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Monday, January 07, 2008

 
The purpose of the Detail is to help keep you informed of the current state of affairs in the latent print community, to provide an avenue to circulate original fingerprint-related articles, and to announce important events as they happen in our field.
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Breaking NEWz you can UzE...
compiled by Jon Stimac

Suspect Says Ankle Monitor Proves He's Not Burglar PIONEER PRESS, MN - Jan 5, 2008 - ...police are trying to figure out how the suspects fingerprints allegedly turned up at the scene...

Broken Fingerprint Machine Led to Mistaken Inmate Release, Union Says MILWAUKEE JOURNAL SENTINEL, WI - Jan 3, 2008 - ...machine located in the jail's release area has been out of order for about a year...

Fingerprints, DNA Analysis Help Police Develop Leads  ERIE TIMES, PA - Jan 2, 2008 ...the lab entered the print into the FBI's IAFIS, which contains more than 47 million prints...

CSI Team is Real; the Crime's Fake FLORIDA TIMES-UNION, FL - Dec 28, 2007  ...students learn how to collect fingerprints and study blood, hair and fibers from fake crime scenes...

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Recent CLPEX Posting Activity
Last Week's Board topics containing new posts
Moderated by Steve Everist

Calls for Inquiry to be scrapped
Daktari 4705 Sun Jan 06, 2008 8:04 pm

Evidence Fabrication in South Africa
Pat A. Wertheim 4320 Sun Jan 06, 2008 1:44 pm

Technology Bah-Humbug
Charles Parker 436 Sat Jan 05, 2008 11:58 pm

Watchdog groups urge ‘CSI’ toy recalls
RedFive 208 Fri Jan 04, 2008 7:10 pm

Need published studies on results of casing processing...
Amy Miller 198 Fri Jan 04, 2008 7:04 pm

Zero Error Rate Methodology
Charles Parker 583 Fri Jan 04, 2008 2:45 am

Zero Error Rate vs. No Error Rate
Michele 377 Thu Jan 03, 2008 7:52 pm

DNA and Fingerprints
Charles Parker 622 Mon Dec 31, 2007 9:23 pm

Teaching the kids
charlton97 192 Sun Dec 30, 2007 11:44 am

(http://clpex.com/phpBB/viewforum.php?f=2)
 

 
UPDATES ON CLPEX.com


Updated the Fingerprint Interest Group web page with FIG # 27.

Updated the Detail Archives
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Last week

we had a happy holidays and hopefully a good catch-up week as we swing into 2008.

This week

we start a new regular column from Michele Triplett entitled KEPT - Keeping Examiners Prepared for Testimony.  Make sure to check out the end of each Weekly Detail for this regular column, and thanks to Michele for providing this important topic!

This week our main feature is the NIST CONOPS involving the upcoming latent print technology evaluation:
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Concept of Operations (CONOPS) for Evaluation of Latent Fingerprint Technologies (ELFT)
by NIST

http://fingerprint.nist.gov/latent/elft07/elft_p2_concept.pdf

(Rev. E, 1 Nov 2007)

1. Overview

The National Institute of Standards and Technology (NIST) is conducting a series of tests for evaluating the state of the art in automated latent fingerprint matching. The intent of the testing is to quantify the core algorithmic capability of contemporary matchers. The testing will be conducted using software-only implementations, and utilizing NIST hardware.

The umbrella project for the series of tests has been named Evaluation of Latent Fingerprint Technologies (ELFT). The scope and structure of these tests are based partly upon lessons learned from the April 2006 NIST Latent Fingerprint Testing Workshop, supplemented by technical interchanges with workshop participants and vendors. The initial round of tests was initiated in April 2007. Computer runs concluded in July, and the analysis of the results was posted on the website in September 2007.

The principal objective of this report is to provide a “snapshot” of the thinking, analysis, and planning that went into ELFT. The report is not intended to provide any test results or conclusions. These will be presented in subsequent reports. While the immediate goal of ELFT is to assess automated technology, the long-term goals go far beyond simply quantifying performance. It is fully expected that understanding the performance envelope and limitations of contemporary matchers will lead to improvements in technology. These in turn, will lead to enhanced performance for searches of ten-prints and plain impressions against unsolved latent databases/watchlists. Equally important, technology improvements will provide law enforcement the capability to search their unsolved latent fingerprints against ten-print files with greatly reduced effort.

ELFT is structured as a multi-year project, and the full impact of this work may not be felt for several years. The first part of this project, ELFT07 (07 is the year), consists of two tests, run in a “lights-out” environment. The two tests have been termed Phase I and II. Phase I is a proof of concept test, whose main purpose is to demonstrate integrity of the software in a lights-out environment. During Phase I the software will demonstrate: a) automated feature extraction from latent images; b) the ability to match these features against enrolled 10-print backgrounds; and c) generation of candidate lists. Phase II will then employ a larger database to quantify the achievable performance (“hit rate”) for automated searches.

In subsequent years (2008+) we plan to expand the above tests in several ways. First, we plan to augment the ten-print databases with a mix of rolled and plain impressions (“flats”). These will enhance NIST’s and the latent community’s understanding of the challenges of matching latents against flats. Continuing this line of investigation, we will then transition to searches of plain impressions against databases of latent images (sometimes referred to as reverse searches). Initially these tests will be restricted to single-finger searches, and subsequently will be enlarged to multi-finger latent searches.

Envisioned tests include: a) latents and mates scanned at enhanced resolution (1000 and 2000 ppi); b) latents lifted/developed/processed in diverse manner (i.e., how was the image actually produced from the latent); c) latents matched against latents; and d) searches employing new or non-traditional features (e.g., level-3 features).

NIST is also looking into the development of latent image quality measures (LIQM). The principal function of the latent image quality measure is to provide a good indication of whether a latent is amenable to automated (“lights-out”) matching. Only latent prints of
higher quality measure would be submitted for lights-out matching. The development of a suitable quality measure might require additional testing.

We have outlined above the full scope of this project with a “broad brush.” In the remainder of this document we focus on this year’s portion of the project, ELFT07. So as not to break up the flow, more detailed discussion of select topics have been moved to the end, Section 14.

2. Who Should Participate in the Tests?

Developers of latent fingerprint matcher software systems are strongly encouraged to participate in ELFT07. In addition, companies, research organizations, or universities that have developed mature prototypes, or stable research latent fingerprint matchers, are invited to participate. The latent fingerprint matching software submitted need not be a “production” system, nor be commercially available.

3. Precedence of Documents

It is intended that this Concept of Operations (CONOPS or ConOps) be the single most comprehensive document covering Latent Testing/ELFT concepts. It will be periodically revised to reflect updates in NIST’s planning, and to incorporate vendor comments and suggestions. However, this CONOPS is not guaranteed to be the most accurate for highly technical data. In the event of conflict with the Application Programming Interface (API) or the Application Form the latter two will take precedence. These two documents may be supplemented by additional reference documents in the future.

4. Test Objectives – What will be Tested during ELFT07?

As previously indicated, the primary purpose of the Phase I and II testing is to quantify the core algorithmic capability of contemporary matchers, in order to understand their strengths and limitations. In the initial tests the emphasis is on matching latents against ten-prints. (In subsequent tests this will be expanded to include other types of matching, for example latents against plain-impressions, and latents against latents.) The testing will be conducted using a software-only implementation, in a lights-out
environment, and utilizing NIST hardware and datasets. During Phase I the software will demonstrate: a) automated feature extraction from latent images; b) the ability to match these features against enrolled 10-print backgrounds; and c) generation of candidate lists.

While Phase I is primarily intended to be a proof of concept test, it will nevertheless provide a certain amount of performance statistics. Only aggregate statistics (combining all successful participants) will be published. Individual results will be disclosed only to the owner of the respective SDK.

NIST will compute (and report as an aggregate statistic) the number of “hits” in each position (first, second, third, up to 50).

Figure 5 – Specimen Cumulative Distribution

NIST will also report two overall performance metrics:

1) Metric #1 simply counts the fraction of cases in which the correct mate appears
in top position on the candidate list. This metric ignores all candidates below
top position.

2) Metric #2 gives partial credit for mates appearing in lower than top position. It
assign 1.0 to a mate in top position, 0.5 to one in second position; 0.3333 to
third position, etc. The final score is the sum of these scores divided by the
number of searches with mates. (For the validation data all searches have
mates.) This score will always be at least as high as metric #1, and will
generally be somewhat higher. The highest possible score is 1.0 and the lowest
is 0.

NIST will also use DET performance metrics as a primary indicator of one-to-many
identification search accuracy. This involves plotting False Acceptance Rate (FAR) and
True Acceptance Rate (TAR) for all values of the threshold. (Equivalently, one can use
false rejection and false acceptance rates.)

For Phase II we will employ a larger database so as to improve the statistics. The results will be published as aggregate statistics, as well as individual participant statistics (for those participants electing to continue to Phase II). NIST will also report enrollment and search timing information. Speed of execution, for both enrollment and latent search, is of secondary importance. However, in order to conduct these tests in a reasonable amount of time NIST must impose some limitations. These are covered in Section 11. In reporting timing measurements NIST will specify the exact hardware that the software was hosted on. NIST will in addition caveat timing measurements by noting that operational latent searching algorithms are likely to be implemented in more sophisticated hardware.

NIST recognizes that latent searches pose many special problems. One of these is that “strong hits” may have widely different matcher scores (for example, depending upon the number of minutiae). This may place challenges on the DET approach. Largely for this reason we have recommended the inclusion of a normalized score that attempts to compensate for the matching score variations. See Sections 13 and 14 for further discussion.

A comprehensive discussion of performance metrics and their definitions is found Patrick Grother, Ross Micheals, and P. Jonathon Phillips, Face Recognition Vendor Test 2002 Performance Metrics, 31 March 2003. See also http://ois.nist.gov/nistpubs/technipubs/recent/search.cfm?dbibid=13808

5. Publication of Participation and Results

NIST understands that this project is entering a relatively unexplored field, and many challenges lie ahead. For this reason we have structured ELFT07 to include two phases. We consider Phase I to be a Proof of Concept Test. This means that the primary objective of Phase I will be to demonstrate that the submitted SDK executes on the Phase I data to completion, in a lights-out environment, and produces a “meaningful” output. A “meaningful” output is basically an output in the correct format.

The detailed results of Phase I will be discussed with the Participant on a “one-on-one basis,” but will not be published or submitted to other government agencies. The number, but not the names, of participants who attempted and completed Phase I will be disclosed. However, in the (likely) event there are a significant number of participants in Phase I, NIST is considering publishing the aggregate test results (under the premise that this may be a fair assessment of the state of the art). By “aggregate test results” we mean that results are “lumped,” and that no specific candidate list or participant-specific scores will be mentioned. Participants will have the option to withdraw anonymously following participation in Phase I. (This means that their withdrawal will not become a public announcement.)

Participants who elect to continue to Phase II may resubmit their SDKs. These need not be identical to those of Phase I. Following completion of Phase II testing the Government will combine all results into a Final Report. The Evaluation of Latent Fingerprint Technologies Test, Phase II Final Report will contain, at a minimum, descriptive information concerning ELFT07, descriptions of each experiment, and aggregate test results. Should individual participant’s scores be published, NIST will exercise care that any implied rankings are well supported by the underlying statistics. (That is, two scores should be considered essentially the same if the difference is significantly less than the error bar.)

Participants will have an opportunity to review and comment on the Final Report. Participants’ comments will be either incorporated into the main body of the report (if it is decided NIST reported in error) or published as an addendum. Comments will be attributed to the participant. After the release of the Phase II Final Report, Participants may decide to use the results for their own purposes. Such results shall be accompanied by the following phrase: “Results shown from the Evaluation of Latent Fingerprint Technologies Test (ELFT07) do not constitute endorsement of any particular system by the U. S. Government.” Such results shall also be accompanied by the Internet address (URL) of the ELFT07 Final Report on the ELFT07 website.

For Phase III and beyond NIST intends to publish statements of the performance of all implementations submitted for testing. These will include measurements of identification error rates and throughput. These results will be attributed to participants. Accordingly, NIST will require an appropriately signed application form from all participants and NIST will not evaluate any implementation unless the participant consents to the disclosure of its performance. The NIST tests use sequestered images. These will not be provided to participants.

6. Protection of Participant’s Software

NIST recognizes the proprietary nature of the participant’s software and will take all reasonable steps to protect this.

7. Why “Lights-out”?

The term “lights-out,” as used in this document, will indicate that no human assistance will be required in conducting the latent searches. In particular, all feature extraction steps, both for the enrolled images and for the latent images, must be performed entirely by the SDK under test.

There are good reasons why NIST selected the “lights-out” mode of testing:

1. It decouples the skill of the human expert from the intrinsic merits of the software.
2. It protects the privacy of the test data by keeping the data in house, and not requiring examination by non-government personnel. This mode of testing allows the use of Sensitive but Unclassified test data.
3. It encourages a forward-looking view of how latent searches might be done in the near future. It is anticipated this broader outlook will lead to technical innovations. Algorithmically speaking, “lights out” consists of two separate concepts. The first is automated feature extraction (and of course matching). The second is candidate list reduction. NIST envisages that in the near future automated search capabilities will assist latent experts by reducing the size of candidate lists that they need to examine by eliminating the more obvious “nuisance” non-matches. We refer to this part of the automated matching as candidate list reduction. To achieve effective candidate list reduction may require additional computer processing, including the development of new algorithms.

Although this is a “lights-out” test NIST will use some human assistance in the data preparation phase. Any such assistance will be provided indirectly by NIST, and might include a) cropping and/or re-orienting of selected latent images, and b) specifying a region-of-interest in the from of a mask. The mask will be a byte image conformal with the size of the latent image. Initially only two values will be used for each pixel, 0 and 255. A zero value will indicate “do not use this pixel,” while 255 will indicate a “good”
pixel. In the future these two values may be augmented by other values to indicate finer gradations of quality. NIST will also involve latent experts for examining potential consolidations and for resolving contested or unclear results.

8. Test Data

NIST will select the test datasets from its internal sources. The Test Datasets are protected under the Privacy Act (5 U.S.C. 552a), and will be treated as Sensitive but Unclassified and/or Law Enforcement Sensitive. ELFT07 Participants will have no access to ELFT07 Test Data, neither before, during, or after the test, with the exception of the small Validation Dataset.

8.1 ELFT07 Datasets

The Validation Dataset is a very small dataset intended to demonstrate that the received software (SDK) is stable and compliant with the API. Upon receiving the applicant’s SDK and Validation Dataset results, NIST will rerun the applicant’s software using the Validation Dataset. For the applicant to be officially accepted (and designated a Participant) NIST must be able to reproduce the submitted results.

For Phase I the Validation Dataset consisted of ten latent searches and 100 background ten-prints. For Phase II this will increase somewhat, say 15 searches. Problems encountered during Phase I demonstrated the need to include test cases for “exceptions,” for example missing fingers. In the event of disagreement in the two outputs, or other difficulties, the Participant will be notified. Participants will be notified with a detailed description of the problem(s) and given reasonable opportunity to resubmit.

Both Phase I and Phase II Test Datasets consist of latent images for searches and tenprints for the background (“gallery”). The Phase I Validation dataset was very “benign,” and avoided known problem cases such as: 1) very small latent area; 2) extremely busy or otherwise difficult background; 3) highly blurred image; 4) multiple fingerprint impressions; or 5) upside down or mirror images. The (main) Phase I Dataset was similar, though it did include a small number of “problem cases.” The Phase II dataset will be principally drawn from casework, but will for the most part avoid the more difficult cases.

Latent images will be supplied uncompressed, and will have been scanned at either 500 ppi or 1000 ppi. The participant should be prepared to handle either resolution. Additional image characteristics may be found in the API. Background (“gallery”) data will consist of rolled ten-print impressions, scanned at 500 ppi, and presented in a decompressed form.

8.2 Size of Images

For ELFT07 the following guidelines apply:

v All mates (background) will be rolled impressions. There will be no “flats.”
v In all cases rolled impressions will not exceed 1000 x 1000
v In all cases latent images will not exceed 2000 x 2000, though often will be significantly smaller.
v Minimum dimensions for latent images under 300 are possible, but never smaller than 150.

Over the entire series of planned tests the size of test images may vary considerably. For example, images scanned at 2000 ppi will contribute some very large sizes, potentially as large as 4000 x 4000 pixels. However, these very large sizes will only appear in the “downstream” tests.

9. Testing Platform

NIST will host the participant’s software (SDK) on a high-end PC (workstation/server type). Although these PCs include of a mix of models, a “typical” PC will have the equivalent of a Pentium 4, 2.8 GHz processor, or higher; 2 GB of memory; and at least 50 GB of disk memory. The participant software must be able to reside and execute on this single PC. NIST, at its discretion, must be able to copy the software to several PCs to expedite or scale-up the testing. These computers are configured with either a Windows 2000 or Linux operating system.

10. Format of Participant Software

The software undergoing testing will be hosted on NIST-supplied computers. The executable modules will be built up from two sources: 1) participant-supplied software provided in the form of a Software Development Kit (SDK), and 2) NIST-supplied software. The core of the executable module is of course derived from the SDK. The part supplied by NIST is mainly concerned with the image retrieval and manipulation.

13. Format of Candidate List

The output candidate list should have a fixed length of fifty (50) candidates. We have selected this size because it is short enough to be convenient, yet long enough to give an indication of the number of “hits just out of reach.” (We currently don’t envision cases in which the background is less than 50 fingers. Should this situation arise, the candidate list could be suitably “padded.”) The candidate list consists of two parts, a required and an optional part. The required part consists of: 1) the index of the mating ten-print subject; 2) the matching finger number; 3) the absolute matching score; and 4) an estimate of the probability of a match (0 to 100, see also Section 14). The optional part consists of: 5) the number of good minutiae identified in the latent; 6) the number of latent minutiae which were successfully matched; 7) the quality estimate of the latent (0 to 100, 100 is best); and 8) the quality estimate of the mate (0 to 100, 100 is best). The API provides further guidelines regarding the meaning of quality scores. The candidate list is ordered based upon the absolute score, highest score in first position.


14. Supplemental Notes

14.1 Supplemental Notes to Section 1.0 -- Concepts of Operation for “Improved Watchlist Searches”

A major goal of this project is to improve searches of watchlist/lookout-lists. It is becoming increasingly common to capture live fingerprints of arriving passengers at ports of entry and similar venues. The capture fingerprints are then compared to fingerprints of the person on file (one-to-one comparison, or validation match); they may also be searched against selected watchlists (one-to-many search).

The newly acquired fingerprints may then be matched against any or all of three types of finger prints. Verification, or one-to-one searches, are generally performed by matching with plain impression taken at a previous time. Watchlists or Lookout-lists contain the fingerprints of prior offenders, or persons of interest. They may be comprised of any of the types of fingerprints, though latent fingerprints are generallykept in separate files, exclusively dedicated to latents.

Plain-impression may be matched against any of three types of fingerprints. The simplest and clearest example of the applicability ELFT is to watchlists comprised of latents. However, there are several other ways in which ELFT may contribute to the point-of-entry scenario:

Ø Low-quality livescan images provide many of the same challenges as do latents. Improvements in latent matching should therefore transfer to real-time livescan matching. (Livescan images they are subject to “retake,” but the number of retakes is necessarily very limited, because of the need to expedite the processing.)
Ø To provide searches of watchlists in near-real-time, substantial algorithmic improvements are required. The multi-stage matching approach used by some latent matchers may offer a solution.
Ø For increased search accuracy, additional features (e.g., level 3) might be required. A goal of ELFT to examine the performance increases provided by selected new features.

14.2 Supplemental Notes to Section 1.0 -- Concepts of Operation for “Improved Criminal Latent Searches”

A second major goal of ELFT is to provide “an automated latent search capability” to latent examiners. By this we mean that latent examiners should have the capability of screening their latent images with a minimum of effort. We use the term screening to emphasize that such searches are not fully equivalent to traditional searches.

14.3 Features for Use in Matching

Generally speaking, the selection of the features for use in the matching process is left to the participant. Matchers need not primarily be a “minutiae matcher.”

Candidate List architectures in which “advanced matchers” are selectively invoked depending upon initial results are allowed. For example the matcher might initially use certain core features in comparing the search (probe) with a background (gallery) subject. The result of this comparison might produce one of three possible outcomes: 1) the two fingerprints are too different, and no further effort should be expended on this candidate; 2) the two are so similar that this is definitely a mate; or 3) the two have points of similarity, but the match is not conclusive. The third case might then trigger a call to an “advanced matcher” for further resolution.

If using “advanced matchers”, it is up to the participant to decide if the additional features (if any) required are to be extracted and stored on disk memory during the enrollment phase. Since it may not be possible to keep all gallery images in memory, it might be necessary for the software to retrieve the data from disk during searches. This extra fetch time will be included in execution time measurements.

Approximately a year-and-a-half downstream NIST intends to test the effect of using augmented feature sets. These will be largely based upon the CDEFFS feature sets, but are not necessarily limited to these. For a description of the proposed CDEFFS features please go to the following website: http://fingerprint.nist.gov/standard/cdeffs/index.html (http://fingerprint.nist.gov/standard/cdeffs/index.html)

These augmented feature tests test will be run in a dual mode: a) first without employing any new feature, then b) employing designated new features. To allow this mode of operation a somewhat different format might be required for the SDK.

14.4 Supplemental Notes to Sections 7 and 13 -- Candidate List Reduction

NIST envisages that in the near future automated search capabilities will assist latent experts by reducing the size of candidate lists they need to examine through elimination of the more obvious “nuisance” non-matches (impostors). For example, assuming that one hundred latents are submitted for searches, and that each search produces a candidate list of twenty candidates, an examiner needs to look at 2000 candidates. Since a typical identification rate for latent searches might be around 4%, this means that 2000 candidates need to be examined to find the four true identifications. While it is true that skilled examiners can quickly dismiss “nuisance candidates,” nevertheless it does take up valuable examiner time. An even larger concern is that too many nuisance candidates might result in the true mates being overlooked due to operator fatigue. It is therefore desirable to minimize these nuisance candidates. We refer to this part of the automated matching process as candidate list reduction. To achieve effective candidate list reduction may require additional computer processing, including the development of new algorithms.

Since candidate list reduction poses many challenges, we plan to implement it in stages. The initial stage is to introduce a new parameter called Probability of True Match. This is intended to give a numerical estimate that the candidate is a true mate of the latent. This parameter should be supplied as a number between zero and 100. The number 100 will be interpreted as an extremely high confidence “hit.” The intent is to use this parameter as a key to candidate list reduction. Certainly the raw matcher score by itself provides a strong clue regarding the merit of a given candidate. However, by itself it is insufficient. For one thing, there is no agreed upon standard for the range of matcher scores: Does a value of 5000 indicate a high score? A very high score? Secondly, whether a given score belongs to a true mate depends upon the size of the background. The larger the background the more likely it is that large impostor scores will be created. The Probability of True Match therefore needs to take matcher score and background size into account. Additional information might also be factored in, such as: a) the score gap to the next candidate; b) the quality of the latent; and c) the quality of the mate.

There does not appear to be any simple way of computing Probability of True Match, and participants are encouraged to develop their own procedures. Purely as an illustrative example, we offer the following procedure.

Assume a background size of N. Assume additionally that some candidate has achieved a score of S. To fix our ideas assume S = 5000. Assume further that data such as shown in Figure 6 is available. Then from this figure we obtain TAR = 0.3 and FAR = .00001. This may be interpreted as: the a priori probability of obtaining a score exceeding 5000 is 0.3 when matching against the true mate. Conversely, the a priori probability of exceeding 5000 when matching against an imposter is .00001. Note that this is for a single imposter. The probability that one or more imposters exceed 5000 in a background of N, assuming independence in match scores, is 1 – (1 – FAR)N . If this is taken as the a priori probability of an imposter exceeding 5000, then we can renormalize in the Bayesian sense to obtain the probability that the candidate is a true mate.

http://fingerprint.nist.gov/latent/elft07/elft_p2_concept.pdf 


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KEPT - Keeping Examiners Prepared for Testimony
#1 - Comparison Phase - Documentation
by Michele Triplett, King County Sheriff's Office

Disclaimer:  The intent of this is to provide thought provoking discussion.  No claims of accuracy exist. 

 

Question – Comparison Phase - Documentation:

Did you document what your conclusion was based on (did you chart it, draw it, or include a written explanation)?

 

Possible Answers:

a)      No.

b)      Yes, our office does this for every individualization.

c)      No, I have never done this.

d)     No, it’s not in our operating policies to do it.

e)      No, we don’t have the manpower to do this.

f)       No, it wasn’t needed in this case.

g)      No, we don’t document what we see because we don’t want to influence others.

h)      Under ASCLD, the latent lift is considered sufficient documentation.

 

Discussion:

Answer a:  Previously, it was recommend to answer with a simple ‘yes’ or ‘no’ but that isn’t as acceptable as it once was.  If you just answer the above question with ‘No’ then it appears you did a less than acceptable job….that your work was lacking. 

Answer b:  Perhaps some people make a chart for every individualization.  This may look like a good practice but it’s not needed.  It may look like you’re being thorough when you’re really just wasting time by doing unnecessary steps. 

Answers c, d, and e:  These are excuses and it leaves people with the impression that you should have done some form of documentation.

Answer f:  I feel the best answer is f; documentation wasn’t done because it wasn’t needed.  We all have our own standards, agency standards, and industry standards but the most accepted standard is the scientific standard.  Science doesn’t require documentation for simple conclusions (stating that 8x8 is 64 is good enough; I don’t have to document each tick mark.  But if I’m ever asked to produce documentation, I should be able to).  It’s also important to note that science doesn’t require contemporaneous documentation on simple conclusions like this.  When conclusions are more complex, then documentation should go along with the conclusion so others know the conclusion had good justification behind it.

Answer g:  This may sound like a good answer but it’s really just an excuse for not understanding or following scientific protocols.  Science requires that conclusions be documentable in case anyone should ever ask for them.

Answer h:  The first thing to notice about this answer is that they use the term ASCLD instead of ASCLD/LAB.  It’s always more professional to use the correct name of an organization (preferably not the acronym).  This answer also seems to misunderstand the question.  Documentation of the physical evidence found is very different from documentation of the justification behind an analytical conclusion.  The latent print itself cannot be sufficient justification for a conclusion.

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Until next Monday morning, don't work too hard or too little.

Have a GREAT week!