Generation of Privacy-friendly Datasets of Latent Fingerprint Images
using Generative Adversarial Networks
Stefan Seidlitz, Kris Jürgens, Andrey Makrushin, Christian Kraetzer and Jana Dittmann
Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, Germany
Keywords: Digitized Forensics, Latent Fingerprint, Image Synthesis, Generative Adversarial Networks, GAN, Privacy.
Abstract: The restrictions posed by the recent trans-border regulations to the usage of biometric data force researchers
in the fields of digitized forensics and biometrics to use synthetic data for development and evaluation of new
algorithms. For digitized forensics, we introduce a technique for conversion of privacy-sensitive datasets of
real latent fingerprints to "privacy-friendly" datasets of synthesized fingerprints. Privacy-friendly means in
our context that the generated fingerprint images cannot be linked to a particular person who provided
fingerprints to the original dataset. In contrast to the standard fingerprint generation approach that makes use
of mathematical modeling for drawing ridge-line patterns, we propose applying a data-driven approach
making use of generative adversarial neural networks (GAN). In our synthesis experiments the performance
of three established GAN architectures is examined. The NIST Special Database 27 is exemplary used as a
data source of real latent fingerprints. The set of training images is augmented by applying filters from the
StirTrace benchmarking tool. The suitability of the generated fingerprint images is checked with the NIST
fingerprint image quality tool (NFIQ2). The unlinkability to any original fingerprint is established by
evaluating outcomes of the NIST fingerprint matching tool.
Fingerprints are known to be directly linked to an
individual and fingerprint as biometric modality is
well accepted and widely established means of user
authentication. The applications making use of
fingerprints spread form biometric access control
systems to forensic investigation of latent
fingerprints. Empirical studies on forensic or
biometric fingerprint processing and recognition
require a large dataset of fingerprints for validation of
results. Moreover, development of fingerprint
detection and recognition algorithms based on
machine learning is hardly possible without an
abundant amount of training data. However,
fingerprints as well as any other biometric data are
seen as a special category of personal data which is
prohibited to be processed by the recent trans-border
regulations without exception for the purpose of non-
commercial research. A prominent example of a
fingerprint dataset valuable for digitized forensics
which was removed from the public access after
establishing such regulations is the NIST Special
Database 27 (Garris et al., 2000). Note that some
categories of personal data can be processed after
anonymization which is not the case for biometric
samples because they require no meta-data to be
linked to individuals. An elegant solution to the
privacy-caused processing restrictions is a genera-
tion of artificial fingerprints that have the same
characteristics as real fingerprints, but cannot be
linked to particular persons. For biometrics, there is a
de facto standard synthesizing tool called SFinGe
(Cappelli, 2009). Fingerprints are generated to fit a
certain basic pattern and a predefined set of minutitae.
In contrast, in the field of digitized forensics the focus
is on mimicking substrate and environmental
influences on a digitalized latent fingerprint. Since
such influences can be hardly formalized, modern
data-driven image generation approaches has to be
adopted for fingerprints.
Recent achievements in development of deep
convolutional neural networks and especially gene-
rative adversarial networks (GAN) allow for
automated generation of artificial images that can be
hardly told apart from real images. In this paper, we
examine several GAN architectures in application to
generation of fingerprint images and assess the
quality of the generated images. The quality is two-
fold. The generated fingerprints must appear natural
Seidlitz, S., Jürgens, K., Makrushin, A., Kraetzer, C. and Dittmann, J.
Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks.
DOI: 10.5220/0010251603450352
In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP, pages
ISBN: 978-989-758-488-6
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
and be privacy-friendly. Natural appearance means
that the naked eye cannot see difference to a typical
original pattern and a fingerprint is applicable for the
further investigations (appropriate basic pattern,
sufficient number of minutiae, etc). Privacy-friendly
means that a generated fingerprint does not match one
particular original fingerprint. If a generated
fingerprint matches k (k 1) original instances, then
k-anonymity will be indicated. As a metric for both
criteria we use NIST tools (Ko, 2007): NFIQ2 for the
former and the combination of the MINDTCT and
Bozorth3 for the latter.
Our contribution is in demonstrating that GAN
can be successfully applied for conversion of privacy-
sensitive datasets of fingerprint images to privacy-
friendly datasets and in comparing three currently
very prominent GAN architectures:
ProgressiveGAN, StyleGAN and StyleGAN2 for this
purpose. While comparing generation perfor-mances
of the networks, we consider the following
characteristics: how many training iterations are
required to obtain high-quality fingerprint images, the
time of image generation, and the proportion of high-
quality fingerprints in the whole number of generated
fingerprints. The better network would be able to
generate more high-quality fingerprints in a shorter
time frame. In order to improve the diversity of
generated images and make the process of image
generation more stable, we augment the training set
by applying filters from the StirTrace benchmarking
tool (Hildebrandt et al., 2015). Note that here we are
not focused on generation of one particular dataset of
artificial fingerprints, but rather propose a technique
for compilation of such privacy-friendly datasets out
of existing data.
In Section 2, we overview related works. In
Section 3, we introduce our concept of generation and
assessment of synthetic fingerprint images. In Section
4, we elaborate on important aspects of our
implementation. In Section 5, we evaluate the GAN
generated fingerprint images. Section 6 concludes the
paper with the summary of results.
Early works on generation of synthetic fingerprint
images were concerned rather with reconstruction of
ridge-line patterns from biometric templates
considering this act as a potential attack on a
biometric system (Galbally et al., 2008). Starting
from the set of minutiae, a fingerprint area, an
orientation map and a frequency map are estimated.
Then, the iterative pattern growing approach draws
ridges along the orientation lines by applying Gabor
filters (Cappelli et al., 2007). This approach has been
implemented in the software called SFinGe (Cappelli,
2009). The most critical step in this workflow is the
estimation of an orientation map based on the set of
minutiae or, to be more precise, based on singular
points of a basic pattern (core, deltas). It is shown in
(Ram et al., 2010) that singular points can be modeled
by the zero-poles of Legendre polynomials, resulting
in a discontinuous orientation field. An exhaustive
study on the possibility of modeling fingerprints by
the phase portraits of differential equations is
conducted in (Zinoun, 2018) and the limitations are
In contrast to mathematical modeling, the modern
trend is a data-driven generation of realistic images
by means of generative adversarial networks (GAN).
Recently, very impressive results have been
demonstrated with images of human faces. Based on
a huge amount of face images, researchers from
NVIDIA successfully created high-quality, high-
resolution synthetic faces, which can be hardly told
apart from the real ones (Karras et al., 2018). The first
effort to synthesize fingerprints using a Wasserstein
GAN is made in (Bontrager et al, 2017) aiming at
generating so-called master fingerprints that match
multiple original fingerprints. Later on, in (Minaee et
al., 2018), a connectivity imposed GAN is introduced
and applied to two datasets: FVC-2006 and PolyU.
The size of generated images in both publications is
rather insufficient. In (Attia et al, 2019) fingerprints
are synthesized by a variational autoencoder. In (Cao
et al., 2018), a combination of an autoencoder and an
adapted Wasserstein GAN is used for synthesizing
512x512 pixel fingerprint images. A CycleGAN is
applied in (Wyzykowski et al., 2020) to transfer
texture from real fingerprints to conventionally
synthesized ridge-line patterns with added sweet
pores which dramatically improves their realistic
appearance. An alternative approach for generating
high-resolution realistic fingerprints is in combining
GAN with a super-resolution network proposed in
(Riazi et al. 2020). In (Fahim, et al., 2020), a
lightweight GAN is proposed for creating 128x128
pixel images and compared with five established
GAN architectures based on 64x64 pixel patches. The
next breakthrough is done in (Mistry et al., 2020) by
incorporating identity information into the fingerprint
synthesis network which is based once again on
combining auto-encoder and Wasserstein GAN.
Here, we look for a suitable GAN architecture to
generate high-resolution (512x512 pixel) gray-scale
fingerprint images. This target size corresponds to the
high-quality fingerprint image format (1000 ppi) used
VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications
in dactyloscopic analyses (Orandi et al., 2014).
Images with this size and resolution are capable of
representing a significant part of a scanned latent
fingerprint incl. the level 1 feature (basic pattern) as
well as a sufficient number of level 2 features
(minutiae) and potentially, depending on the prints
quality, also level 3 features (sweat pores). The vast
majority of dated GAN architectures are designed to
generate images with a resolution less or equal to
256x256 pixels (see e.g. (Karras et al., 2017)) and
therefore omitted in our considerations. Generating
images of higher resolution is possible by adding
several up-sampling layers (Zhang et al., 2016), but it
would increase instability of the training process.
Some GAN architectures such as ConditionalGAN
(Wang et al., 2018) suffice the resolution criterion but
are not capable of generating plausible images from
fully random latent vectors. Such GANs are also not
addressed. To the best of our knowledge, currently
only three GAN architectures fit to our requirements:
ProgressiveGAN (Karras et al., 2017), StyleGAN
(Karras et al., 2018) and StyleGAN2 (Karras et al.,
2019). These networks are able to generate plausible
high-resolution images from fully random latent
The usage of GAN is driven by privacy concerns. The
synthesized fingerprints in a privacy-friendly dataset
should exactly reproduce the characteristics of a
reference dataset, but must leak no information in
terms of reproducing the same minutiae.
The classic fingerprint synthesizing approaches
start with minutiae or singular points to generate
ridge-lines. If a set of minutia is taken from a
reference fingerprint, the synthesized fingerprint
would perfectly match it. Random selection of
singular points or of a set of minutiae may lead to
generation of implausible ridge-line patterns. Hence,
tools like SFinGe can be successfully applied for
generation of fingerprints with parameterizable
characteristics reaching the diversity by randomizing
parameters. However, SFinGe is hardly applicable to
mimic characteristics that are presented in some
reference data but cannot be formally described such
as an appearance of a substrate. This is often a case
for latent fingerprints which include not only
characteristic of the fingerprint itself, but also
characteristics of the environment in which the
fingerprints were left behind (including topological
characteristics of the surface). Table 1 summarizes
the differences between the both aforementioned
concepts of synthesizing fingerprint images.
Table 1: Comparison of SFinGe and GAN for generation of
fingerprint images.
Fingerprint type exemplar latent
Reproduction of characteristics of
a fingerprint
(basic pattern,
characteristics of
(incl. substrate,
process etc.)
Our concept for the generation of privacy-friendly
fingerprint image datasets is illustrated in Figures 1
and 2. The basic idea is that, for a given proprietary
dataset of fingerprint images, we create a dataset of
anonymous fingerprint images that are not linked to
individuals but preserve all characteristics of the
images in the initial dataset incl. background noise,
image quality, frequency of ridge lines, one of the
standard basic patterns, plausible number and
locations of minutiae, etc. The number of samples in
a new dataset is optional and depends only on time
spent to the generation process. Note that images in
the original dataset may include more than one
fingerprint and may also vary in size. In contrast, the
GAN training images must all have a specific size.
Hence, a dataset specific pre-processing of images is
Figure 1: Training of a GAN model.
Figure 1 schematically describes training of a
GAN model. In our further considerations, we take
NIST Special Database (SD) 27 (Garris, 2000) with
2856 latent and matching tenprint fingerprint images
as an example source of fingerprint data. There are
two pre-processing steps performed before images
are fed into the network: First, the fingerprints are
segmented from the images. The patches are cut
around fingerprint core points so that each resulting
image contains exactly one partial or full fingerprint.
The core points are located using the NIST tool
MINDTCT (Ko, 2010). Note that the MINDTCT
sometimes falsely highlight some artifacts like letters
1000 dpi
(2856 images)
FP patches,
1000 dpi,
512×512 pixels
(39768 samples)
GAN Model
FP patches
1000 dpi,
512×512 pixels
(516984 samples)
Segmentation Augmentation
of GAN
1000 dpi
(2856 images)
FP patches,
1000 dpi,
512×512 pixels
(39768 samples)
GAN Model
FP patches
1000 dpi,
512×512 pixels
(516984 samples)
Segmentation Augmentation
of GAN
Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks
on the scanned trace cards in NIST SD 27 images as
core points leading to non-fingerprint patches.
However, we decided not to remove these outliers
from the training data because they should be
automatically sorted out in the later steps of the
generation process. The number of extracted
fingerprint patches is 39768. Second, the resulting set
of fingerprint patches is augmented by their filtered
versions (see Section 3.1) increasing the number from
39768 to 516984 for 12 StirTrace filters applied. This
is done to improve the diversity of the generated
images and to make the process of image generation
more stable.
Figure 2: Generation of synthetic fingerprint images.
Figure 2 demonstrates the generation process of
fingerprints and assessment of their quality. During
GAN training, random latent vectors are fed into the
generator resulting in synthetic fingerprint images.
The snapshots of generated fingerprints are automa-
tically stored at some training iterations. From each
snapshot, we randomly pick 1000 fingerprint images
for further analysis. For every image in every
snapshot, we obtain a quality score using the NIST
Fingerprint Image Quality estimator - NFIQ2 (Elham
et al., 2013). From each snapshot, we calculate the
mean value of the NFIQ2 scores and the number of
images with NFIQ2 scores higher than 35 to
determine at which iteration of the training process
the generation model works best. Images with such
scores correspond to the two highest quality classes
in the 5-class NFIQ scale (Galbally et al., 2019) and,
therefore, are referred to as high-quality fingerprints.
The best snapshot has the highest number of high-
quality 512x512 pixel fingerprints. Fingerprints with
NFIQ2 scores lower or equal to 35 are filtered out.
Each remaining fingerprint is biometrically matched
to all training fingerprints using NIST tools (see
Section 3.3).
3.1 Data Augmentation with StirTrace
StirTrace (Hildebrandt et al., 2015) is designed for
benchmarking pattern recognition tasks in the context
of digitized forensics. This tool can be seen as a set of
filters to mimic typical artifacts that usually arise in
the process of digitizing fingerprints, especially
addition of noise. The most relevant filters used here
for data augmentation are additive noise (strengths: 3,
5 and 9), additive Gaussian noise (strengths: 3, 5 and
9), median cut (strengths 3, 5 and 9) and salt and
pepper noise (strengths: 3, 5 and 9). Note that filtering
does not change the number and location of minutiae
in a fingerprint. Hence, it is sufficient that the
generated synthetic fingerprints are matched only
against original patches and not against all training
3.2 GAN Architectures
The term Generative Adversarial Network (GAN)
was proposed in 2014 by Ian Godfellow for the
architecture containing two neural networks
Generator (G) and Discriminator (D), which are
trained interchangeably. Generator produces synthe-
tic data from random vectors and discriminator tries
to distinguish these data from genuine data. The basic
idea is that the generator improves with training while
the discriminator’s performance gets worse.
However, after the point where the discrimi-nator is
unable to tell apart genuine and synthesized data, the
generator cannot be improved further, making the
training process rather unstable. When operating with
images, D is represented by a convolutional neural
network and G by a de-convolutional neural network.
Progressive growing GAN (ProgressiveGAN) is
an elegant solution to the convergence issue of the
GAN training (Karras et al., 2017). Training begins
with low-resolution images e.g. 4x4 pixels and the
input images are re-scaled to this resolution. After the
training process converges at the selected resolution,
the resolution is increased and the training is repeated.
This is done until the target resolution is reached. This
is how the generator learns rough characteristics of
training images first and then gradually fine
characteristics. Technically, switching of resolution
happens by gradual addition of intermediate layers
into the network. Progressive-GAN was the first
architecture that enabled gene-ration of high-
(FP patches,
1000 dpi)
Avg. NFIQ2 scores
for snapshots
Snapshot X
(FP patches,
1000 dpi)
FP patches,
1000 dpi
(39768 samples)
Snapshot X
(FP patches,
1000 dpi)
FP patches,
500 dpi
(39768 samples)
Snapshot X
(FP patches,
500 dpi)
of NFIQ2
(NFIQ2 > 35)
(39768 samples)
(1000 samples)
of Bozorth3
anonymous FP,
1000 dpi
(No matches)
(FP patches,
1000 dpi)
Avg. NFIQ2 scores
for snapshots
Snapshot X
(FP patches,
1000 dpi)
FP patches,
1000 dpi
(39768 samples)
Snapshot X
(FP patches,
1000 dpi)
FP patches,
500 dpi
(39768 samples)
Snapshot X
(FP patches,
500 dpi)
of NFIQ2
(NFIQ2 > 35)
(39768 samples)
(1000 samples)
of Bozorth3
anonymous FP,
1000 dpi
(No matches)
VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications
resolution naturally looking fake faces which can be
hardly told apart from real ones.
Based on ProgressiveGAN, an improved archite-
cture called StyleGAN (Karras et al., 2018) was
developed to take apart aggregated characteristics of
images also referred to as styles and to move from one
style to another. For face images the styles are e.g. a
haircut or a skin color. Technically, the analysis and
clustering of the latent space is done by introduction
of adaptive instance normalization (AdaIN) layers
and addition of noises to control the intensity of a
particular style. However, StyleGAN often produces
characteristic imperfection e.g. droplet or phase
artifacts which are attributed to the network
architecture. Droplet artifacts arise due to
independent normalization of means and variations of
different style feature maps in AdaIN layers and
phase artifacts arise due to progressive growing. A
variation of the StyleGAN architecture called Style-
GAN2 (Karras et al., 2019) was proposed to avoid the
aforementioned imperfections. Adaptive instance
normalization is replaced by weight demodulation
making normalization of means and variations of the
different styles not independent anymore. The
progressive growing issue is solved by generating
images with only target resolution by adding up the
weighted outcomes of all layers of the generator. It
does not change the idea of progressive learning of
image characteristics (from rough to fine), but helps
to get rid of phase artifacts.
3.3 Fingerprint Anonymity Assessment
Biometric matching of fingerprints is done by using
NIST tools: MINDTCT and Bozorth3 (Ko, 2007).
MINDTCT extracts the list of minutia from a finger-
print while Bozorth3 compares two such lists and
produces a similarity score representing the number
of matched minutiae. Note that MINDTCT requires
500 dpi images to properly detect minutiae. Since the
target resolution of our synthesized fingerprints is
1000 dpi, the images are downscaled with a factor 2
before applying MINDTCT. The documentation of
Bozorth3 suggests values over 40 for the perfect
match. However, we use here a value of 30 as a
decision threshold to guarantee better anonymity.
We use the original NVIDIA implementations of the
addressed GAN architectures from GitHub reposito-
ries: and http:// The implementation
of ProgressiveGAN is a part of the StyleGAN repo-
sitory. The GANs are used in their default configu-
ration, we only set the target image size to 512x512
pixel and kImages to 7500. The parameter kImages
refers to the amount of real images which the GAN
discriminator has seen during training. We switch off
the estimation of the perceptual path length and linear
separability of all GANs because these metrics do not
work with gray scale images (for reasons see (Karras
et al., 2018)). The amounts of layers used and the
trainable parameters of each GAN are summarized in
Table 2. For the training of the GANs we used a
workstation with two NVIDIA Titan RTX graphic
cards with 24 GB VRAM each.
The StirTrace tool is used in version 4 as provided
at The
applied filters as well as the composition of the
training set after this data augmentation step are
summarized in Table 3.
The NFIQ2, MINDTCT and Bozorth3 are the
parts of the NIST Biometric Image Software (NBIS)
which is available at
Table 2: Configurations of the addressed GANs.
GAN type network type amount
of layers
StyleGAN Generator
StyleGAN2 Generator
Table 3: Composition of the training dataset.
Filter Kernel size Number of samples
without filter - 39768
+ additive noise 3, 5, 9 3 x 39768 = 119304
+ additive Gaussian
3, 5, 9 3 x 39768 = 119304
+ median cut 3, 5, 9 3 x 39768 = 119304
Salt & pepper noise 3, 5, 9 3 x 39768 = 119304
Total: 516984
We compare GAN architectures regarding the
following aspects: how many training iterations are
required to obtain high-quality fingerprint images, the
speed of image generation, and the proportion of
Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks
high-quality fingerprints in the whole number of
generated fingerprints. The better network is able to
generate more high-quality fingerprint images in a
shorter time frame. Additionally, we validate the
“anonymity” of the created datasets.
5.1 Training and Generation Time
The training time strongly varies from one GAN to
another. Due to the time constraints, we initially
limited the training parameter kImages to 7500.
Nonetheless, only StyleGAN reached the image size
of 512x512 pixels with 7500 kImages on one GPU.
The training of ProgressiveGAN with 7500 kImages
resulted in images of 256x256 pixels. Hence, the
training was continued with 12000 kImages on two
GPUs. For the proper comparison the 256x256
images are upscaled to 512x512 pixels. The training
time of StyleGAN2 is extremely long (see Table 4),
so that we first switched from one GPU to two GPUs
after 3429 kImages and stopped training of after 4452
kImages (ca. 11 days). Nevertheless, even at this
point, the generated fingerprint images already
reached a good subjective quality (to be attributed to
the residual structure of generator and discriminator).
Note that StyleGAN2 has an advantage over the two
other architectures because training already starts
with 512x512 pixel images while the other two start
with very small images und gradually upscale them.
Table 4: Training time of the GANs on our reference PC.
GAN type Used
kImages Training
image size
1 < 7500 2d 1h 44m 256x256
2 7501-
4d 22h 57m 512x512
StyleGAN 1 < 7500 3d 3h 36m 512x512
StyleGAN2 1 < 3429 8d 5h 12m 512x512
StyleGAN2 2 3430-
2d 20h 46m 512x512
5.2 Quality of Generated Images
Figures 3 and 4 demonstrate the development of the
average NFIQ2 score and the ratio of high-quality
images of 1000 images randomly selected from GAN
snapshots over the course of training, respectively.
We see that StyleGAN2 starts genera-ting high-
quality fingerprints after only few training iterations
and the fingerprint quality even degrades with the
higher kImages parameter. The diagrams also
demonstrate that StyleGAN clearly outperforms
ProgressiveGAN regarding both the average NFIQ2
score and the number of fingerprints with NFIQ2
scores higher than 35. For StyleGAN and Progres-
siveGAN, the snapshot at which the targeted
resolution of 512x512 pixels is reached is marked.
For all three GANs, the average NFIQ2 scores
stabilize between 25 and 30.
Figure 3: GAN training: average NFIQ2 scores.
Figure 4: GAN training: ratio of high-quality fingerprints.
Figure 5: Distributions of NFIQ2 scores of 1000 random
GAN fingerprint images from the selected snapshots.
We compare the performances of GANs by
comparing the snapshots at which GAN reaches the
highest average NFIQ2 score with the possibly low
kImages parameter. The optimal value of kImages is
7870, 6840 and 1925 for ProgressiveGAN, Style-
GAN and StyleGAN2 respectively. The distributions
of NFIQ2 scores for the selected snapshots are
depicted in Figure 5. The histograms shows that
VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications
fingerprint images with the highest quality are
generated by StyleGAN followed by Progressive
GAN and StyleGAN2. Out of 1000 images in a
corresponding snapshot, StyleGAN generated 371
images with NFIQ2 higher than 35, Progressive GAN
266 images and StyleGAN2 191 images. Note that all
GAN generated images have on average higher
NFIQ2 scores than original training images.
Table 5 visualizes the difference between GAN-
generated fingerprint images of high (NFIQ2 > 35)
and of relatively low quality (25 ≤ NFIQ2 < 35). The
perceptual quality of the fingerprint images is
extremely high, with plausible level 1 and level 2
features and, in the case of StyleGAN, even
something that already looks like some sweat pores.
Table 5: Examples of GAN-generated fingerprint images.
Progres.GAN StyleGAN StyleGAN2
> 35
39 43
< 35
26 26 25
5.3 Anonymity of Generated
After filtering out the low-quality fingerprint images
with NFIQ2 score below 35, we compare each high-
quality fingerprint image with all training images.
Low Bozorth3 scores indicate that the generated
fingerprints cannot be linked to any person who
provided fingerprints to the training dataset. The
average Bozorth3 scores range between two and six
depending on the fingerprint and the GAN type.
However, there are few generated fingerprints with
exactly one Bozorth3 score higher than 30 indicating
the match between a generated fingerprint and one of
the fingerprints in the training dataset. Table 6 shows
the ratio of synthesized fingerprints with Bozorth3
scores lower than 30, between 30 and 39, and higher
than 40 for each considered GAN type. For
StyleGAN for instance, 59 images have a score above
40 meaning 15.9% of high-quality non-anonymous
images in the selected snapshot.
Table 6: Number of GAN fingerprints with Bozorth3 scores
(s) in a certain range in the selected snapshots.
StyleGAN StyleGAN2
s < 30 169/266 ~
182/371 ~
106/191 ~
30 ≤ s < 40 65/266 ~
130/371 ~
66/191 ~
s 40 32/266 ~
59/371 ~
19/191 ~
The experimental results suggest that Progressive
GAN with 63.53% of Bozorth3 scores lower than 30
generates on average the highest number of anony-
mous fingerprints and therefore can be seen as the
most privacy-friendly generation approach. Progres-
siveGAN is followed by StyleGAN2 (55.5% anony-
mous fingerprints) and then StyleGAN (49.06%
anonymous fingerprints). Considering the absolute
number of the high-quality anonymous fingerprints in
the selected snapshot, StyleGAN has clearly the best
generation performance with 182 images followed by
ProgressiveGAN (169 images) and then StyleGAN2
(106 images).
In our case study, we conducted experiments only
with the NIST SD 27 database. The proportions of
anonymous high-quality fingerprints within the
whole number of generated fingerprints as well as the
generation time cannot be generalized for any
reference database taken as training data for a GAN.
However, the experimental results clearly show that
GAN is a suitable technique for "anonymization" of
privacy-sensitive fingerprint datasets.
We demonstrate that a GAN is in general a suitable
technique for generation of high-quality anonymous
fingerprint images. As a data-driven approach a GAN
takes a privacy-sensitive dataset and converts it to
privacy-friendly dataset without loss of dataset
characteristics. The resulting datasets can be used for
research on fingerprints without privacy-caused
limitations. In a case study with the NIST SD27
dataset, we show that all three addressed GAN
architectures (ProgressiveGAN, StyleGAN and Style
GAN2) are capable of converting original privacy-
sensitive fingerprint images to privacy-friendly ones.
StyleGAN2 has an advantage that the fingerprint
images with high NFIQ2 scores are generated after
only a few iterations of training. In contrast,
ProgressiveGAN and StyleGAN require many
training iterations to reach the target image
Generation of Privacy-friendly Datasets of Latent Fingerprint Images using Generative Adversarial Networks
resolution. However, StyleGAN2 is the worst
approach regarding the absolute number of high-
quality anonymous fingerprints generated. From the
perspective of fast generation, StyleGAN is clearly
superior. ProgressiveGAN is preferable regarding the
better anonymity. Our future work will address the
training of GAN models based on multifarious
fingerprint images from many independent sources
and conditional generation of fingerprint patterns
such as predefined locations of minutia or substrate
This research has been funded in part by the Deutsche
Forschungsgemeinschaft (DFG) through the research
project GENSYNTH under the number 421860227.
We thank Philip Wiegratz, Alexander Heck and Mark
Trebeljahr for participating in this work at an early
stage and establishing the feasibility of GAN-based
generation of fingerprint images.
Attia, M., Attia, M. H., Iskander, J., Saleh, K., Nahavandi,
D., Abobakr, A., Hossny, M., Nahavandi, S., 2019.
Fingerprint Synthesis via Latent Space Representation,
In Proc. IEEE Int. Conf. on Systems, Man and
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