The T-Model has demonstrated robustness to reliably define conservative, relatively accurate numbers of look-alikes likely to occur in precisely defined fingerprint populations.  It has demonstrated the ability to accurately ferret out the largest and best look-alikes ever recorded as insufficient to individualize.  It has also demonstrated the ability to accurately identify the most notable erroneous fingerprint identifications ever recorded as insufficient to individualize. 

As a result, the T-Model is deemed sufficiently worthy for continued extended validation studies and sufficiently complete to be submitted for critical review by the broad scientific community.  As a result, researchers, scientists and fingerprint examiners are solicited to:

1. Perform independent empirical validation studies in order to corroborate or falsify the robustness of the T-Model to define conservative numbers of look-alikes likely to occur based on clusters of ridge unit types for fixed fingerprint populations similar to the experiments performed by the author (see Validation Study).  Based on SWGFAST guidelines for Validation of Research and Technology, it is recommended that validation studies should be completed by a scientific, scholastic, and/or professional organization not affiliated to the author.  As a result, to insure independent results, experimentalists preferably should not be affiliated with the author’s past or employer, i.e. city of Hayward or city of San Jose.  All validation 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).
2. Test criteria for reduced levels of ridge formation clarity, reliability and quality of agreement and the subsequent reduction factors, if needed, with the condition that any changes do not cause the number of calculated look-alikes for the Chesapeake IAFIS or Clark non-matches to fall below 1. 
3. Perform an independent frequency study based on the ridge unit approach utilizing a significant fingerprint sample to corroborate, or refine, adjusted quantitative weights for ridge formation types based on results reported by Osterburg, Santamaria, the MTV study, and the author.
   
4. Test the reliably of the model to reliably ferret out look-alikes and identify any known erroneous fingerprint identification as bearing a calculated number of look-alikes to be  greater than 1.  
   
5. Report findings of a larger and better amount of corresponding ridge formations in a known non-match that with conservative lower bound values exceeds the aggregate amount found in the Chesapeake IAFIS or Clark non-match.
6. Report findings of any individualization which based on relevant fingerprint population bears a number of calculated look-alikes to be greater than 1 and nevertheless, the examiner asserts valid basis to individualize.
   
7. Write a computer program that calculates the number of look-alikes likely to occur based on variable fingerprint populations that utilizes values for ridge formations, expansion factors, reductions factors, etc.  The source code and values should be made available for refinement, when needed.

Copies of validation testing materials are requested, either hardcopy or recorded at 1000 DPI resolution, and may be forwarded to the author at the following mailing or email address: 

Henry Templeman, CLPE
San Jose Police Department
Central Identification Unit
201 W. Mission Street
San Jose, CA  95110
(408) 277-5104
henry@henrytempleman.com

The T-Model is hardly all encompassing and any comments or suggestions to improve it are welcome and sincerely appreciated.  In any case, please be critical if you see an error.