ISO/IEC TR 29794-5:2010

TECHNICAL ISO/IEC
REPORT TR
29794-5
First edition
2010-04-01
Information technology — Biometric
sample quality —
Part 5:
Face image data
Technologies de l'information — Qualité d'échantillon biométrique —
Partie 5: Données d'image de face

Reference number
©
ISO/IEC 2010
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ii © ISO/IEC 2010 – All rights reserved

Contents Page
Foreword .iv
Introduction.v
1 Scope.1
2 Normative references.1
3 Terms and definitions .1
4 Abbreviated terms.1
5 Approaches to Face Image Quality .2
6 Categorization of Facial Quality.2
7 Facial Image Quality Analysis.4
7.1 Dynamic Subject Characteristics .5
7.1.1 Subject’s Behaviour .5
7.1.2 Analysis Based on Statistical Differences of the Left and Right Half of the Face.5
7.2 Static Characteristics of the Acquisition Process .7
7.2.1 Image Resolution and Size.8
7.2.2 Noise.8
7.3 Characteristics of Image Acquisition .8
7.3.1 Image Properties.8
7.3.2 Image Appearance.9
7.3.3 Illumination Intensity.9
7.3.4 Image Brightness.9
7.3.5 Image Contrast.10
7.3.6 Exposure.11
7.3.7 Focus, Blur and Sharpness.11
7.3.8 Colour.12
7.3.9 Subject-Camera distance.12
Bibliography.13

© ISO/IEC 2010 – All rights reserved iii

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.
In exceptional circumstances, the joint technical committee may propose the publication of a Technical Report
of one of the following types:
⎯ type 1, when the required support cannot be obtained for the publication of an International Standard,
despite repeated efforts;
⎯ type 2, when the subject is still under technical development or where for any other reason there is the
future but not immediate possibility of an agreement on an International Standard;
⎯ type 3, when the joint technical committee has collected data of a different kind from that which is
normally published as an International Standard (“state of the art”, for example).
Technical Reports of types 1 and 2 are subject to review within three years of publication, to decide whether
they can be transformed into International Standards. Technical Reports of type 3 do not necessarily have to
be reviewed until the data they provide are considered to be no longer valid or useful.
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 TR 29794-5, which is a Technical Report of type 2, was prepared by Joint Technical Committee
ISO/IEC JTC 1, Information technology, Subcommittee SC 37, Biometrics.
ISO/IEC 29794 consists of the following parts, under the general title Information technology — Biometric
sample quality:
⎯ Part 1: Framework
⎯ Part 4: Finger image data [Technical Report]
⎯ Part 5: Face image data [Technical Report]
iv © ISO/IEC 2010 – All rights reserved

Introduction
The purpose of this part of ISO/IEC 29794 is to define and specify methodologies for computation of objective,
quantitative quality scores for facial images. Furthermore, the purpose, intent, and interpretation of face quality
scores are defined.
ISO/IEC 19794-5, Information technology — Biometric data interchange formats — Part 5: Face image data,
already gives some specifications that are related to
⎯ scene constraints of the facial images,
⎯ photographic properties of the facial images, and
⎯ digital image attributes of the facial images.
Within this part of ISO/IEC 29794, a sample of a classification scheme of facial quality is exemplified and
approaches for the determination of certain aspects of quality are introduced.

© ISO/IEC 2010 – All rights reserved v

TECHNICAL REPORT ISO/IEC TR 29794-5:2010(E)

Information technology — Biometric sample quality —
Part 5:
Face image data
1 Scope
For aspects of quality specific to facial images, this part of ISO/IEC 29794:
⎯ specifies terms and definitions that are useful in the specification, use and testing of face image quality
metrics;
⎯ defines the purpose, intent, and interpretation of face image quality scores.
Performance assessment of quality algorithms and standardization of quality algorithms are outside the scope
of this part of ISO/IEC 29794.
2 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 29794-1, Information technology — Biometric sample quality — Part 1: Framework
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 29794-1 and the following apply.
3.1
comparison score
numerical value (or set of values) resulting from a comparison
3.2
face quality assessment algorithm
algorithm that computes a quality score for a given face image sample
3.3
facial image
electronic image-based representation of the portrait of a person
4 Abbreviated terms
CCD Charge-coupled device
DCT Discrete Cosine Transform
© ISO/IEC 2010 – All rights reserved 1

GCF Global Contrast Factor
FQAA Face Quality Assessment Algorithm
QS Quality Score
FQS Face Quality Score
QSN Quality Score Normalization
5 Approaches to Face Image Quality
Face Image Quality can be defined in many ways, depending on the application. For the purpose of this part
of ISO/IEC 29794 standard Face Image Quality is defined in relation to the use of facial images with
automated face recognition systems. The performance of an automated face recognition system is affected by
the amount of defect or the degree of imperfection present in the face image. The knowledge of quality can,
and is currently being used to, process face images differently, by either invoking some image enhancement
or normalization methods prior to feature extraction, invoking different matchers based on quality, or simply
changing the threshold. The use of face image quality assessment to enhance the overall performance of the
system is increasing [3, 4, 5].
A very important application of real-time quality analysis of faces is Face Recognition in Video, also referred to
as Face in a Crowd, Recognition on the move, or Face at a Distance, e.g [21].
This part of ISO/IEC 29794 shows some approaches for estimating Face Image Quality. The aim is to give the
reader examples of assessment algorithms. Note, that these algorithms have pros and cons and no one
algorithm is likely to be suitable for all facial images. Standardization of these algorithms is out of scope of this
part of ISO/IEC 29794.
The following related work is being done in ISO/IEC JTC1 SC37 [1, 2]:
• ISO/IEC 29794-1 suggests the use of Quality Algorithm Identification (QAID), or Quality Score Percentile
Rank upon standardization of a Quality Score Normalization Dataset (QSND).
This part of ISO/IEC 29794 adopts the following approach for face sample quality description:
• Specifying characterization of the facial quality and possible defects of face biometric samples in
categorized aspects.
• Showing how FQAAs can be used to derive face quality scores (FQSs) related to specific characteristics
and associated possible defects. An FQAA typically analyzes a face sample locally at the pixel or feature
level and fuses the local analysis results over a global region. An FQS evaluates one or more
characteristics and associated potential defects, and provides an indicator of the quality.
A typical approach of a system for generation of quality scores for facial images then takes the atomic FQSs
generated by the FQAAs and combines them to a final quality score. The final quality score must predict
performance metrics such as either false match or false non-match of an automatic facial image recognition.
6 Categorization of Facial Quality
Different factors affect the quality of the facial image with respect to biometric systems’ performance. A
successful recognition will be based on the biometric characteristics of the subject and a number of factors
that influence these characteristics such as variations (e.g. due to ageing) and the environmental conditions in
the acquisition process:
• Influence of subjects characteristics on biometric performance,
• Influence of the acquisition process (including the capturing device) on biometric performance.
2 © ISO/IEC 2010 – All rights reserved

This classification is not sufficient, as it does not distinguish between static and dynamic characteristics and
properties:
• static subjects characteristics are related to anatomical characteristics of the subject,
• dynamic subj
...