ISO/IEC 29794-4:2017
(Main)Information technology — Biometric sample quality — Part 4: Finger image data
Information technology — Biometric sample quality — Part 4: Finger image data
ISO/IEC 29794-4:2017 establishes - terms and definitions for quantifying finger image quality, - methods used to quantify the quality of finger images, and - standardized encoding of finger image quality, for finger images at 196,85 px/cm spatial sampling rate scanned or captured using optical sensors with capture dimension (width, height) of at least 1,27 cm × 1,651 cm.
Technologies de l'information — Qualité d'échantillon biométrique — Partie 4: Données d'image de doigt
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Standards Content (Sample)
INTERNATIONAL ISO/IEC
STANDARD 29794-4
First edition
2017-09
Information technology — Biometric
sample quality —
Part 4:
Finger image data
Technologies de l'information — Qualité d'échantillon biométrique —
Partie 4: Données d'image de doigt
Reference number
ISO/IEC 29794-4:2017(E)
©
ISO/IEC 2017
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ISO/IEC 29794-4:2017(E)
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ISO/IEC 29794-4:2017(E)
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions, symbols and abbreviated terms . 1
4 Conformance . 2
5 Finger image quality metrics . 2
5.1 Overview . 2
5.1.1 General. 2
5.1.2 Constituent of local quality metrics . 3
5.1.3 Constituent of global quality metrics . 3
5.1.4 Image preprocessing . 3
5.1.5 Image examples . 5
5.2 Normative contributive quality metrics . 5
5.2.1 General. 5
5.2.2 Orientation certainty level . 5
5.2.3 Local clarity score . 7
5.2.4 Frequency domain analysis (FDA) score .11
5.2.5 Ridge valley uniformity .13
5.2.6 Orientation flow .14
5.2.7 MU .15
5.2.8 MMB .15
5.2.9 Minutiae count in finger image .16
5.2.10 Minutiae count in center of mass region .16
5.2.11 Minutiae quality based on local image mean .16
5.2.12 Minutiae quality based on local orientation certainty level .17
5.2.13 Region of interest image mean .17
5.2.14 Region of interest orientation map coherence sum .19
5.2.15 Region of interest relative orientation map coherence sum .20
5.2.16 Quality feature vector composition .20
5.3 Non-normative quality metrics .23
5.3.1 General.23
5.3.2 Radial power spectrum .23
5.3.3 Gabor quality score .25
5.4 Unified quality score .27
5.4.1 Methodology for combining quality metrics .27
5.4.2 Training method .27
6 Finger image quality data record .28
6.1 Binary encoding .28
6.2 XML encoding .29
6.3 Quality algorithm identifiers .30
Annex A (normative) Conformance test assertions .32
Annex B (informative) Factors influencing fingerprint image character .44
Annex C (informative) Area consideration .46
Bibliography .47
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ISO/IEC 29794-4:2017(E)
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.
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 ISO documents 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).
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of
any patent rights identified during the development of the document will be in the Introduction and/or
on the ISO list of patent declarations received (see www.iso.org/patents).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation on 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 the following
URL: www.iso.org/iso/foreword.html.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 37, Biometrics.
This first edition cancels and replaces ISO/IEC/TR 29794-4:2010, which has been technically revised to
become an International Standard.
A list of all parts in the ISO 29794 series can be found on the ISO website.
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ISO/IEC 29794-4:2017(E)
Introduction
This document specifies finger image quality metrics. A reference implementation of the normative
metrics is available at https://github.com/usnistgov/NFIQ2.
The quality of finger image data is defined to be the degree to which the finger image data fulfils
specified requirements for the targeted application. Thus, the quality information is useful in many
applications. ISO/IEC 19784-1 allocates a quality field and specifies the allowable range for the scores,
with a recommendation that the score be divided into four categories with a qualitative interpretation
for each category. Image quality fields are also provided in the fingerprint data interchange formats
standardized in ISO/IEC 19794-2, ISO/IEC 19794-3, ISO/IEC 19794-4, and ISO/IEC 19794-8. This
document defines a standard way to calculate the finger image quality score that facilitates the
interpretation and interchange of the finger image quality scores.
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INTERNATIONAL STANDARD ISO/IEC 29794-4:2017(E)
Information technology — Biometric sample quality —
Part 4:
Finger image data
1 Scope
This document establishes
— terms and definitions for quantifying finger image quality,
— methods used to quantify the quality of finger images, and
— standardized encoding of finger image quality,
for finger images at 196,85 px/cm spatial sampling rate scanned or captured using optical sensors with
capture dimension (width, height) of at least 1,27 cm × 1,651 cm.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements 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 2382-37, Information technology — Vocabulary — Part 37: Biometrics
ISO/IEC 19794-1:2011, Information technology — Biometric data interchange formats — Part 1:
Framework
ISO/IEC 29794-1, Information technology — Biometric sample quality — Part 1: Framework
3 Terms, definitions, symbols and abbreviated terms
3.1 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/IEC 2382-37, ISO/IEC 29794-
1 and the following apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— IEC Electropedia: available at http://www.electropedia.org/
— ISO Online browsing platform: available at http://www.iso.org/obp
3.1.1
foreground region
set of all pixels of a finger image that form valid finger image patterns
Note 1 to entry: The most evident structural characteristic of a valid finger image is a pattern of interleaved
ridges and valleys.
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ISO/IEC 29794-4:2017(E)
3.1.2
local region
block of m × n pixels of the foreground of a finger image, where m and n are smaller than or equal to the
width and the height of the finger image
3.1.3
finger image quality assessment algorithm
algorithm that reports a quality score for a given finger image
3.1.4
metric
quantification of a covariate using a prescribed method
3.1.5
covariate
variable or parameter that either directly, or when interacting with other covariates, affects fingerprint
recognition accuracy
3.2 Symbols and abbreviated terms
DFT Discrete Fourier Transform
I matrix of grey-level intensity values corresponding to the pixels of an image
S ridge valley signature of a local region V
V matrix of grey-level intensity values corresponding to the pixels of a local region
4 Conformance
A finger image quality assessment algorithm conforms to this document if it conforms to the normative
requirements of Clause 5.
A finger image quality record shall conform to this document if its structure and data values conform
to the formatting requirements of Clause 6 (finger image quality data record) and its quality values are
computed using the methods specified in 5.2, 5.3 and 5.4.
Conformance to normative requirements of Clause 6 fulfils Level 1 and Level 2 conformance as specified
in ISO/IEC 19794-1:2011, Annex A. Conformance to normative requirements of 5.2 and 5.4 is Level 3
conformance as specified in ISO/IEC 19794-1:2011, Annex A.
5 Finger image quality metrics
5.1 Overview
5.1.1 General
Clause 5 establishes metrics for predicting the utility of a finger image (5.2 and 5.3). Image quality
metrics from a single image are useful to ensure the acquired image is suitable for recognition.
A complete finger image quality analysis shall examine both the local and global structures of the
finger image. Fingerprint local structure constitutes the main texture-like pattern of ridges and valleys
within a local region while valid global structure puts the ridges and valleys into a smooth flow for the
entire fingerprint. The quality of a finger image is determined by both its local and global structures.
Clause 5 describes the features and characteristics of finger images at both local and global structures
that are to be used for quantifying finger image quality.
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ISO/IEC 29794-4:2017(E)
For applying the algorithms as described in 5.2 and 5.3, the finger image shall have a spatial sampling
rate of 196,85 pixels per centimetre (500 pixels per inch).
5.1.2 Constituent of local quality metrics
A finger image is partitioned into local regions such that each local region contains sufficient ridge-valley
information, preferably having at least 2 clear ridges, while not overly constraining the high curvature
ridges. For images with a spatial sampling rate of 196,85 pixel per centimetre (500 pixel per inch), the
[1]
ridge separation usually varies between 8 pixels to 12 pixels . A ridge separation comprises a ridge
and a valley. In order to cover two clear ridges, the local region size has to be greater than 24 pixels in
both width and height. The size for each local region shall be 32 × 32 pixels, which is sufficient to cover
2 clear ridges. Instead of Cartesian coordinate, curvilinear coordinate along the ridge can also be used.
5.1.3 Constituent of global quality metrics
A global quality metric should be computed over the whole image and assess the utility of fingerprint
characteristics in the image.
5.1.4 Image preprocessing
5.1.4.1 Description
A segmentation process follows where each local region is labelled as background or foreground.
There are several segmentation approaches, such as using the average magnitude of the pixel-intensity
[1]
gradient in each local region .
This document does not prescribe segmentation methods, but notes that performing segmentation
influences the computed scores. Constant or near constant areas of the input image shall be removed
according to 5.1.4.2 prior to computing quality using the metrics specified in 5.2 and 5.3.
5.1.4.2 Removal of near constant white lines in image
Prior to computing features, fingerprint images are cropped to remove white pixels on the margins.
Starting from the outer margins, rows and columns with average pixel intensity above 250 are removed.
Pixel intensities take values [0, 255] for an 8-bit gray scale image. As a first approximation of the region
of interest, image columns and rows which are near constant white background are removed. Using the
algorithm specified below, a fixed threshold is set for gray scale pixel intensity of T = 250 to obtain the
μ
image without near constant areas.
The algorithm is specified as:
a) For each row R in I, starting from the top
i
1) Compute the row arithmetic mean μ
row
2) On the first occurrence where μ ≤T set idxi=
row μ t
3) On the last occurrence where μ ≤T set idxi=
row μ b
b) For each column C in I, starting from the left
i
1) Compute the column arithmetic mean μ
col
2) On the first occurrence where μ ≤T set idxi=
col μ l
3) On the last occurrence where μ ≤T set idxi=
col μ r
c) Extract the region of interest as Î = I.roi(idx , idx , idx , idx )
l t r b
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5.1.4.3 Foreground segmentation based on local standard deviation
For quality features which require a foreground mask to indicate regions containing the fingerprint an
algorithm using local standard deviation is adopted.
The algorithm is specified as:
a) Normalize I to zero mean and unit standard deviation to produce Î
b) For each local region V in Î
1) Compute the standard deviation of V as σ
V
2) Mark the corresponding local region in I as foreground if σ > 0,1
mask V
5.1.4.4 Computing the dominant ridge flow orientation for a local region from pixel-intensity
gradients
The dominant ridge flow orientation is determined by computing the pixel-intensity gradient
information and then determining the orientation of the principal variation axis.
The numerical gradient of the local region is determined using finite central difference for all interior
pixels in x-direction and y-direction
IIxy+11,,−−xy
() ()
f = (1)
x
2
IIxy,,+11−−xy
() ()
f = (2)
y
2
With f and f , the dominant ridge flow orientation, angle (V), is determined analytically using the
x y
sine and cosine doubled angle determined from the arithmetic means of the pixel-intensity gradient
covariances.
2
a= f (3)
x
2
b= f (4)
y
c= ff (5)
xy
ac
C = (6)
cb
2
2
dc=+ ab− + (7)
()
c
sinθ = (8)
d
ab−
cosθ = (9)
d
1 sinθ
−1
angle V = tan (10)
()
2 cosθ
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5.1.5 Image examples
For algorithms operating in a block-wise manner the input image is subdivided into local regions
according to the overlay grid shown in Figure 1 b). The local region V(8,5) is used as example in local
processing and is marked up using a bold line. Figure 1 c) shows an enlarged view of V(8,5) and Figure 1
d) shows V(8,5) rotated according to its dominant ridge orientation computed using Formula (10).
a) Input finger image b) division into local c) enlarged view of d) V(8,5) rotated
regions V(8,5) according to its
dominant ridge
orientation as
determined using
Formula (10)
Figure 1 — Input image used — Examples of the processing of quality
5.2 Normative contributive quality metrics
5.2.1 General
5.2 specifies normative contributive finger image quality assessment algorithms.
5.2.2 Orientation certainty level
5.2.2.1 Description
[3]
The orientation certainty level (OCL) of a local region is a measure of the consistency of the
orientations of the ridges and valleys contained within this local region. The feature computes local
quality and operates in a block-wise manner.
The finger image within a 32 × 32 pixels local region [as shown in Figure 1 c)] generally consists of
dark ridge lines separated by white valley lines along the same orientation. The consistent ridge
orientation and the appropriate ridge and valley structure are distinguishable local characteristics of
the fingerprint local region.
The pixel-intensity gradient (dx, dy) at a pixel describes the direction of the maximum pixel-intensity
change and its strength. By performing Principal Component Analysis on the pixel-intensity gradients
in a local region, an orthogonal basis for the local region can be formed by finding its eigenvalues and
eigenvectors. The resultant first principal component contains the largest variance contributed by
the maximum total gradient change in the direction orthogonal to ridge orientation. The direction is
given by the first eigenvector and the value of the variance corresponds to the first eigenvalue, λ .
max
On the other hand, the resultant second principal component has the minimum change of gradient in
the direction of ridge flow which corresponds to the second eigenvalue, λ . The ratio between the
min
two eigenvalues thus gives an indication of how strong the energy is concentrated along the dominant
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ISO/IEC 29794-4:2017(E)
direction with two vectors pointing to the normal and tangential direction of the average ridge flow
respectively.
5.2.2.2 Computing the eigenvalues and local orientation certainty
From the covariance matrix C [Formula (6)] the eigenvalues λ and λ are computed as
min max
2
2
ab+− ab− +4c
()
λ = (11)
min
2
2
2
ab++ ab− +4c
()
λ = (12)
max
2
which yields a local orientation certainty level
λ
min
10−>, if λ
max
local
λ
Q = (13)
max
OCL
0, otherwise
which is a ratio in the interval [0,1] where 1 is highest certainty level and 0 is lowest.
NOTE The orientation certainty level fails to predict match-ability when some marks or residual exist in the
samples that have strong orientation strength, such as those exhibited by latent prints left by the previous user.
5.2.2.3 OCL algorithm
For each local region V in I:
a) compute the pixel-intensity gradient of V with centered differences method [Formulae (1), (2)];
b) compute the covariance matrix C [Formula (6)];
local
c) compute the eigenvalues of C to obtain Q [Formulae (11), (12), (13)].
OCL
Figure 2 visualizes the processing steps.
a) Current local region with the ratio between local
b) Local quality scores Q for example
OCL
eigenvalues marked as ellipse
fingerprint image
Figure 2 — Processing steps of orientation certainty level quality algorithm
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ISO/IEC 29794-4:2017(E)
5.2.3 Local clarity score
5.2.3.1 Description
Good quality fingerprints exhibit clear ridge-valley structure. Thus, the local clarity score (LCS)
[4]
, which is the measure of the ridge-valley structure clarity, is a useful indicator of the quality of a
fingerprint. The feature computes local quality and operates in a block-wise manner.
To perform ridge-valley structure analysis, the foreground of the finger image is quantised into local
[3]
regions of size 32 × 32 pixels . Inside each local region, an orientation line, which is perpendicular
to the ridge direction, is computed. At the centre of the local region along the ridge direction, a local
region of size 32 × 16 pixels shall be extracted and transformed to a vertically aligned local region.
On S, the local region average profile, calculated in 5.2.3.4, a linear regression (or least square fitting) is
applied to determine the Determine Threshold (DT) which is a line positioned at the centre of the local
region V, and is used to segment the local region into the ridge or valley region. Regions with grey level
intensity lower than DT are classified as ridges. Otherwise, they are classified as valleys.
Since good finger images cannot have ridges that are too close or too far apart, the nominal ridge and
valley thickness can be used as a measure of the quality of the finger image captured. Similarly, ridges
that are unreasonably thick or thin indicate that the finger image may not be captured properly, such
as pressing too hard or too soft, or the image is a residual sample. Thus, the finger image quality can
be determined by comparing the ridge and valley thickness to each of their nominal range of values.
Any value out of the nominal range may imply a bad quality ridge pattern. To normalize the range of
the thickness values, a pre-set maximum thickness is used. The maximum ridge or valley thickness
(W ) for a good finger image is estimated at 20 pixels for a 196,85 pixel per centimetre (500 pixel
max
per inch). The pre-set value of 20 pixel for a 196,85 pixel per centimetre (500 pixel per inch) scanner
[1]
spatial sampling rate is obtained from the median of the typical ridge separation of 8 to 12 pixels ,
and assuming that any ridge separation will not exceed twice of the median value. This will ensure that
the pre-set value is indeed the maximum to limit the value of the normalized ridge and valley thickness
between 0 and 1. The ridge thickness (W ) and valley thickness (W ) are then normalized with respect
r v
to the maximum thickness.
With the ridge and valley separated as above, a clarity test can be performed in each segmented
rectangular 2-D region.
For local regions with good clarity, the pixel-intensity distribution of ridges and the pixel-intensity
local
distribution of the valleys have a very small overlapping area and thus Q is high. The following
LCS
factors affect the size of the total overlapping area:
a) noise on ridge and valley;
b) water patches on the image due to wet fingers;
c) incorrect orientation angle due to the effect of directional noise;
d) scar across the ridge pattern;
e) highly curved ridges;
f) ridge endings, bifurcations, delta and core points;
g) incipient ridges, sweat pores and dots.
Factors a) to c) are physical noise found in the image. Factors d) to g) are actual physical characteristics
of the fingerprint.
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ISO/IEC 29794-4:2017(E)
5.2.3.2 Computing the ridge valley signature of a local region
Given the local region V the ridge valley signature S is obtained by
16
V xy,
()
∑
y=1
S x = (14)
()
16
where V(x, y) is the grey level at point (x, y); x is the index along x-axis.
5.2.3.3 Determining the proportion of misclassified pixels
Formulae (15) and (16) specify the calculation of α and which are the proportion of pixels misclassified
respectively as valley or ridge. v is the number of pixels in valley region with intensity lower than DT
B
and v is the total number of pixels in valley region. r is the number of pixels in the ridge region with
T B
intensity higher than DT and r is the total number of pixels in the ridge region.
T
v
B
α = (15)
v
T
r
B
β = (16)
r
T
5.2.3.4 Determining the normalized ridge and valley width
The normalized valley width W and the normalized ridge width W are determined
v r
W
v
W = (17)
v
S
max
W
125
W
r
W =
...
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