ISO/IEC 19795-1:2006
(Main)Information technology — Biometric performance testing and reporting — Part 1: Principles and framework
Information technology — Biometric performance testing and reporting — Part 1: Principles and framework
ISO/IEC 19795-1:2006 is concerned with the evaluation of biometric systems in terms of error rates and throughput rates. Metrics for the various error rates in biometric enrolment, verification and identification are unambiguously specified. Recommendations and requirements are given for the conduct of performance evaluations through the steps of planning the evaluation; collection of enrolment, verification or identification transaction data; analysis of error rates; and the reporting and presentation of results. The principles presented are generic to the range of biometric modalities, applications, and test purposes, and to both offline and online testing methodologies. These principles help avoid bias due to inappropriate data collection or analytic procedures; give better estimates of field performance for the expended effort; and clarify the limits of applicability of the test results.
Technologies de l'information — Essais et rapports de performance biométriques — Partie 1: Principes et canevas
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INTERNATIONAL ISO/IEC
STANDARD 19795-1
First edition
2006-04-01
Information technology — Biometric
performance testing and reporting —
Part 1:
Principles and framework
Technologies de l'information — Essais et rapports de performance
biométriques —
Partie 1: Principes et canevas
Reference number
©
ISO/IEC 2006
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ii © ISO/IEC 2006 – All rights reserved
Contents Page
Foreword. v
Introduction . v
1 Scope. 1
2 Conformance. 1
3 Normative references. 1
4 Terms and definitions. 2
4.1 Biometric data . 2
4.2 User interaction with a biometric system. 3
4.3 Personnel involved in the evaluation . 3
4.4 Types of evaluation . 4
4.5 Biometric applications . 5
4.6 Performance measures. 5
4.7 Data presentation curves. 7
4.8 Statistical terms. 7
5 General biometric system. 8
5.1 Conceptual diagram of general biometric system . 8
5.2 Conceptual components of a general biometric system. 8
5.3 Functions of general biometric system. 10
5.4 Enrolment, verification & identification transactions . 12
5.5 Performance measure. 12
6 Planning the evaluation. 14
6.1 General. 14
6.2 Use of other parts of ISO/IEC 19795 . 14
6.3 Determine information about the system. 14
6.4 Controlling factors that influence performance .15
6.5 Test subject selection . 16
6.6 Test size. 17
6.7 Multiple tests. 18
7 Data collection. 19
7.1 Avoidance of data collection errors. 19
7.2 Data and details collected. 20
7.3 Enrolments. 20
7.4 Genuine transactions . 21
7.5 Identification transactions of users enrolled in the system. 22
7.6 Impostor transactions . 23
7.7 Identification transactions of users not enrolled in the system . 25
8 Analyses. 26
8.1 General. 26
8.2 Fundamental performance metrics. 26
8.3 Verification system performance metrics . 28
8.4 (Open-set) Identification system performance metrics . 30
8.5 Closed-set identification . 31
8.6 Detection error trade-off / Receiver operating characteristic curves. 31
8.7 Uncertainty of estimates . 32
9 Record keeping. 32
10 Reporting performance results . 33
10.1 Fundamental metrics. 33
© ISO/IEC 2006 – All rights reserved iii
10.2 Verification system metrics . 33
10.3 Identification system metrics. 33
10.4 Closed-set identification system metrics. 34
10.5 Reporting test details . 34
10.6 Graphical presentation of results. 35
Annex A (informative) Differences between evaluation types . 38
Annex B (informative) Test size and uncertainty . 39
B.1 Confidence intervals and test size assuming independent identically distributed
comparisons. 39
B.1.1 Rule of 3 . 39
B.1.2 Rule of 30 . 39
B.1.3 Number of comparisons to support a claimed error rate . 39
B.2 Variance of performance measures as a function of test size . 40
B.3 Estimates for variance of performance measures. 41
B.3.1 General. 41
B.3.2 Variance of observed false non-match rate . 41
B.3.3 Variance of observed false match rate . 43
B.4 Estimating confidence intervals. 44
B.4.1 General. 44
B.4.2 Bootstrap estimates of the variance and confidence intervals. 44
B.4.3 Subset sampling . 45
Annex C (informative) Factors influencing performance . 46
C.1 General. 46
C.2 List of factors. 46
C.2.1 Population demographics . 46
C.2.2 Application. 47
C.2.3 User physiology. 47
C.2.4 User behaviour. 48
C.2.5 User appearance. 48
C.2.6 Environmental influences. 49
C.2.7 Sensor and hardware. 49
C.2.8 User interface. 50
C.3 Examples for reporting. 50
C.3.1 Finger position . 50
C.3.2 Illumination . 50
C.3.3 Glasses. 50
C.3.4 Dirt on platen . 51
C.3.5 Weather. 51
Annex D (informative) Pre-selection. 52
D.1 Pre-selection algorithm performance . 52
Annex E (informative) Identification performance as a function of database size . 53
Annex F (informative) Algorithms for generating ROC, DET and CMC curves. 54
F.1 Algorithm for ROC and DET. 54
F.2 Algorithm for generating CMC. 54
Bibliography . 55
iv © ISO/IEC 2006 – All rights reserved
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are members of
ISO or IEC participate in the development of International Standards through technical committees
established by the respective organization to deal with particular fields of technical activity. ISO and IEC
technical committees collaborate in fields of mutual interest. Other international organizations, governmental
and non-governmental, in liaison with ISO and IEC, also take part in the work. In the field of information
technology, ISO and IEC have established a joint technical committee, ISO/IEC JTC 1.
International Standards are drafted in accordance with the rules given in the ISO/IEC Directives, Part 2.
The main task of the joint technical committee is to prepare International Standards. Draft International
Standards adopted by the joint technical committee are circulated to national bodies for voting. Publication as
an International Standard requires approval by at least 75 % of the national bodies casting a vote.
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent
rights. ISO and IEC shall not be held responsible for identifying any or all such patent rights.
ISO/IEC 19795-1 was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 37, Biometrics.
ISO/IEC 19795 consists of the following parts, under the general title Information technology — Biometric
performance testing and reporting:
⎯ Part 1: Principles and framework
⎯ Part 2: Testing methodologies for technology and scenario evaluation
The following parts are under preparation:
⎯ Part 3: Modality-specific testing [Technical Report]
⎯ Part 4: Performance and interoperability testing of data interchange formats
⎯ Part 5: Performance of biometric access control systems
© ISO/IEC 2006 – All rights reserved v
Introduction
This part of ISO/IEC 19795 is concerned solely with the scientific “technical performance testing” of biometric
systems and devices. Technical performance testing seeks to determine error and throughput rates, with the
goal of understanding and predicting the real-world error and throughput performance of biometric systems.
The error rates include both false positive and false negative decisions, as well as failure-to-enrol and failure-
to-acquire rates across the test population. Throughput rates refer to the number of users processed per unit
time based both on computational speed and human–machine interaction. These measures are generally
applicable to all biometric systems and devices. Technical performance tests that are device-specific — for
example, fingerprint scanner image quality — are not considered in this part of ISO/IEC 19795.
It is acknowledged that technical performance testing is only one form of biometric testing. Other types of
testing not considered in this part of ISO/IEC 19795 include
⎯ reliability, availability and maintainability;
⎯ security, including vulnerability;
⎯ conformance;
⎯ safety;
⎯ human factors, including user acceptance;
⎯ cost/benefit;
⎯ privacy regulation compliance.
Methods and philosophies for these other types of test are currently being considered internationally by a
broad range of groups.
The purpose of this part of ISO/IEC 19795 is to present the requirements and best scientific practices for
conducting technical performance testing. This is necessary because even a short review of the technical
literature on biometric device testing over the last two decades or more reveals a wide variety of conflicting
and contradictory testing protocols [1-11]. Even single organizations have produced multiple tests, each using
a different test method. Test protocols have varied not only because test goals and available data are different
from one test to the next, but also because no standard has existed for protocol creation.
Biometric technical performance testing can be of three types: technology, scenario or operational evaluation.
Each type of test requires a different protocol and produces different types of results. Even for tests of a single
type, the wide variety of biometric devices, sensors, vendor instructions, data acquisition methods, target
applications and populations makes it impossible to present precise uniform testing protocols. Other parts of
ISO/IEC 19795 will provide specific advice and requirements for the development and use of such different
test protocols. This part of ISO/IEC 19795 addresses specific philosophies and principles that can be applied
over a broad range of test conditions.
This part of ISO/IEC 19795 has been developed from the UK Biometrics Working Group’s Best Practices in
Testing and Reporting Performance of Biometric Devices [12] which itself drew from two primary source
documents developed by the US National Institute of Standards and Technology (NIST) [13, 14], a variety of
evaluation reports [7-10], and comments from the Biometrics Consortium Working Group on Interoperability,
Performance and Assurance.
vi © ISO/IEC 2006 – All rights reserved
INTERNATIONAL STANDARD ISO/IEC 19795-1:2006(E)
Information technology — Biometric performance testing
and reporting —
Part 1:
Principles and framework
1 Scope
This part of ISO/IEC 19795
⎯ establishes general principles for testing the performance of biometric systems in terms of error rates and
throughput rates for purposes including prediction of performance, comparison of performance, and
verifying compliance with specified performance requirements;
⎯ specifies performance metrics for biometric systems;
⎯ specifies requirements on test methods, recording of data and reporting of results; and
⎯ provides a framework for developing and describing test protocols, to help avoid bias due to inappropriate
data collection or analytic procedures, to help achieve the best estimate of field performance for the
expended effort, and to improve understanding of the limits of applicability of the test results.
This part of ISO/IEC 19795 is applicable to empirical performance testing of biometric systems and algorithms
through analysis of the matching scores and decisions output by the system, without detailed knowledge of
the system’s algorithms or of the underlying distribution of biometric characteristics in the population of
interest.
Not within the scope of this part of ISO/IEC 19795 is the measurement of error and throughput rates for
people deliberately trying to circumvent correct recognition by the biometric system (i.e. active impostors).
2 Conformance
To conform to this part of ISO/IEC 19795, a biometric performance test shall be planned, executed and
reported in accordance with the mandatory requirements contained herein.
3 Normative references
The following referenced documents are indispensable for the application of this document. For dated
references, only the edition cited applies. For undated references, the latest edition of the referenced
document (including any amendments) applies.
ISO/IEC 17025, General requirements for the competence of testing and calibration laboratories
© ISO/IEC 2006 – All rights reserved 1
4 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
4.1 Biometric data
4.1.1
sample
user’s biometric measures as output by the data capture subsystem
EXAMPLE Fingerprint image, face image and iris image are samples.
NOTE In more complex systems, the sample may consist of multiple presented characteristics (e.g., 10-print
fingerprint record, face image captured from different angles, left and right iris image pair).
4.1.2
features
digital representation of the information extracted from a sample (by the signal processing subsystem) that will
be used to construct or compare against enrolment templates
EXAMPLE Minutiae coordinates and principal component coefficients are features.
4.1.3
template
model
user’s stored reference measure based on features extracted from enrolment samples
NOTE The reference measure is often a template comprising the biometric features for an ideal sample presented by
the user. More generally, the stored reference will be a model representing the potential range of biometric features for
that user. In this part of ISO/IEC 19795, we normally use “template” to include “model”.
4.1.4
matching score
similarity score
measure of the similarity between features derived from a sample and a stored template, or a measure of how
well these features fit a user’s reference model
NOTE 1 A match or non-match decision may be made according to whether this score exceeds a decision threshold.
NOTE 2 As features derived from a presented sample become closer to the stored template, similarity scores will
increase.
4.1.5
verification decision
determination of the probable validity of a user’s claim to identity in the system
4.1.6
candidate list
set of potential enrolled identifiers for a subject produced by an identification attempt (or by a pre-selection
algorithm)
4.1.7
identification decision
determination of a candidate list for the user’s probable identity in the system
2 © ISO/IEC 2006 – All rights reserved
4.2 User interaction with a biometric system
4.2.1
presentation
submission of a single biometric sample on the part of a user
4.2.2
attempt
submission of one (or a sequence of) biometric samples to the system
NOTE An attempt results in an enrolment template, a matching score (or scores), or possibly a failure–to-acquire.
4.2.3
transaction
sequence of attempts on the part of a user for the purposes of an enrolment, verification or identification
NOTE There are three types of transaction: enrolment sequence, resulting in an enrolment or a failure-to-enrol; a
verification sequence resulting in a verification decision; or identification sequence, resulting in an identification decision.
4.2.4
genuine attempt
single good-faith attempt by a user to match their own stored template
4.2.5
zero-effort impostor attempt
attempt in which an individual submits his/her own biometric characteristics as if he/she were attempting
successful verification against his/her own template, but the comparison is made against the template of
another user
4.2.6
active impostor attempt
attempt in which an individual tries to match the stored template of a different individual by presenting a
simulated or reproduced biometric sample, or by intentionally modifying his/her own biometric characteristics
NOTE Error rates for active impostor attempts will vary from those for zero-effort impostor attempts. Defining the
methods and skill used in active impostor attempts is outside the scope of this part of ISO/IEC 19795.
4.2.7
presentation effects
broad category of variables affecting the way in which the users’ inherent biometric characteristics are
displayed to the sensor
EXAMPLE In facial recognition, this could include pose angle and illumination; in fingerprinting, finger rotation and
skin moisture. In many cases, the distinction between changes in the fundamental biometric characteristic and the
presentation effects may not be clear (e.g. facial expression in facial recognition or pitch change in speaker verification
systems).
4.2.8
channel effects
changes imposed on the presented signal in the transduction and transmission process due to the sampling,
noise and frequency response characteristics of the sensor and transmission channel
4.3 Personnel involved in the evaluation
4.3.1
user
person presenting a biometric sample to the system
© ISO/IEC 2006 – All rights reserved 3
4.3.2
test subject
user whose biometric data is intended to be enrolled or compared as part of the evaluation
4.3.3
crew
set of test subjects gathered for an evaluation
4.3.4
target population
set of users of the application for which performance is being evaluated
4.3.5
administrator
person performing the testing or enrolment
4.3.6
operator
individual with function in the actual system
EXAMPLE Staff conducting enrolments or overseeing verification or identification transactions.
4.3.7
observer
test staff member recording test data or monitoring the crew
4.3.8
experimenter
person responsible for defining, designing and analysing the test
4.3.9
test organization
functional entity under whose auspices the test is conducted
4.4 Types of evaluation
4.4.1
technology evaluation
offline evaluation of one or more algorithms for the same biometric modality using a pre-existing or specially-
collected corpus of samples
4.4.2
scenario evaluation
evaluation in which the end-to-end system performance is determined in a prototype or simulated application
4.4.3
operational evaluation
evaluation in which the performance of a complete biometric system is determined in a specific application
environment with a specific target population
4.4.4
online
pertaining to execution of enrolment and matching at the time of image or signal submission
NOTE Online testing has the advantage that the biometric sample can be immediately discarded, saving the need
for storage and for the system to operate in a manner different from usual. However, it is recommended that images or
signals are collected if possible.
4 © ISO/IEC 2006 – All rights reserved
4.4.5
offline
pertaining to execution of enrolment and matching separately from image or signal submission
NOTE 1 Collecting a corpus of images or signals for offline enrolment and calculation of matching scores allows
greater control over which attempts and template images are to be used in any transaction.
NOTE 2 Technology testing will always involve data storage for later, offline processing. However, with scenario and
operational testing, online transactions might be simpler for the tester – the system is operating in its usual manner and,
although recommended, storage of images or signals is not absolutely necessary.
4.5 Biometric applications
4.5.1
verification
application in which the user makes a positive claim to an identity, features derived from the submitted sample
biometric measure are compared to the enrolled template for the claimed identity, and an accept or reject
decision regarding the identity claim is returned
NOTE The claimed identity might be in the form of a name, personal identification number (PIN), swipe card, or other
unique identifier provided to the system.
4.5.2
identification
application in which a search of the enrolled database is performed, and a candidate list of 0, 1 or more
identifiers is returned
4.5.3
closed-set identification
identification for which all potential users are enrolled in the system
4.5.4
open-set identification
identification for which some potential users are not enrolled in the system
4.6 Performance measures
4.6.1
failure-to-enrol rate
FTE
proportion of the population for whom the system fails to complete the enrolment process
NOTE The observed failure-to-enrol rate is measured on test crew enrolments. The predicted/expected failure-to-
enrol rate will apply to the entire target population.
4.6.2
failure-to-acquire rate
FTA
proportion of verification or identification attempts for which the system fails to capture or locate an image or
signal of sufficient quality
NOTE The observed failure-to-acquire rate is distinct from the predicted/expected failure-to-acquire rate (the former
may be used to estimate the latter).
4.6.3
false non-match rate
FNMR
proportion of genuine attempt samples falsely declared not to match the template of the same characteristic
from the same user supplying the sample
NOTE The measured/observed false non-match rate is distinct from the predicted/expected false non-match rate (the
former may be used to estimate the latter).
© ISO/IEC 2006 – All rights reserved 5
4.6.4
false match rate
FMR
proportion of zero-effort impostor attempt samples falsely declared to match the compared non-self template
NOTE The measured/observed false match rate is distinct from the predicted/expected false match rate (the former
may be used to estimate the latter).
4.6.5
false reject rate
FRR
proportion of verification transactions with truthful claims of identity that are incorrectly denied
4.6.6
false accept rate
FAR
proportion of verification transactions with wrongful claims of identity that are incorrectly confirmed
4.6.7
(true-positive) identification rate
identification rate
proportion of identification transactions by users enrolled in the system in which the user’s correct identifier is
among those returned
NOTE 1 This identification rate is dependent on (a) the size of the enrolment database, and (b) a decision threshold for
matching scores and/or the number of matching identifiers returned.
4.6.8
false-negative identification-error rate
FNIR
proportion of identification transactions by users enrolled in the system in which the user’s correct identifier is
not among those returned
NOTE False-negative identification-error rate = 1 – true-positive identification rate.
4.6.9
false-positive identification-error rate
FPIR
proportion of identification transactions by users not enrolled in the system, where an identifier is returned
NOTE 1 The false-positive identification-error rate is dependent on (a) the size of the enrolment database, and (b) a
decision threshold for matching scores and/or the number of matching identifiers returned.
NOTE 2 With closed-set identification false-positive identification is not possible, as all users are enrolled.
4.6.10
pre-selection algorithm
algorithm to reduce the number of templates that need to be matched in an identification search of the
enrolment database
4.6.11
pre-selection error
〈pre-selection algorithm〉 error that occurs when the corresponding enrolment template is not in the pre-
selected subset of candidates when a sample from the same biometric characteristic on the same user is
given
NOTE In binning pre-selection, pre-selection errors happen when the enrolment template and a subsequent sample
from the same biometric characteristic on the same user are placed in different partitions.
6 © ISO/IEC 2006 – All rights reserved
4.6.12
penetration rate
〈pre-selection algorithm〉 measure of the average number of pre-selected templates as a fraction of the total
number of templates
4.6.13
identification rank
smallest value k for which a user’s correct identifier is in the top k identifiers returned by an identification
system
NOTE Identification rank is dependent on the size of the enrolment database, and should be quoted “rank k out of n”.
4.7 Data presentation curves
4.7.1
detection error trade-off curve
DET curve
modified ROC curve which plots error rates on both axes (false positives on the x-axis and false negatives on
the y-axis)
NOTE An example set of DET curves is shown in 10.6.2, Figure 3.
4.7.2
receiver operating characteristic curve
ROC curve
plot of the rate of false positives (i.e. impostor attempts accepted) on the x-axis against the corresponding rate
of true positives (i.e. genuine attempts accepted) on the y-axis plotted parametrically as a function of the
decision threshold
NOTE An example set of ROC curves is shown in 10.6.3, Figure 4.
4.7.3
cumulative match characteristic curve
CMC curve
graphical presentation of results of an identification task test, plotting rank values on the x-axis and the
probability of correct identification at or below that rank on the y-axis
NOTE An example set of CMC curves is shown in 10.6.4, Figure 5.
4.8 Statistical terms
4.8.1
variance
V
measure of the spread of a statistical distribution
NOTE 1 If E(X) represents the distribution mean of a random variable X, then V(X)=E( (X-µ) ), where µ = E[X].
NOTE 2 The variance, if known, shows how close an estimated result is likely to be to its true value.
4.8.2
confidence interval
a lower estimate L and an upper estimate U for a parameter x such that the probability of the true value of x
being between L and U is the stated value (e.g. 95 %)
EXAMPLE If [L,U] is a (95 %) confidence interval for parameter x, then probabilityxL∈=,U 95 %.
[]
( )
NOTE The smaller the test size, the wider the confidence interval.
© ISO/IEC 2006 – All rights reserved 7
5 General biometric system
5.1 Conceptual diagram of general biometric system
Given the variety of applications and technologies, it might seem difficult to draw any generalizations about
biometric systems. All such systems, however, have many elements in common. Biometric samples are
acquired from a subject by a sensor. The sensor output is sent to a processor which extracts the distinctive
but repeatable measures of the sample (the features), discarding all other components. The resulting features
can be stored in the database as a template, or compared to a specific template, many templates or all
templates already in a database to determine if there is a match. A decision regarding the identity claim is
made based upon the similarity between the sample features and those of the template or templates
compared.
Data Data
Matching
Decision
Storage
Capture
Enrolment
Matching
Database
Identity
Template
Similarity
Claim
Score(s)
Signal
Presentation Template
Processing
Match? Candidate?
Template
Candidate
Match/
Creation
Threshold
List
Non-match
Biometric
Characteristics
Features
Verified? Identified?
Quality Control
Re-acquire
Feature Extraction
Sensor
Decision
Segmentation
Criteria
Sample
Verification Identification
Outcome Outcome
Enrolment
Verification
Identification
Figure 1 — Components of general biometric system
Figure 1 illustrates the information flow within a general biometric system consisting of data capture, signal
processing, storage, matching, and decision subsystems. This diagram illustrates both enrolment, and the
operation of verification and identification systems. The following subclauses describe each of these
subsystems in more detail. It should be noted that, in any real biometric system, these conceptual
components may not exist or may not directly correspond to the physical components, for example quality
control could also take place before segmentation or before feature extraction.
5.2 Conceptual components of a general biometric system
5.2.1 Data capture subsystem
The data capture subsystem collects an image or signal of a subject’s biometric characteristics that they have
presented to the biometric sensor, and outputs this image/signal as a biometric sample.
8 © ISO/IEC 2006 – All rights reserved
5.2.2 Transmission subsystem (Not portrayed in diagram)
The transmission subsystem (not always present or visibly present in a biometric system) will transmit
samples, features, and/or templates between different subsystems. Samples, features or templates may be
transmitted using standard biometric data interchange formats. The biometric sample may be compressed
and/or encrypted before transmission, and expanded and/or decrypted before use. A biometric sample may
be altered in transmission due to noise in the transmission channel as well as losses in the
compression/expansion process. It is advisable that cryptographic techniques be used to protect the
authenticity, integrity, and confidentiality of stored and transmitted biometric data.
5.2.3 Signal processing subsystem
The signal processing subsystem extracts the distinguishing features from a biometric sample. This may
involve locating the signal of the subject’s biometric characteristics within the received sample (a process
known as segmentation), feature extraction, and quality control to ensure that the extracted features are likely
to be distinguishing and repeatable. Should quality control reject the received sample/s, control may return to
the data capture subsystem to collect a further sample/s.
In the case of enrolment, the signal processing subsystem creates a template from the extracted biometric
features. Often the enrolment process requires features from several presentations of the individual’s
biometric characteristics. Sometimes the template comprises just the features.
5.2.4 Data storage subsystem
Templates are stored within an enrolment database held in the data storage subsystem. Each template is
associated with details of the enrolled subject. It should be noted that prior to being stored in the enrolment
database, templates may be re-formatted into a biometric data interchange format. Templates may be stored
within a biometric capture device, on a portable medium such as a smart card, locally such as on a personal
computer or local server, or in a central database.
5.2.5 Matching subsystem
In the matching subsystem, the features are compared against one or more templates and similarity scores
are passed to the decision subsystem. The similarity scores indicate the degree of fit between the features
and template/s compared. In some cases, the features may take the same form as the stored template. For
verification, a single specific claim of subject enrolment would lead to a single similarity score. For
identification, many or all templates may be compared with the features, and output a similarity score for each
comparison.
5.2.6 Decision subsystem
The decision subsystem uses the similarity scores generated from one or more attempts to provide the
decision outcome for a verification or identification transaction.
In the case of verification, the features are considered to match a compared template when the similarity
score exceeds a specified threshold. A claim about the subject’s enrolment can then be verified on the basis
of the decision policy, which may allow or require multiple attempts.
In the case of identification, the enrolee identifier or template is a potential candidate for the subject when the
similarity score exceeds a specified threshold, and/or when the similarity score is among the highest k values
generated for a specified value k. The decision policy may allow or require multiple attempts before making an
identification decision.
NOTE Conceptually, it is possible to treat multi-biometric systems in the same manner as uni-biometric systems, by
treating the combined biometric samples/templates/scores as if they were a single sample/template/score and allowing the
decision subsystem to operate score fusion or decision fusion as and if appropriate.
© ISO/IEC 2006 – All rights reserved 9
5.2.7 Administration subsystem (Not portrayed in diagram)
The administration subsystem governs the overall policy, implementation and usage of the biometric system,
in accordance with the relevant legal, jurisdictional and societal constraints and requirements. Illustrative
examples include:
⎯ providing feedback to the subject during and/or after data capture;
⎯ requesting additional information from the subject;
⎯ storage and format of the biometric templates and/or biometric interchange data;
⎯ provide final arbitration on output from decision and/or scores;
⎯ set threshold values;
⎯ set biometric system acquisition settings;
⎯ control the operational environment and non-biometric data storage;
⎯ provide appropriate safeguards for end-user privacy;
⎯ interact with the application that utilizes the biometric system.
5.2.8 Interface (Not portrayed in diagram)
The biometric system may or may not interface to an external application or system via an Application
Programming Interface, Hardware Interface or a Protocol Interface.
5.3 Functions of general biometric system
5.3.1 Enrolment
In enrolment, a transaction by a subject is processed by the system in order to generate and store an
enrolment template for that individual.
Enrolment typically involves:
⎯ sample acquisition,
⎯ segmentation and feature extraction,
⎯ quality checks, (which may reject the sample/features as being unsuitable for creating a template, and
require acquisition of further samples),
⎯ template creation (which may require features from multiple samples), possible conversio
...
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