Counterfeit Detection and Value Recognition of Euro Banknotes
Sebastiano Battiato, Giovanni Maria Farinella, Arcangelo Bruna and Giuseppe Claudio Guarnera
Image Processing Laboratory, University of Catania, Catania, Italy
Banknote Recognition, Counterfeit Detection, Image Forgery.
This paper describes both hardware and software components to detect counterfeits of Euro banknotes. The
system is also able to recognize the banknote values. The proposed method makes use of images acquired
with near infrared camera and works without mechanical parts. This makes the overall system low-cost. The
effectiveness of the proposed solution has been properly tested on a dataset composed by genuine and fake
Euro banknotes provided by Italy’s central bank.
The detection of counterfeit banknotes is one of the
most important task in billing machines. Unfortu-
nately these systems are very expensive and are used
only for ATM machines (i.e., fully unsupervised by
humans) where a high degree of reliability is re-
quired. In the last years, cheaper systems for valida-
tion and classification of banknotes have been com-
mercialized. They are usually based on motor actua-
tors aimed to let the banknote passing through light
emitters and sensors in a dark area. On the other
hand, the motors are expensive and the mechanical
parts may frazzle in few years.
In this paper we present both hardware and soft-
ware components useful to detect counterfeit of Euro
banknotes and to recognize their currency values.
This is obtained by exploiting a low cost system with-
out motor actuators or other moving parts. A proto-
type of the proposed system has been built and tested
on a dataset composed by genuine and fake Euro ban-
knotes provided by the Bank of Italy. The system is
composed mainly by an infrared camera, a micropro-
cessor and a control flow that implements a robust de-
cision system. A glass is placed in the focal plane of
the camera in order to acquire a sharp image. The ac-
quired image is then processed through the designed
algorithms. The user should just lean a banknote on
the glass and the system provides the information on
the validity and the related value.
The remainder of the paper is organized as fol-
lows: Section 2 reviews the state of the art algorithms
in the field. Section 3 summarizes the proposed al-
gorithms for counterfeit detection and currency value
recognition of Euro banknotes. In Section 4 the de-
signed hardware prototype is described, whereas ex-
perimental results are presented in Section 5. Finally,
conclusions are given in Section 6.
It is well known that, in order to avoid forgeries, the
security systems of money are typically encoded in
the banknotes (in several ways in the different curren-
cies). This fact induced researchers to develop differ-
ent counterfeit detection and value recognition algo-
rithms taking into account the different currencies.
In (Hinwood et al., 2006) authors make use of
light transmittance and pattern recognition techniques
to recognize the value of banknotes. The pro-
posed solution requires the banknote to pass through
light emitters (LED) and receivers (photo transistors)
placed in opposite side. Hence, the system requires
a motor or a moving part. Moreover, the method can
be simply deceivedby counterfeit banknotes(not han-
dled by the authors).
In the approach proposed in (He et al., 2004) the
banknotes are segmented into different regions and
a classifier for each segmented region is employed.
A consensus on the classification results obtained on
the different segmented regions provide the final de-
cision. Specifically, genetic algorithms were used for
classification of both, the segmented regions and fu-
sion blocks.
A neural network and genetic algorithms has been
exploited in (Takeda et al., 1999; Takeda et al.,
2003) to address the problem of banknote recognition,
Battiato S., Farinella G., Bruna A. and Guarnera G..
Counterfeit Detection and Value Recognition of Euro Banknotes.
DOI: 10.5220/0004205100630066
In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2013), pages 63-66
ISBN: 978-989-8565-48-8
2013 SCITEPRESS (Science and Technology Publications, Lda.)
whereas in (Khashman and Sekeroglu, 2005) the au-
thors proposed an Intelligent Banknote Identification
System (IBIS) based on neural networks technique.
The system is designed for Turkish Lira and Cyprus
Pounds identification.
Despite different approaches have been presented
in literature, most of them do not take into account
the Euro banknotes or the validation task. Moreover,
some approaches have been tested only on simulated
data. In the Euro banknotes there are several features
which make challenging the forgery: the sheet, the
watermarks, the particular inks with different behav-
ior in visible, infrared and ultraviolet lights, etc. All
the techniques above could be adapted to other cur-
rencies (e.g., Euro) for the recognition task, but their
adaptation to counterfeit detection is not straightfor-
ward because, under visible light, the counterfeit ban-
knotes usually are identical to the valid ones. Hence,
a system designed for a specific currency (e.g., Euro)
cannot be employed as it is for other currencies (e.g.,
Turkish Lira); each currency has its own specific fea-
tures to be exploited for counterfeit detection and typ-
ically those features strongly differ from a currency to
Different than other approaches, in the proposed
system the infrared (IR) technology has been ex-
ploited since it can provide good and robust features
for counterfeit detection of Euro banknotes.
The proposed software consists of three main blocks:
Calibration, Training and Use Module.
3.1 Calibration
Since in our setup the LED illumination is not spa-
tially uniform, and taking into account its changes
from prototype to prototype, a calibration phase is
required to remove illuminations variability. Specifi-
cally, in this phase a brightness map is computed and
a compressed version of it is stored inside the flash
memory. The brightness map is obtained by acquir-
ing several images of a white sheet of paper in differ-
ent environments (i.e., lighting conditions), and then
considering the average of all the acquired frames.
This process allows also to reduce noise (usually zero
mean Gaussian noise). The map is then used during
the Training and Use Modules to normalize the input
banknote images.
The Training block is used to learn optimal fea-
tures of the banknotes (i.e., patches), which will be
used to determine the validity and the value of each
Figure 1: A banknote of 50 Euro as acquired by the IR cam-
era of the system, and the set of regions used to test its gen-
uineness. White: Overall ROI. Blue: the threshold to bina-
rize the image is calculated in this area. Green: this area
must be dark under infrared light. Red: when seen under
infrared light, this area must be bright and without notice-
able patterns. Magenta: additional area used to check the
genuineness of some denominations.
banknote. The parameters are learned using a large
data set of both genuine and counterfeit banknotes,
which come in several face values.
In our tests we have used both genuine and real
counterfeit banknotes collected, only for testing pur-
poses, at the Bank of Italy. A training dataset of 1000
images has been acquired taking into account typical
contexts of use of the apparatus, in which there are
very different lighting conditions(e.g., neon, sunlight,
incandescent and fluorescent lamps, etc.) and a high
degree of misalignment with respect to the prosce-
nium (the system must be robust to slightly translated
and or rotated banknotes).
Genuine Euro banknotes are made such that only
specific visual features are visible under infrared
lighting. In particular banknotes must show darker ar-
eas in different zones depending on their value. Those
areas always show characteristic patterns. However,
there are overlap among all banknote values, for both
dark and bright areas, which can be used as validity
The aim of learning stage was hence to find the
best area to check banknote validity by using a train-
ing dataset composed by genuine and fake Euro ban-
Given the set of all genuine banknotes G =
, G
, . . . , G
}, the training block is devoted to
search for the largest common dark area F
, and the
largest common bright area T
of genuine banknotes.
3.2 Training
Let F
= IRSignal(G
) be the infrared highlighted
dark area of each G
, then F
Let T
= G
\ F
be the unresponsive bright
area of each G
, then T
The regions T
and F
need to be refined in order
to be robust to counterfeit banknotes, which in some
case might show a slightly similar infrared response.
In a similar way, given the set C = {C
, C
, . . . , C
of all counterfeit banknotes in our data set, we define
c, j
= IRSignal(C
) and T
c, j
= C
\ F
c, j
Let C
be the set of all C
such that
c, j
6= ) (T
c, j
6= ). We can
finally define T and F considering C
as follows:
T = T
c, j
and F = F
c, j
For each banknote B
in G
C, for each possible
threshold s [0, 255], we computed the percentage
of pixels above s in region T
and the per-
centage percD
of pixels below s in regions F
. Us-
ing those values we computed optimum percB
thresholds to separate genuine and counterfeit
To classify the banknote value, during train-
ing phase we learn the most discriminant patterns
for each possible banknote value D
D =
{5, 10, 20, 50, 100, 200, 500}, such that the intra-class
distance is minimized whereas the inter-class distance
is maximized. This process is very tricky for some
banknote values which share similar patterns (e.g., 5
Euro and 20 Euro). The final output is a set of patches,
with the corresponding position, related to the differ-
ent banknote values. To take into account slightly
translated and/or rotated images, the search area of
each patch has the same shape and center coordinates
of the patch itself, but it is wider: approximately the
search area is 2.5 times the corresponding patch area.
Once the training phase is performed, the selected
banknote features (i.e., the learned patches, locations
and thresholds) are stored in the flash memory and
used to infer validity and value of input banknote im-
3.3 Banknote Authentication
Once the image has been corrected for the non-
uniform led illumination (see Section 3.1), a proper
threshold needs to be found according to the actual
input data. In our experiments we have noticed that
the average gray value (indicated as MeanRef) of the
blue region shown in Fig. 1 can be robustly used for
this purpose.
To check the genuineness, the percentage of
pixels inside the green region with a gray value
below MeanRef (i.e., percLT) is computed, to-
gether with the percentage of pixels inside the
magenta and red regions with a grey value
above MeanRef (i.e., percGT). If ((percLT >
)AND(percGT > percB
)) the banknote is
classified as genuine.
To identify the face value, the system makes use
of patches learned during the training stage (i.e., tem-
plates related to the different banknotes values) and
the corresponding search areas coordinates. Search
areas are wider than the patches in order to be ro-
bust to small misalignments. Each template patch is
placed at the center of its search area and a correla-
tion measure between the pattern itself and the corre-
sponding pixels on the search area is used for com-
parison. The procedure then search if a translation
around the neighborhood of the current position could
increase the correlation. This is performed by moving
the pattern position. This step is then repeated until
the correlation reaches a local maximum. The pat-
tern with the highest correlation determines the final
value to be assigned to the input banknote. Informa-
tion about genuineness and face value are then sent
to the display, in order to inform the user about the
results of the banknote analysis. The overall compu-
tational speed is of about 1 second for validity and 2
seconds for value recognition using the architecture
described in the Section 4.
The hardware prototype has been designed to demon-
strate the effectiveness of the proposed framework.
The Infrared LEDs (IR leds block) illuminate the
scene. It is composed by 6 LEDs placed around the
proscenium (the area where the banknote is placed).
The illumination is not uniform in the real system.
Hence, a calibration is needed to take into account
the non uniformity and the performances decay dur-
ing the system life (see Section 3.1). An optical fil-
ter is inserted to avoid the external light source to
influence the image acquisition system. It is placed
on top of the infrared camera (IR camera block) that
acquires the image. The camera is a common low
cost camera with CCTV output. Since the output
is analog, an analog to digital converter (A/D con-
verter block) is used to obtain a digital standardized
format (i.e., CCIR 656). The digital image is ac-
quired by the microprocessor. In the prototype we
have used an ATMEL AT91SAM9XE256, containing
a ARM926EJ S
processor, 200MHz with 256KB
internal high-speedflash memoryto store the program
instructions. It contains also an Image Sensor Inter-
face (ISI) port able to capture video sequences com-
pliant with the standard ITU-R BT 601/656. The soft-
ware contains, beside the control logic for the entire
Table 1: Genuine/Counterfeit classification.
True Positive (counterfeit banknote correctly classified) False Positive (genuine banknote incorrectly classified)
100% 4.3%
False Negative (counterfeit banknote incorrectly classified) True Negative (genuine banknotes correctly classified)
0% 95.7%
Table 2: Banknote value classification.
5E 10E 20E 50E 100E 200E 500E Counterfeit
5E 88,00% 0,00% 0,00% 0% 0,00% 0,00% 0,00% 12,00%
10E 1,00% 91,00% 0,00% 0,00% 0,00% 0,00% 0,00% 8%
20E 0,00% 0,00% 98,00% 0,00% 0,00% 0,00% 0,00% 2%
50E 0,00% 0,00% 0,00% 99,00% 0,00% 0,00% 0,00% 1,00%
100E 0,00% 0,00% 0,00% 0,00% 93,00% 0,00% 0,00% 7,00%
200E 0,00% 0,00% 0,00% 0,00% 0,00% 98,00% 1,00% 1,00%
500E 0,00% 0,00% 0,00% 0,00% 0,00% 0,00% 95,00% 5,00%
subsystems (e.g., IR led, IR camera settings, Display,
etc.), the related algorithms (for both validation and
classification) described in Sections 3. The prototype
has been also equipped with external SRAM mem-
ory, since the microprocessor contains only 32KB of
internal SRAM.
To evaluate performances of the proposed technique
we used our prototype to acquire a test set of 1750
banknote images, with the same criteria used for the
training dataset (i.e., both counterfeit and genuine
banknotes have been acquired under several environ-
ment lighting conditions, with different illuminants
and brightness). In both cases, training and experi-
mental phases, the banknotes samples have been pro-
vided by the Bank of Italy.
Acquired counterfeit banknotes include also spec-
imens carefully calibrated to mislead digital counter-
feit detectors. To deal with special cases (i.e., fake
banknotes provided by the bank), additional proce-
dures have been included. The overall processing
time is very close to the base algorithm, since it in-
cludes a few average computations on very small ar-
eas. Table 1 reports the results of the validity assess-
ment. Table 2 shows the results of the banknotesvalue
In this paper we have proposed an effective system
to detect counterfeit of Euro banknotes composed by
both hardware and software modules. Conversely to
the state of the art algorithms, the proposed solution
makes use of infrared imaging and low-cost hard-
ware. The proposed system allows recognizing not
only the value, but also forgeries. The described al-
gorithms are robust to changes in environment light-
ing, in terms of illuminant type and incident inten-
sity. Thanks to a training phase it is also robust to
non-uniformity of the infrared light. The experiments
performed on genuine and fake banknotes provided
by the Bank of Italy show good performances in both
validity and value recognition.
The authors would like to thank Imperial Emporium
Srl and the Bank of Italy for supporting this research
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