Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

Date of Last Review N/A Date of Next Review 03-2017 Appendix present/Letter No

1 of 16

Document #15

Standard for the Definition and Measurement of Rates of Errors and NonConsensus Decisions in Friction Ridge Examination

(Latent/Tenprint)

1. Preamble

1.1. This document defines the concepts of error rates and non-consensus decisions as they pertain to 

friction ridge examination, presents strategies for measuring them, and discusses how they should be 

communicated. 

1.2. This document defines error to mean an examiner’s decision that is demonstrably wrong. The term 

error as it is used here is limited to individualization or exclusion conclusions that contradict ground 

truth or have been respectively declared exclusion or individualization by a consensus. A nonconsensus decision is a determination or conclusion that cannot be assessed as an error, but conflicts 

with the consensus for that decision. For example, an inconclusive decision cannot be proved correct 

or incorrect, but can be considered non-consensus.

1.3. This standard does not mandate the measurement of rates of errors and non-consensus decisions, but 

prescribes terminology and approaches to be used when such rates are discussed and measured. This 

standard does not address the implications of errors or non-consensus decisions. If rates of errors or 

non-consensus decisions pertaining to friction ridge examination are presented, they must be defined 

in compliance with the content of this document. 

2. Scope

This standard applies to the measurement of the rates of error and non-consensus decisions in all friction 

ridge examinations. It addresses false positives, false negatives, and non-consensus decisions resulting from 

the application of the ACE or ACE-V process.

3. Terminology

3.1. Definition of source attribution terms

3.1.1. Source

An area of friction ridge skin from an individual from which an impression originated.
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

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3.1.2. Ground truth

Definitive knowledge of the actual source of an impression1. Ground truth cannot be 

determined by consensus based on friction ridge examination alone, but requires controlled 

collection of impressions from known individuals. This definition of ground truth cannot be 

satisfied with prints from operational casework. A properly administered proficiency test uses 

ground truth as its basis.

3.1.3. Consensus determination or conclusion

3.1.3.1. Agreement reflecting the collective judgment of a group of examiners trained to 

competency when making determinations or conclusions with respect to one or 

more impressions. 

3.1.3.2. Consensus can be obtained by means determined by the testing body, such as 

simple majority rule of the consensus panel, qualified majority, or unanimity. The 

size of the consensus panel and the method used to reach consensus needs to be 

documented. The procedures on which consensus is based must be defined prior to 

the evaluation.

3.1.3.3. An audit of casework may use consensus to determine appropriate decisions or 

conclusions. 

3.1.4. Mated

Mated impressions describe impressions intentionally collected to originate from the same 

source, and used for the purpose of measuring error rates. Whenever the ground truth source 

is not known, consensus may be used as a surrogate for the determination of the mating status 

of the impressions2. 

3.1.5. Non-mated

Non-mated impressions describe impressions intentionally collected to originate from different 

sources, and used for the purpose of measuring error rates. Whenever the ground truth source 

is not known, consensus may be used as a surrogate for the determination of the mating status 

of the impressions3.

3.2. Definition of the types of error and error rates

An error is a conclusion reached by an examiner that contradicts the mating status of two impressions, 

and therefore is provably wrong (compare with non-consensus decision – see 3.3).

3.2.1. Erroneous individualization (also known as false positive or type I error)

The incorrect determination that two areas of friction ridge impressions are mated. Rates that 

can be computed from erroneous individualizations are:

3.2.1.1. False positive rate (FPR)

The proportion of the comparisons between non-mated prints that result in an 

erroneous individualization conclusion.

 1 The term “ground truth” can also be used at the feature level. In that case, it means the definitive knowledge that 

all marked features correspond between two impressions.

2

 An individualization decision is in essence the determination by an examiner that two impressions are mated.

3

 An exclusion decision is in essence the determination by an examiner that two impressions are not mated.
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

Date of Last Review N/A Date of Next Review 03-2017 Appendix present/Letter No

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3.2.1.2. Positive predictive value (PPV)

The proportion of individualization decisions that are correct4.

3.2.2. Erroneous exclusion (also known as false negative, or type II error)

The incorrect determination that two areas of friction ridge impressions are non-mated. Rates 

that can be computed from erroneous exclusions are:

3.2.2.1. False negative rate (FNR)

The proportion of the comparisons between mated prints that result in an erroneous 

exclusion conclusion.

3.2.2.2. Negative predictive value (NPV)

The proportion of exclusion determinations that are correct.

3.3. Definition of additional non-consensus decisions

3.3.1. A non-consensus decision is a determination or conclusion reached by an examiner at any 

step of the examination process that cannot be assessed as an error, but conflicts with the 

consensus for that decision. For example, whether or not the examination of a pair of mated 

impressions should result in an individualization or inconclusive decision cannot be defined in 

terms of error but only with respect to a consensus. 

3.3.2. Measuring non-consensus decisions provides a means to quantify whether examiners are 

being overly cautious or aggressive in their decisions. 

3.3.3. Note that non-consensus decisions indicate conflict between an examiner and a consensus, 

rather than a simple conflict between two individuals. 

3.3.4. Non-consensus determination of suitability

3.3.4.1. When an examiner’s determination of suitability does not concur with consensus. 

Suitability determinations include non-consensus no value, and non-consensus 

value decisions. 

3.3.4.2. Non-consensus determinations of no value

Decisions of no value that conflict with the consensus.

3.3.4.3. Non-consensus determinations of value 

Decisions of value that conflict with the consensus.

3.3.5. Non-consensus inconclusive

When an examiner reaches a decision of inconclusive that conflicts with the consensus, 

exclusive of false positive and negative errors.

3.3.6. Non-consensus individualization conclusion

3.3.6.1. When an examiner reaches a decision of individualization that conflicts with the 

consensus, exclusive of false positive errors.

 

4

 Most statistical definitions of positive predictive value do not have to account for inconclusive or no value 

decisions. The positive predictive value is sometimes presented in terms of the false discovery rate, which is 

defined as the percentage of individualization decisions that are incorrect. In order to account for inconclusive and 

no value decisions, in our case the false discovery rate is mathematically equal to 1 minus the positive predictive 

value minus the non-consensus individualization rate. 
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

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3.3.6.2. This type of non-consensus conclusion is different from the errors defined in 3.2, 

since the examiner is not necessarily wrong in absolute terms.

3.4. Other measures of errors and non-consensus decisions

3.4.1. Missed individualization

The failure to make an individualization when in fact both friction ridge impressions are mated 

(includes false negative, non-consensus inconclusive, and non-consensus no value).

3.4.2. Missed exclusion

The failure to make an exclusion when in fact the friction ridge impressions are non-mated 

(includes false positive, non-consensus inconclusive, and non-consensus no value). 

4. Measurement of the rates of errors and non-consensus decisions 

4.1. Rates of errors and non-consensus decisions can be measured at various resolutions based on how 

examiners are grouped:

4.1.1. Individual rates of errors and non-consensus decisions

The rates of errors and non-consensus decisions measured for an individual examiner pertain 

directly to the question of the accuracy of a specific friction ridge examination and may be the 

most relevant to present in court. 

4.1.2. Organizational rates of errors and non-consensus decisions

The rates of errors and non-consensus decisions measured for a defined group of examiners, 

such as a unit, an agency, or a corporation. These rates are relevant to provide estimates of 

performance across an organization. They reflect the effectiveness of and adherence to 

standard operating procedures, proficiency testing policy, and continuing education and 

training programs at the organizational level.

4.1.3. Categorical rates of errors and non-consensus decisions

4.1.3.1. The rates of errors and non-consensus decisions measured for a representative 

group of examiners sharing one or more characteristics, such as training, 

experience, certification, type of cases, or caseload. These rates are relevant to 

provide estimates of performance across homogenous groups of examiners. 

Community rates of errors and non-consensus decisions

4.1.3.2. The average rates of errors and non-consensus decisions measured for the 

fingerprint examiner community. These rates are relevant to provide a general 

estimate of the accuracy of friction ridge examination, but are less pertinent when 

addressing the accuracy of specific examinations. 

4.2. Practical considerations

4.2.1. Rates of errors and non-consensus decisions can be measured by testing the proficiency of 

any of the groups of fingerprint examiners described in 4.1 when examining unknown and 

known impressions through the ACE process. It is also possible to assess these rates after 

ACE-V, except when measuring rates for individual examiners. 

4.2.2. The level of resources necessary to measure rates of errors and non-consensus decisions at 

the various resolutions defined in section 4.1 will depend on the accuracy and robustness 

desired for each resolution.

4.2.2.1. Because individual error rates are specific to an examiner, the number of 

observations required to accurately determine these rates for that examiner may be 

impractically large. In addition, measuring individual rates and accounting for 
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

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changes in expertise over time requires extensive and repeated testing of that 

individual. 

4.2.2.2. Organizational, categorical and community rates of errors and non-consensus 

decisions are averaged across multiple examiners and may be more robust to 

changes in personnel, and therefore require fewer observations per individual than 

when measuring individual rates. 

4.2.2.3. Accurate measurement of community rates will require adequate representation of 

all categories and organizations involved in the examination of friction ridges, as well 

as the development of standardized tests and examination procedures, the 

leadership of professional organizations to administer them and encouragement to 

participate from the organizations described in 4.1.2.

4.2.3. Two types of tests can be designed, with respect to how the origin of the impressions is 

determined: ground truth, or consensus. All rates of errors and of non-consensus decisions can 

be measured by both tests. This is not meant to suggest that all of these need be measured, or 

that there is any particular deficiency if estimates of these are unknown. It is expected that in 

any particular study or context, only some of the rates defined in section 3 will be relevant.

4.2.4. Measuring rates when the ground truth is known

4.2.4.1. This test measures rates using a dataset of pairs of impressions of known origin 

collected under controlled conditions, so that the measurement of error rates relies 

on the ground truth, while the measurement of the rates of non-consensus decisions 

relies on the consensus estimation of the truth.

4.2.4.1.1. The measurement of the error rates will be affected by the proportions 

of pairs of mated and non-mated impressions. These relative 

proportions depend on the type of casework or operating procedures 

simulated. For example, AFIS casework has a very small proportion of 

pairs of mated impressions when considering all possible candidates. 

4.2.4.1.2. The measurement of the error rates will be affected based on whether 

or not the test allows examiners to decide on the suitability of the 

impressions, or that comparisons are inconclusive. 

4.2.4.1.3. The difficulty of the test will depend on the selection of the impressions. 

The number of non-consensus decisions of suitability, non-consensus 

inconclusive decisions, non-consensus individualization conclusions and 

erroneous exclusion conclusions will depend on the quantity and quality 

of information present in the mated impressions. 

4.2.4.1.3.1. The number of non-consensus decisions of suitability, 

non-consensus inconclusive decisions, non-consensus 

exclusion conclusions and erroneous individualization 

conclusions will depend on the quantity and quality of 

information present in the non-mated impressions, and 

on how the non-mated impressions were selected (e.g., 

selected randomly, through AFIS, or filtered by fingerprint 

characteristics, such as pattern classification).

4.2.4.1.4. The rates may be measured by presenting the examiner with a series of 

comparisons, each including one unknown print and one of the 

following: (1) one control impression; (2) the set of control impressions 

from one individual; (3) sets of control impressions from multiple 

individuals. 
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

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4.2.4.1.5. Benefits of using ground truth data when measuring rates of errors and 

non-consensus decisions:

4.2.4.1.5.1. The expected conclusion for each comparison is known.

4.2.4.1.5.2. This type of test permits the use of a greater variety of 

substrates, backgrounds, and development techniques 

than commonly observed in casework.

4.2.4.1.6. Challenges of measuring the rates when the ground truth is known:

4.2.4.1.6.1. While the theoretical conclusion is known, the 

appropriate decisions of suitability and inconclusive must 

be estimated by consensus.

4.2.4.1.6.2. Examiners may be aware that they are being tested.

4.2.4.1.6.3. Careful collection and control of the data is required to 

ensure absolute confidence in the origin of every 

impression in the sample.

4.2.4.1.6.4. It may be difficult to accurately replicate the relative 

proportions of the various combinations of substrates, 

backgrounds, and development techniques observed in 

casework. This is especially true when measuring the 

rates for the community as a whole.

4.2.5. Measuring rates when the ground truth is not known

4.2.5.1. This test measures rates using information obtained by auditing operational 

casework in which an estimate of ground truth is approximated by consensus.

4.2.5.1.1. Care should be taken that the selection of data is either random or sequential for the 

results to be considered truly representative of casework. 

4.2.5.1.2. The measurement of the error rates will be affected based on whether or not the 

number of non-consensus decisions is recorded during the audit.

4.2.5.1.3. The data obtained during the audit may subsequently be used as test data to 

measure rates of errors and non-consensus decisions on other groups of examiners 

than the one audited, as described in 4.2.3.

4.2.5.1.4. Benefits of measuring rates when the ground truth is not known

4.2.5.1.4.1. A greater amount of data may be available.

4.2.5.1.4.2. Can accurately represent casework conditions.

4.2.5.1.4.3. Examiners will not know that they are tested at the time when they 

perform the original examination.

4.2.5.1.5. Challenges of measuring rates when the ground truth is not known

4.2.5.1.5.1. The ground truth can only be estimated through the use of review 

panels or case auditors.

4.2.5.1.5.2. The accuracy of the measured rates will be limited by the fact that the 

rates are based on consensus estimates of ground truth, and therefore it 

is possible that the consensus panel will reach the same non-consensus 

decision or erroneous conclusion as the original examiner.

4.2.5.1.5.3. The calculation of the rates may be complex if the number of known 

impressions varies substantially among the audited cases. 
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

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5. Calculating Rates of Errors and Non-consensus Decisions

5.1. General formulas

5.1.1. All observations taken into account for the numerators and denominators of the following 

formulas only relate to observations collected during the duration of the study. 

5.1.2. False positive rate

The false positive rate is the ratio of the number of erroneous individualization conclusions 

(numerator) and the total number of comparisons made involving non-mated pairs 

(denominator). 

5.1.3. False negative rate

The false negative rate is the ratio of the number of erroneous exclusion conclusions 

(numerator) and the total number of comparisons made involving mated pairs (denominator). 

5.1.4. Positive predictive value

The positive predictive value is the ratio of the number of correct individualization conclusions 

(numerator) and the total number of individualization conclusions (denominator). 

5.1.5. Negative predictive value

The negative predictive value is the ratio of the number of correct exclusion conclusions 

(numerator) and the total number of exclusion conclusions (denominator).

5.1.6. Rates of non-consensus determination of suitability 

5.1.6.1. Rates of non-consensus determination of value

The rate of non-consensus determination of value is the ratio of the number of 

decisions of value that conflict with the consensus (numerator) and the total number 

of impressions determined to be of value (denominator). 

5.1.6.2. Rates of non-consensus determination of no value

The rate of non-consensus determination of no value is the ratio of the number of 

decisions of no value that conflict with the consensus (numerator) and the total 

number of impressions determined to be of no value (denominator). 

5.1.7. Rates of non-consensus inconclusive

The rate of non-consensus inconclusive is the ratio of the number of inconclusive comparisons 

that conflict with the consensus, excluding the false positive and negative errors (numerator) 

and the total number of inconclusive conclusions reached (denominator). 

5.1.8. Rates of non-consensus individualization conclusions

The rate of non-consensus individualization is the ratio of the number of individualization 

conclusions that conflict with the consensus, excluding false positive errors (numerator) and 

the total number of individualization conclusions reached (denominator). 

5.1.9. Rates of non-consensus exclusion conclusions

The rate of non-consensus exclusion is the ratio of the number of exclusion conclusions that 

conflict with the consensus, excluding false negative errors (numerator) and the total number of 

exclusion conclusions reached (denominator). 

5.2. Considerations on the calculation of rates of errors and non-consensus decisions

5.2.1. The value of the various rates will be affected by whether or not the denominator of the 

formulas in section 4.1 include all comparisons made for any given unknown impression (e.g.,

the comparison of the unknown with (1) all impressions returned in an AFIS candidate list; (2) 
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

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Web Posting Date 11/24/12

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all 10 impressions from an known individual; (3) with a single impression). All these 

denominators are appropriate, but the choice of the testing body needs to be clearly noted 

whenever an error rate is cited (see examples 2 and 3, in sections 5.3.3 and 5.3.4).

5.2.2. All errors and non-consensus decisions have to be considered for the calculation of the rates, 

regardless of whether they are the result of errors in judgment, clerical, or administrative errors.

5.2.3. The formulas presented in section 4.1 do not allow for combining the numbers of errors and 

non-consensus decisions because there is no simple formula that combines them.

5.2.4. Examiners who make excessive numbers of non-consensus decisions of unsuitability or 

inconclusive are likely to reduce their error rates, but will increase the numbers of missed 

individualizations and exclusions.

5.3. Examples for the calculation of rates of error and non-consensus decisions

5.3.1. The following simplified and hypothetical examples are designed to help understand the 

calculation of the rates of error and non-consensus decisions.

5.3.2. Hypothetical Example 1

A study is performed to measure the error rates and predictive values of a group of nine 

examiners. Each examiner in the group is presented with a series of 11 exercises. Each 

exercise includes one unknown and one known impression. Six of the exercises have mated 

pairs of impressions (ground truth is known to the researcher, but not to the examiner); the 

remaining five pairs do not have mated impressions (ground truth is known).

Each examiner is asked to examine the impressions using ACE and form a conclusion of either 

individualization or exclusion. Examiners are not allowed to decide on the suitability of the 

impressions, and cannot report inconclusive decisions.

The following data resulted from this hypothetical study (Table 1).

Ground truth - Expected answer

Mated Non-mated

Unknown # 1 2 3 4 5 6 7 8 9 10 11

1

2 X

3 X X

4

5

6 X

7

8 X

Answer from 

Examiner #

9 X

Table 1: Results of the test: A green color indicates a correct answer; a red color indicates a wrong answer.
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

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Web Posting Date 11/24/12

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Based on these data, the following hypothetical error rates and predictive values can be 

calculated (Tables 2 through 5). 

Ground truth 

Expected answers

Mated Non-mated

Total 

conclusions 

reached

Predictive 

values

Individualization 6 0 6

Positive

Conclusions 6/6=1

reached by 

Examiner 1

Exclusion 0 5 5 Negative

5/5=1

Total ground truth conclusions 6 5

Error rates False negative 

0/6=0

False positive

0/5=0

Table 2: Error rates and predictive values for examiner 1.

Table 3: Error rates and predictive values for examiner 2.

Ground truth 

Expected answers

Mated Non-mated

Total 

conclusions 

reached

Predictive 

values

Individualization 5 0 5

Positive

Conclusions 5/5=1

reached by 

Examiner 2

Exclusion 1 5 6 Negative

5/6=0.83

Total ground truth conclusions 6 5

Error rates False negative

1/6=0.16

False positive

0/5=0
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

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Ground truth 

Expected answers

Mated Non-mated

Total 

conclusions 

reached

Predictive 

values

Individualization 5 1 6

Positive

Conclusions 5/6=0.83

reached by 

Examiner 3

Exclusion 1 4 5 Negative

4/5=0.8

Total ground truth conclusions 6 5

Error rates False negative

1/6=0.16

False positive

1/5=0.2

Table 4: Error rates and predictive values for examiner 3.

Table 5: Error rates and predictive values for the group.

Ground truth 

Expected answers

Mated Non-mated

Total 

conclusions 

reached

Predictive 

values

Individualization 50 2 52

Positive

Conclusions 50/52=0.96

reached by the 

group

Exclusion 4 43 47 Negative

43/47=0.91

Total ground truth conclusions 54 45

Error rates False negative 

4/54=0.074

False positive

2/45=0.044
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

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5.3.3. Hypothetical Example 2

An audit of operational casework was performed in laboratory Oz to determine the organization 

rates of errors and non-consensus decisions. 

A panel of 4 examiners trained to competency performed the audit. In this example, consensus 

was defined as at least 3 out of the 4 examiners performing the audit concurring on a decision. 

The auditors reviewed casework and made decisions using laboratory Oz’s standard operating 

procedures, which were based on SWGFAST documents. 

The audit was considering the operation of laboratory Oz over the past 10 years, during which 

time 10,000 latent impressions were examined. For the audit, the auditors selected 1,000 latent 

impressions. The selection of these impressions could have occurred in various ways: random 

selection over the entire pool, or stratified selection based on various categories, such as 

selecting pre-defined numbers of individualization/exclusion decisions, or equal numbers per 

examiner. For the purpose of this example, it is assumed that the samples were selected 

randomly. 

In this example, comparison decisions (individualization, inconclusive, exclusion) are 

calculated by subject, not calculated separately per finger.5

The original decisions made by laboratory Oz on the selected 1,000 latent impressions were as 

follows:

Original decision Number

No value 80

Of value but no suitable 

candidate

220

Exclusion 160

Inconclusive 240

Individualization 300

After auditing the impressions, the audit panel found the following results. Errors are 

highlighted in yellow; non-consensus decisions are highlighted in blue.

 5

 Note that calculations by subject do not specifically address errors in which an individualization is made to the 

correct subject but the wrong area of friction ridge skin.
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

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Original decision Audit

No value Of value but 

no candidate

Exclusion Inconclusive Individualization

No value 80 69 1 1 8 1

Of value but no 

candidate6

220 2 218 0 0 0

Exclusion 160 4 0 140 10 6

Inconclusive 240 23 0 7 200 10

Individualization 300 14 0 1 15 270

1000 112 219 149 233 287

 6

 “Of value but no candidate” means no subjects were compared, or all AFIS candidate images that were 

compared were excluded.

Description Numerator Denominator Rates

False positive 1 149 0.007

Positive predictive value 270 300 0.900

False negative 6 287 0.021

Negative predictive 

value

140 160 0.875

Non-consensus no 

value

11

(1+1+8+1)

80 0.138

Non-consensus value 43

(2+4+23+14)

920

(220+160+240+300)

0.047

Non-consensus 

inconclusive

40

(23+7+10)

240 0.167

Non-consensus 

exclusion

14

(10+4)

160 0.088

Non-consensus 

individualization

29

(15+14)

300 0.097

Missed individualization 17

(1+6+10)

287 0.059

Missed exclusion 9

(1+7+1)

149 0.060
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

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5.3.4. Hypothetical Example 3

An audit of operational casework was performed in laboratory Narnia to determine the 

organization rates of errors and non-consensus decisions. All of Narnia’s casework is 

performed on AFIS. This example shows the effect of calculations on a per-image basis.

As in example 2, a panel of 4 examiners trained to competency performed the audit. In this 

example, consensus was defined as at least 3 out of the 4 examiners performing the audit 

concurring on a decision. The auditors reviewed casework and made decisions using 

laboratory Narnia’s standard operating procedures, which were based on SWGFAST 

documents. 

The audit was considering the operation of laboratory Narnia over the past year, during which 

time 1,000 latent impressions were searched. The original candidate list for each search was 

retained and used in the audit. For the audit, all 1,000 latent impressions were reviewed. 

In this example, comparison/evaluation decisions (individualization, inconclusive, exclusion) 

are calculated by separately for each image compared.

The original decisions made by laboratory Narnia on the selected 1,000 latent impressions 

were as follows. In every case where the latent was of value, 20 AFIS candidate images were 

compared, so each latent that was of value and searched resulted in 20 comparison/evaluation 

decisions.

Original decision Number

No value for AFIS search 150

Exclusion 16,000

Inconclusive 750

Individualization 250

After auditing the impressions, the audit panel found the following results. Errors are 

highlighted in yellow; non-consensus decisions are highlighted in blue.
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

Date of Last Review N/A Date of Next Review 03-2017 Appendix present/Letter No

14 of 16

Original decision Audit consensus

Latent is no 

value

Latent is of 

value (not 

searched)

Exclusion Inconclusive Individualizati

on

No value for AFIS 

search

150 140 10 n/a n/a n/a

Exclusion 16,000 n/a n/a 15,844 150 6*

Inconclusive 750 n/a n/a 16 730 4

Individualization 250 n/a n/a 2* 3 245

15,862 883 255

*One latent was erroneously individualized to an image in its candidate list when the correct subject was in the 

candidate list; therefore that latent resulted in one false positive and one false negative.

Description Numerator Denominator Rates

False positive 1 149 0.007

Positive predictive value 270 300 0.900

False negative 6 287 0.021

Negative predictive 

value

140 160 0.875

Non-consensus no 

value

11

(1+1+8+1)

80 0.138

Non-consensus value 43

(2+4+23+14)

920

(220+160+240+300)

0.047

Non-consensus 

inconclusive

40

(23+7+10)

240 0.167

Non-consensus 

exclusion

14

(10+4)

160 0.088

Non-consensus 

individualization

29

(15+14)

300 0.097

Missed individualization 17

(1+6+10)

287 0.059

Missed exclusion 9

(1+7+1)

149 0.060
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

Date of Last Review N/A Date of Next Review 03-2017 Appendix present/Letter No

15 of 16

6. Interpretation and Communication of the Rates of Errors and Non-Consensus Decisions 

6.1. The hypothetical error rates and predictive values reported in table 5 must be interpreted and 

communicated as follows. Rates of non-consensus decisions can be interpreted and communicated in 

a similar fashion. 

6.1.1. False positive rate

Table 5 indicates a false positive rate of 0.044. This indicates that examiners in the tested 

group erroneously identified the source of an impression on average in 4.4% of the 

examinations that they performed under the test conditions.

6.1.2. False negative rate

Table 5 indicates a false negative rate of 0.074. This indicates that examiners in the tested 

group erroneously excluded the source of an impression on average in 7.4% of the 

examinations that they performed under the test conditions.

6.1.3. Positive predictive value

Table 5 indicates a positive predictive value of 0.96. This indicates that examiners in the tested 

group and under the test conditions, who report an individualization conclusion, were correct, 

on average, 96% of the time. 

6.1.4. Negative predictive value

Table 5 indicates a negative predictive value of 0.91. This indicates that examiners in the 

tested group and under the test conditions, who report an exclusion conclusion, were correct, 

on average, 91% of the time.

6.2. Understanding rates of errors and non-consensus decisions

6.2.1. Error rates cannot be communicated using a single number. Each of the values presented in 

section 2 has different implications and interpretations. Rates of errors and non-consensus 

decisions should be reported together in order to properly interpret the effectiveness and 

accuracy of examiners. 

6.2.1.1. For example, an examiner who has a very low rate of false exclusions, but a high 

rate of non-consensus no value decisions can be considered as excessively 

cautious; and therefore, less effective than another examiner with the same rate of 

false exclusions but a lower rate of non-consensus no value decisions. 

6.2.2. The selection of which of the rates defined in this document will be measured and reported will 

depend on the information solicited. 

6.2.3. The communication of these rates needs to be supported by the disclosure of the experimental 

conditions in which they have been measured, such as the group of examiners to which they 

apply (see section 4.1), the type of casework considered (see section 4.2), whether they are 

based on ground truth or consensus (see sections 4.2), and if consensus was used, how 

consensus was established (see section 3.1.3).

6.2.4. If it is determined that some errors or non-consensus decisions were administrative or clerical 

in nature, this should be noted and disclosed to better explain the corresponding rates.

6.2.5. These rates can be meaningfully compared between different individuals, groups of examiners 

or organizations, only when they were measured under similar conditions. 
Document #15 Standard for the Definition and Measurement 

of Rates of Errors and Non-Consensus Decisions in Friction 

Ridge Examination (Latent/Tenprint, Ver. 2.0

Date of First Issue 03/05/12 Current Issue Date 11/15/12

Web Posting Date 11/24/12

Date of Last Review N/A Date of Next Review 03-2017 Appendix present/Letter No

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7. Revision Table

Version Effective Start Effective End Posted Archived Change

1.2 03/05/12 11/15/12 06/23/12 11/15/12 Original issue

2.0 11/15/12 N/A 11/24/12 N/A No change to content

Reformatted (start of new 

version number)