ISO/IEC 30137-1:2024

Information technology - Use of biometrics in video surveillance systems - Part 1: System design and specification

The ISO/IEC 30137 series is applicable to the use of biometrics in VSSs (also known as closed circuit television or CCTV systems) for a number of scenarios, including real-time operation against watchlists and in post-event analysis of video data. In most cases, the biometric mode of choice will be face recognition, but this document also provides guidance for other modalities, such as gait recognition. This document: - defines the key terms for use in the specification of biometric technologies in a VSS, including metrics for defining performance; - provides guidance on the selection of camera types, placement of cameras, image specification, etc., for the operation of a biometric recognition capability in conjunction with a VSS; - provides guidance on the composition of the gallery (or watchlist) against which facial images from the VSS are compared, including the selection of appropriate images of sufficient quality, and the size of the gallery in relation to performance requirements; - makes recommendations on data formats for facial images and other relevant information (including metadata) obtained from video footage, used in watchlist images, or from observations made by human operators; - establishes general principles for supporting the operator of the VSS, including user interfaces and processes to ensure efficient and effective operation, and highlights the need to have suitably trained personnel; - highlights the need for robust governance processes to provide assurance that the implemented security, privacy and personal data protection measures specific to the use of biometric technologies with a VSS (e.g. internationally recognizable signage) are fit for purpose, and that societal considerations are reflected in the deployed system. This document also provides information on related recognition and detection tasks in a VSS, such as: - estimation of crowd densities; - determination of patterns of movement of individuals; - identification of individuals appearing in more than one camera; - use of other biometric modalities such as gait or iris; - use of specialized software to infer attributes of individuals, e.g. estimation of gender and age; - interfaces to another related functionality, e.g. video analytics to measure queue lengths or to provide alerts for abandoned baggage.

Technologies de l'information — Utilisation de la biométrie dans les systèmes de vidéosurveillance — Partie 1: Conception et spécification

General Information

• Status: Published
• Publication Date: 07-Mar-2024
• ICS: 35.240.15 - Identification cards. Chip cards. Biometrics
• Technical Committee: ISO/IEC JTC 1/SC 37 - Biometrics
• Drafting Committee: ISO/IEC JTC 1/SC 37/WG 4 - Technical Implementation of Biometric Systems
• Current Stage: 6060 - International Standard published
• Start Date: 08-Mar-2024
• Due Date: 12-Mar-2025
• Completion Date: 08-Mar-2024

Relations

• Revises: ISO/IEC 30137-1:2019 - Information technology - Use of biometrics in video surveillance systems - Part 1: System design and specification
• Effective Date: 16-Sep-2023

Overview

ISO/IEC 30137-1:2024 - Information technology - Use of biometrics in video surveillance systems - Part 1: System design and specification - provides guidance for designing and specifying biometric capabilities integrated with video surveillance systems (VSS/CCTV). The standard covers real-time watchlist matching and post-event analysis, primarily for face recognition but also addressing other modalities such as gait recognition. It defines key terms, performance metrics and system architecture considerations to help organizations deploy biometric-enabled VSS in a technically robust and socially responsible way.

Key technical topics and requirements

• Terms, definitions and metrics: Standardized vocabulary for biometric concepts in VSS and metrics for defining performance and evaluation.
• Architecture and use cases: Reference architectures and scenarios including real-time alerts, post-event review and enrolment workflows.
• Hardware and software specification: Guidance on camera selection, camera positioning, illumination, inducing frontal views, and supporting infrastructure required for reliable biometric capture.
• Biometric software requirements: Recommendations for face detection, face comparison algorithms, algorithm selection, testing and related non-biometric analytics.
• Reference image (gallery/watchlist) guidance: Best practices on image selection, image quality, gallery size relative to performance needs, and maintenance of reference databases.
• Computational and multi-camera considerations: Sizing compute resources, optimizing core biometric processes and coordinating multi-camera operations.
• Data formats and metadata: Recommendations for formats for facial images and derived metadata used for matching, audit and operator review.
• Operator support and user interfaces: Principles for effective operator workflows, alert presentation, and training requirements for human reviewers.
• Governance, privacy and societal considerations: Requirements for data protection, signage, accountability and processes to ensure security, privacy and fit‑for‑purpose deployment.
• Performance and PAD metrics: Guidance on performance targets and presentation attack detection (PAD) metrics relevant to VSS contexts.

Practical applications

• Real-time watchlist alerting (security checkpoints, high-value sites)
• Post-event forensic review (law enforcement investigations)
• Border and traveller triaging (integrated with e-passport checks)
• Commercial uses (customer recognition, premium service)
• Crowd analytics and related video analytics (crowd density, queue length, abandoned baggage)

Who should use this standard

• System integrators and VSS designers
• Security architects and operations teams
• Law enforcement and border agencies
• Procurement officers specifying biometric VSS solutions
• Privacy officers, governance and compliance teams
• Test labs and evaluators assessing performance

Related standards

• Other parts of the ISO/IEC 30137 series (performance testing, data annotation)
• Relevant biometric data format and privacy standards (see normative references in the document for specifics)

ISO/IEC 30137-1:2024 is essential for organizations seeking to specify, deploy or evaluate biometric-enabled video surveillance systems with clear technical, operational and governance guidance.

Standards Content (Sample)

International
Standard
ISO/IEC 30137-1
Second edition
Information technology — Use of
2024-03
biometrics in video surveillance
systems —
Part 1:
System design and specification
Technologies de l'information — Utilisation de la biométrie dans
les systèmes de vidéosurveillance —
Partie 1: Conception et spécification
Reference number
© ISO/IEC 2024
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
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Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
© ISO/IEC 2024 – All rights reserved
ii
Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 2
3 Terms and definitions . 2
3.1 Terms related to the target subject .2
3.2 Terms related to VSS .2
3.3 Terms related to biometric systems .4
3.4 Terms related to the environment and scenario .4
3.5 Symbols and abbreviated terms .5
4 Comparison of terms used in biometric systems with those used in video surveillance . 5
5 Architecture. 6
6 Use cases . 7
6.1 General .7
6.2 Post-event use cases .8
6.3 Real-time use cases .8
6.4 Enrolment use cases .9
7 Specification of hardware and software . 9
7.1 General .9
7.2 Physical environment .10
7.3 Illumination environment .10
7.4 Inducing frontal view .10
7.5 Cameras and supporting infrastructure .11
7.5.1 Selection of cameras.11
7.5.2 Positioning of cameras . 12
7.5.3 Infrastructure considerations .16
7.6 Biometric software .17
7.6.1 General .17
7.6.2 Face detection software .17
7.6.3 Face comparison software.18
7.6.4 Algorithm selection and testing .18
7.6.5 Other (non-biometric) software .19
7.7 Computational requirements .19
7.7.1 General .19
7.7.2 Core biometric processes.19
7.7.3 Reducing computational expense . 20
7.8 Specification for reference image database .21
7.8.1 General .21
7.8.2 Reference database size .21
7.8.3 Reference image quality .21
7.8.4 Reference database maintenance . 22
8 Multiple camera operation.22
9 Interfaces to related software .23
10 Guidance for operator assistance .23
11 System design considerations .24
11.1 General .24
11.2 Establishing the business requirements .24
11.3 Site survey .24
11.4 Size and content of the watchlist . 26
11.5 Performance requirements . 26

© ISO/IEC 2024 – All rights reserved
iii
11.5.1 General . 26
11.5.2 Key metrics of performance . 26
11.5.3 PAD performance metrics .27
11.6 Image data and metadata considerations .27
Annex A (informative) Related (non-biometric) video analytic techniques and applications .28
Annex B (informative) Societal considerations and governance processes .31
Annex C (informative) Case study: The use of AFR with VSS for traveller triaging at the border .33
Annex D (informative) Video acquisition measurements .35
Bibliography .45

© ISO/IEC 2024 – All rights reserved
iv
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.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/
IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
ISO and IEC draw attention to the possibility that the implementation of this document may involve the
use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any
claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not
received notice of (a) patent(s) which may be required to implement this document. However, implementers
are cautioned that this may not represent the latest information, which may be obtained from the patent
database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held
responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.html.
In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 37, Biometrics.
This second edition cancels and replaces the first edition (ISO/IEC 30137-1:2019), of which it constitutes a
minor revision. The changes are as follows:
— in the interest of using inclusive language, the terms "black list" and "white list" have been updated to
"block list" and "allow list", respectively;
— minor editorial corrections have been made throughout the text, including corrections to cross-
referencing within the document itself.
A list of all parts in the ISO/IEC 30137 series can be found on the ISO and IEC websites.
Any feedback or questions on this document should be directed to the user’s national standards
body. A complete listing of these bodies can be found at www.iso.org/members.html and
www.iec.ch/national-committees.

© ISO/IEC 2024 – All rights reserved
v
Introduction
Considerable improvements in the performance of automatic facial recognition (AFR) technologies have
resulted in applications such as automated border control using the facial images encoded in e-passports
and implemented in systems whereby the identity of a co-operative traveller is verified in an environment
designed for the collection of uniformly illuminated and optimally posed images. The success of these first
generation AFR systems has encouraged suppliers to consider other applications where the environment for
collection of images may be far from optimal. The inferior performance in such identification applications
with less control can necessitate a greater involvement by trained personnel.
The ISO/IEC 30137 series provides guidance on the use of biometric technologies in video surveillance
systems (VSSs), a framework for performance testing and reporting of such systems, and procedures for
establishing ground truth and annotating video data for testing purposes.
This document provides the architecture, use cases and system design. The use cases include real-time
alerting to the presence of individuals of interest, law enforcement applications such as reviewing post-
event video footage from one or more cameras against pre-populated watchlists, commercial uses such as
the identification of individuals who are to be given preferential service, and faces added to (enrolled in) a
watchlist following observation of behaviours in the video material.
Other scenarios include measurement of crowd densities and determining numbers of individuals traversing
a given point. While these are not the focus of this document, they are closely related and information on
these scenarios is therefore included in Annex A.

© ISO/IEC 2024 – All rights reserved
vi
International Standard ISO/IEC 30137-1:2024(en)
Information technology — Use of biometrics in video
surveillance systems —
Part 1:
System design and specification
1 Scope
The ISO/IEC 30137 series is applicable to the use of biometrics in VSSs (also known as closed circuit
television or CCTV systems) for a number of scenarios, including real-time operation against watchlists and
in post-event analysis of video data. In most cases, the biometric mode of choice will be face recognition, but
this document also provides guidance for other modalities, such as gait recognition.
This document:
— defines the key terms for use in the specification of biometric technologies in a VSS, including metrics for
defining performance;
— provides guidance on the selection of camera types, placement of cameras, image specification, etc., for
the operation of a biometric recognition capability in conjunction with a VSS;
— provides guidance on the composition of the gallery (or watchlist) against which facial images from the
VSS are compared, including the selection of appropriate images of sufficient quality, and the size of the
gallery in relation to performance requirements;
— makes recommendations on data formats for facial images and other relevant information (including
metadata) obtained from video footage, used in watchlist images, or from observations made by human
operators;
— establishes general principles for supporting the operator of the VSS, including user interfaces and
processes to ensure efficient and effective operation, and highlights the need to have suitably trained
personnel;
— highlights the need for robust governance processes to provide assurance that the implemented security,
privacy and personal data protection measures specific to the use of biometric technologies with a
VSS (e.g. internationally recognizable signage) are fit for purpose, and that societal considerations are
reflected in the deployed system.
This document also provides information on related recognition and detection tasks in a VSS, such as:
— estimation of crowd densities;
— determination of patterns of movement of individuals;
— identification of individuals appearing in more than one camera;
— use of other biometric modalities such as gait or iris;
— use of specialized software to infer attributes of individuals, e.g. estimation of gender and age;
— interfaces to another related functionality, e.g. video analytics to measure queue lengths or to provide
alerts for abandoned baggage.
© ISO/IEC 2024 – All rights reserved
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1 Terms related to the target subject
3.1.1
operator
individual(s) responsible for day-to-day operation of the system
Note 1 to entry: This may include adjustment of the video surveillance cameras, selecting data suitable for use by the
biometric application, and acting on the output of the biometric comparison process.
3.1.2
presentation attack
presentation of an artefact or of human characteristics to a biometric capture subsystem in a fashion that
can interfere with the intended policy of the biometric system
3.1.3
target subject(s)
target(s)
individual(s) of interest
Note 1 to entry: A target subject is normally someone already enrolled in a watchlist (3.1.4). However, this is not always
the case; in some scenarios they are a target because they are to be enrolled in a watchlist.
3.1.4
watchlist
list of individuals of interest (and their associated reference images) for detection by the video surveillance
application
Note 1 to entry: The watchlist may be of individuals for whom an added service level is to be offered (e.g. VIPs or
premium customers). This is sometimes referred to as an “allow list”.
Note 2 to entry: The watchlist may be a list of “wanted” individuals, e.g. individuals who should be denied access to
premises or services. This is sometimes referred to as a “block list”.
Note 3 to entry: A system may have multiple watchlists of different groups of target subjects (3.1.3), and with different
performance goals.
Note 4 to entry: In the case of target subject back-tracking (3.3.1), the watchlist normally contains only one target
subject, or in the case of a group of individuals of interest, a few target subjects.
3.2 Terms related to VSS
3.2.1
codec
computer program capable of encoding or decoding a digital data stream or signal

© ISO/IEC 2024 – All rights reserved
3.2.2
compression ratio
measure of the compressed file size to that of the uncompressed file size
3.2.3
dropped frame
frame (3.2.4) from the video camera(s) that is not processed or is not available for facial detection and the
creation of templates
Note 1 to entry: Normally measured in terms of either the number of frames per second dropped, or the percentage of
the frames per second dropped.
3.2.4
frame
single image shown as part of a sequence of images in a video stream
3.2.5
frame rate
frequency (rate) at which an imaging device produces unique consecutive images called frames (3.2.4)
Note 1 to entry: Frame rate is normally expressed in frames per second (fps).
3.2.6
frame size
pixel dimensions of the frame (3.2.4) described in terms of horizontal and vertical pixels, and which can also
be additionally described in terms of total megapixels
3.2.7
post-processing
steps performed after the biometric comparison process
EXAMPLE Triaging decisions based on a fusion of the quality and score metrics.
3.2.8
pre-processing
steps performed prior to the biometric comparison process
EXAMPLE Image quality enhancement, subject detection and feature extraction.
3.2.9
resolution
measure of the amount of detail that can be stored in an image
Note 1 to entry: Resolution is normally measured in pixels per millimetre.
3.2.10
subject tracking
process of aggregating multiple biometric samples for a single individual, possibly from multiple cameras, to
avoid producing separate detection alerts for the same target subject (3.1.3)
3.2.11
video management system
VMS
component of a video surveillance system (3.2.12) that collects video from cameras and other sources, records
that video to a storage device and provides an interface to both view the live video and to randomly access
recorded video according to time
3.2.12
video surveillance system
VSS
system consisting of camera equipment, monitoring and associated equipment for transmission and
controlling purposes, which may be necessary for the surveillance of a protected area

© ISO/IEC 2024 – All rights reserved
3.3 Terms related to biometric systems
3.3.1
back-tracking
act of finding the given image(s) of a face/individual by searching all video feeds where the individual can
possibly have been seen
Note 1 to entry: It is possible, but not necessary, to use facial biometrics for back-tracking.
3.3.2
face detection
determination of the presence of faces within a video frame (3.2.4) and production of the location of each
face in the frame
Note 1 to entry: Face detection is the first step in the face recognition process.
3.3.3
post-event analysis
non-real-time analysis (3.3.4) of data previously captured by video surveillance cameras
EXAMPLE To identify possible suspects following an incident or event.
3.3.4
real-time analysis
online processing of video surveillance data as it is captured
EXAMPLE To identify individuals held on a watchlist (3.1.4) so that immediate action can be taken.
3.3.5
Wiegand
de-facto wiring standard commonly used to connect a card swipe mechanism to the rest of an electronic
entry system
3.3.6
zone of recognition
3-dimensional space within the field of view of the camera and in which the imaging conditions for robust
biometric recognition are met
Note 1 to entry: In general, the zone of recognition is smaller than the field of view of the camera, e.g. not all faces in
the field of view may be in focus and not every face in the field of view is imaged with the necessary inter-eye distance
(IED).
3.4 Terms related to the environment and scenario
3.4.1
attractor
visual or acoustic cue within the environment which encourages individuals to look in a particular
direction (i.e. towards the camera in a facial recognition application) in an attempt to improve recognition
performance
3.4.2
choke point
point of congestion or obstruction through which individuals pass
3.4.3
lux
measure of illumination intensity

© ISO/IEC 2024 – All rights reserved
3.5 Symbols and abbreviated terms
AFIS automated fingerprint identification system
AFR automated facial recognition
APCER attack presentation classification error rate
APNRR attack presentation non-response rate
B&W black-and-white
CCTV closed circuit television (system); another term for video surveillance (system)
EXIF exchangeable image file
FPS frames per second
GUI graphical user interface
HDR high dynamic range
HMM Hidden Markov models
IED inter-eye distance; the distance (usually measured in pixels) between the centres of the eyes
IP internet protocol
LFR live facial recognition; real-time automated facial recognition using video surveillance cameras
MTF modulation transfer function
NIST National Institute of Standards and Technology
NPCER normal presentation classification error rate
NPNRR normal presentation non-response rate
OSDP open supervised device protocol
PAD presentation attack detection
PTZ pan, tilt and zoom; a type of video surveillance camera that can be remotely adjusted (manually
by the operator or automatically by using dedicated software)
SFR spatial frequency response
SLI standard lighting intensity
SNR signal to noise ratio
SOP standard operational procedure
VMS video management system
VSS video surveillance system
4 Comparison of terms used in biometric systems with those used in video
surveillance
The video surveillance and biometrics communities both have well established vocabularies to describe the
various components of a system, but the same term may sometimes be interpreted differently. While the

© ISO/IEC 2024 – All rights reserved
terms are defined in Clause 3, Table 1 highlights some of those terms and expressions where care needs to
be taken when communicating with members of the video surveillance community.
Table 1 — Comparison of terms used in biometric systems with those used in video surveillance
Term Definition within the context of automated biom- Definition within the conventional use of
etric processing human-led VSS, e.g. within the scope of
IEC 62676 series
Crowd monitor- Counting of individuals in a volume, or over a time The observation of a group to determine
ing interval collective behaviour or as part of a process
to detect anomalous activity
Detection and Biometric detection: the process of finding instanc- Target detection: the process of finding
localization es of a particular biometric mode, while correctly targets of interest, such as humans or cars,
rejecting all instances of imagery not representing in a video feed
that biometric mode
Observation Tracking: the process of spatially locating a particu- Target observation: the process of following
lar biometric subject as it moves a particular target in a video feed
Recognition The process for assigning a biometric identifier to a The process of recognizing a familiar face;
subject synonym for identification
Identification The process of determining a subject’s identity by The process of a human determining a
comparing imagery of a biometric mode against a subject’s identity using available (printed)
database formed from imagery of individuals. This galleries, or use of identity cues (clothing)
generally includes not assigning an identity when
the target subject is not present in the database
Verification The process of confirming a subject’s identity by The process of confirming a target’s iden-
comparing imagery of a biometric mode against a tity
particular prior sample of a candidate individual
Inspection Human review of the output from an automated bi- Inspection: the detailed review of VSS im-
ometric system to assess an alert from the biometric agery to determine more detailed informa-
subsystem tion or characteristics, such as age or sex of
an individual, brand of clothing, presence of
jewellery
Alert An indication that an identifier for an enrolled sub- An indication issued by a camera, opera-
ject has been returned by the biometric recognition tor or system that an event of interest has
process occurred
5 Architecture
Figure 1 shows the process flow in a typical biometrically-enabled VSS with components such as the
following.
1) Video surveillance cameras positioned to collect images in a form which supports comparison with
images on the watchlist.
2) A VMS and infrastructure to organize and transmit footage from a number of cameras to the main
server and storage system.
3) Software to detect and track faces (and/or other biometric features) in the video stream and to create
biometric feature sets in the format developed by the supplier of the biometric recognition system.
This can include feature sets created by combining features extracted from multiple face images from a
single individual, continuously updated as new video frames are processed.
4) Comparison and decision software, again likely to be proprietary to the supplier of the biometric system,
which determines whether the system has recognized an individual on the watchlist. The match criteria
and decision thresholds may be different for groups of individuals on the watchlist, e.g. some can be
considered low risk, with only minimal implications if they are not recognised by the system, whereas
for others it can be imperative that they are recognized as soon as possible.
5) Alerts generated by the automated system are passed to the human operator for assessment.

© ISO/IEC 2024 – All rights reserved
6) An operator support environment to aid in making decisions on whether an alert should be followed up
(and how) or rejected as a false alert.
7) Links to analytics systems to record the event and decisions taken, and to provide access to other
information which can assist in disposal of the instance of recognition, e.g. previous instances of a
similar match to the individual on the watchlist, and guidance on the appropriate action to be taken.
8) A systems management “bus” which enables configuration and operation of the key components in the
biometric recognition system according to threat level, workload of human operators, time of day, etc.,
and which supports the merging of recognitions between cameras across the surveillance domain.
Figure 1 shows an example of a server-centric architecture. However, there are other models available,
such as distributed architectures using edge computing (where part of the processing is done in the video
camera of the VSS) or where cameras and computing resources are available within smart devices such as
smartphones and PCs.
Figure 1 — Components of a biometrically-enabled VSS
6 Use cases
6.1 General
This clause provides examples of some of the different ways in which biometrics can be used in conjunction
with VSS to support business needs across a range of organizations, including:
— police and law enforcement (and private security companies, such as those operating shopping malls and
car parks) to alert to the presence of individuals of interest;

© ISO/IEC 2024 – All rights reserved
— police and law enforcement to manage the identification of individuals in video surveillance footage
collected after a notable event or incident;
— commercial usage to alert to the presence of individuals of interest for whom special or differentiated
levels of service are to be provided;
— commercial or government systems to manage the flow of individuals or queues, e.g. in accordance with
agreed service levels;
— border services and client support organizations for quality assurance and customer support, e.g.
following a complaint or an incident.
The use cases can be broken down into three broad categories, namely "post-event", "real-time" and
"enrolment" applications (enrolment may be real-time or post-event). The following subclauses provide
examples of some common use cases, described in terms of performance objectives and the roles played by
various components of the system, including the responsibilities of the system operator.
6.2 Post-event use cases
In post-event use cases, the performance objective is the reliable detection, automated feature extraction,
and searching of large numbers of target subjects against one or more watchlists or databases in an attempt
to identify possible suspects, with a high probability that the candidate list returned by the biometric
subsystem includes (at a high rank) those target subjects that have a matching template stored in the
watchlist.
These use cases are challenging because in many cases the quality and positioning of the video cameras will
be beyond the control of the operator of the biometric subsystem, and they will not have been installed with
biometric applications in mind.
The operator normally has an “expert” role within the end-to-end process, selecting images suitable for
submission as probes and examining candidates returned following a search of the database. They may be
trained in facial comparison techniques, and the decision-making process may be supported by dedicated
image analysis tools. In cases such as back-tracking or clustering (linking images of the same subjects
together) the operator may also make use of other visual information (e.g. the individual’s clothes and
relative location of cameras) to help them to confirm or refute potential matches.
Examples of post-event use cases include:
— post-event analysis of recorded video surveillance material (from one or more cameras) processed with
the use of biometric recognition software to identify one or more individuals in frames or sequences
(using one or more reference images);
— post-event analysis of recorded video surveillance material from more than one camera in which an
individual (whether identified or not) is tracked (either forwards or backwards in time) and between
cameras. This may involve more than just biometric applications, for example video analytics software;
— retrospective clustering — detecting and extracting faces from multiple sources of video for the
purposes of clustering imagery sources of the same individual(s) together. This will normally need to be
an automated process due to large numbers of subjects appearing in multiple video streams, although a
human operator may subsequently review the results and intervene where they find subjects who have
been wrongly classified.
6.3 Real-time use cases
In real-time applications, the performance objective is a high probability of the system alerting for target
subjects with a matching template in a watchlist, and a low probability of an alert for subjects not in the
watchlist. The watchlist will typically consist of a subset of images that are drawn from a larger image
database and have been chosen to address a specific business objective.
These use cases are challenging because of the large amount of data that needs to be processed, especially
if the system involves multiple cameras with multiple subjects in each frame. This presents a challenge

© ISO/IEC 2024 – All rights reserved
in terms of search accuracy and it is also vital that the end-to-end response time is fast enough to enable
effective action to be taken when an alert is generated.
The role of the operator is typically to assess any alerts from the biometric subsystem and to make an initial
decision as to whether the alert is genuine or a false match. They are usually also responsible for instigating
further action as appropriate, such as directing resources on the ground to detain or speak to the target
subject in order to formally confirm their identity.
Examples of real-time use cases include:
— alerting in real time (or near real time) to the presence of an individual traversing the field of view of
a video surveillance camera, identified by the biometric subsystem as being someone whose biometric
data (e.g. a facial image) has previously been stored as a reference in a watchlist.
EXAMPLE 1 Checking individuals entering a building or disembarking a plane or train against a watchlist,
with the aim of either bestowing or denying particular privileges.
EXAMPLE 2 Monitoring of video surveillance by law enforcement agencies for the purposes of crime
prevention and public safety.
This use case is sometimes referred to as live facial recognition (LFR). A practical example illustrating
the use of LFR can be found in Annex C;
— real-time tracking of a particular individual of interest between the fields of view of a number of cameras,
some of which do not necessarily overlap.
6.4 Enrolment use cases
In these use cases, the goal is the successful enrolment of target subjects of interest into a database or
watchlist, such that the biometric templates created are of sufficient quality for the intended use. Prior to
enrolment, a biometric search may be carried out to determine if the target subject is already enrolled.
The operator may have a role in selecting the best quality images for enrolment, but in many cases the
process will be fully automated. Machine learning and cognitive computing can be applied to help ensure
that the best available images are selected for enrolment.
Examples of enrolment use cases include:
— enrolment (into a watchlist) of individuals who enter a protected zone or repeatedly visit the same area;
— “time clocking” individuals in situations where there is an interest in knowing how long they spend in a
particular area, for example to monitor the time of service or queue length;
— enrolment of individuals traversing the field of view of a video surveillance camera into a database in
order to support a watchlist application for future use within the same system, or to use in conjunction
with other biometric applications and databases.
7 Specification of hardware and software
7.1 General
The IEC 62676 series already provides extensive information on camera selection, positioning, network
bandwidth, performance considerations, storage requirements, etc., for traditional (i.e. non-biometric)
applications. This document therefore focuses on those aspects of the hardware and software components
of the VSS that have a direct bearing on the performance of the biometric subsystem.
It is important to note that what can be an ideal set up for a conventional VSS can produce images that are
very poorly suited for use in a biometric application. While the following recommendations are primarily
applicable to an AFR system, they can also be adapted for other modes.

© ISO/IEC 2024 – All rights reserved
7.2 Physical environment
In many cases, the environment in which the VSS is intended to operate is beyond the control of those
responsible for deploying or operating the cameras. Careful positioning of the cameras can help (see 7.5.2),
but where it is possible to exert some influence over the environment where the system operates, the
following points should be considered:
— uneven floors and steps should generally be avoided as changes of angle/height often cause individuals
to look down, thus making it hard to obtain usable images of the face;
— barriers may be introduced to modify the flow of individuals through the environment, ensuring they
all pass through the field of view of the camera(s) at the correct distance and moving towards (in the
case of an AFR system) the camera. Such techniques, together with careful positioning of the cameras
can increase the amount of time an individual is within the field of view which will in turn improve the
performance of the system;
— choke points may be introduced to reduce the number of individuals passing through the field of view
of the camera at any one time, thereby reducing the number of target subjects that need to be processed
simultaneously by the biometric application. Choke points can also improve biometric sample collecting
by limiting the speed with which target subjects move through a capture area, by improving the lighting
at that location, and creating a situation where the pose of the target subject to the camera(s) is more
favourable.
The introduction of barriers or choke points may have negative implications for individuals moving
through the environment. Due consideration should be given to the need for usability, accessibility and
user friendliness. The balance between these factors and the need to obtain high quality images in order
to maximize system performance should be determined on a case by case basis. See Annex B for more
information on societal aspects to be considered when employing such techniques.
7.3 Illumination environment
Sufficient illumination is needed to support biometric processing. Where possible, the following points
should be considered:
— areas near windows or in sunlight should generally be avoided as the lighting cannot be controlled and
will vary with the time of the day or year and with prevailing weather conditions. Shaded or artificially
illuminated areas generally produce better results;
— additional lighting may be introduced to raise overall light levels and also to ensure balanced illumination
across faces, with no strong shadows or excessively bright highlights; additional lighting also allows
faster shutter speeds to be used, helping to avoid motion blur in the video;
— near-infrared lighting may also be used (in conjunction with surveillance cameras that can detect
those wavelengths) to help reduce shadows and to
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