientist Richard Feynman), but also no latent fingerprint identification can be proved true or authenticated.  There is always the theoretical chance the "identification" can be a look-alike and you can be wrong.

The National Academy of Science report makes it clear that fingerprint identification is probabilistic in nature and therefore fingerprint identification can never be verified or proved true with absolute certainty.  As a result, the "verification" in ACE-V is misleading and exaggerates the weight of fingerprint evidence.  Subsequently, the idea that fingerprint examination requires the examination by two independent examiners in order to be "scientific" is a fallacy.  For purposes of quality assurance, and for that reason only, should a second examiner perform a technical review or check of the initial examiner's work.  The proper term to replace "verification" is either "corroboration" or "agreement".

Lastly, it is significant to note that unlike conventional ACE-V fingerprint methodology, the T-Model forbids conclusions of positive identification for amounts of corresponding ridge features in two impressions that fail to meet the minimum probabilistic threshold for the case at hand.  All true scientific theories are prohibitive. 

A validation study to determine the error rate for the T-Model to perform fingerprint analysis is presented in a format in line with the National Academy of Sciences, e.g., NAS Report (See Error Rate in Terms of Best Look-alikes).

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Henry Templeman encourages the broad scientific community to test the results from the experiments describes on this web site, to perform broader experiments in order to corroborate, falsify or refine the T Model.     

Validation studies should be documented in a manner to ensure that any qualified individual could evaluate what was done and replicate the validation process.  Documentation should be in the form of hard copy fingerprint cards, photographic, or digital records of fingerprint samples used, with notes or reports of findings, which includes reference material.  Documentation of external validation must identify the name and professional affiliation of the person(s) conducting the study, date, as well as the research question, procedures, results and conclusion(s). 

Any independent experiments that corroborate or falsify results from experiments performed by the author  should comply with SWGFAST guidelines established for Validation of Research and Technology [61]. 

For the above experiments, it is significant to note that ridge edge contours and widths in each look-alike failed to precisely agree.  However, the absence of such agreement is not a factor that reduces the weight for the ridge formations and/or ridge unit types in agreement.  The absence of ridge edge contour and width agreement means no additional weight is factored into the aggregate value for the total ridge formations observed.  Since the absence of level III ridge detail agreement is not a negation or exclusionary factor, the values assigned for the above ridge formations found in agreement were interpreted as the same and therefore assessed the same aggregate value.