EP3400584B1 - Completeness check of a value document - Google Patents

Description

The present invention relates to a method and a corresponding device for checking documents of value for completeness and authenticity. Counterfeits of valuable documents could be composed of a plurality of partial documents, for example, sections of genuine valuable documents were combined with sections of copies. According to the invention, it is possible to reliably identify such forgeries and to check documents of value for completeness or authenticity.

DE 19714 519 A1 teaches to scan a document to be checked over the entire surface or along a defined measuring track with a sensor suitable for detecting the marker substance. The distribution of the marker signal is determined and compared with the expected signal curve specified by the pattern of the marking printed with the marker material. In particular, the general presence of the characteristic substance, jumps in its distribution and areas that deviate from the expected reference distribution are checked.

The DE 10 346 636 A1 describes a sensor-based authenticity check of valuable documents with a luminescent marker, which is carried out integrally along a track across the valuable document. While the addition of the luminescence signal along the measurement track is well suited to detecting small, noisy spectral signals, it prevents a small-scale and therefore precise assessment of completeness.

The WO 2011/037750 A2 describes the authenticity recognition of banknotes by detecting a homogeneously distributed IR luminescent substance of measurement traces and comparison of the measured modulation of the luminescence intensity through overprinting or applied holograms, stripes, etc. with expected target profiles. Areas with high statistical fluctuations, such as security threads or hologram strips, are excluded from the evaluation and an authenticity decision is made if, for example, > 51% of the measured profile matches one of the four position-dependent authenticity references.

This means that, under ideal conditions, a statement of completeness along the measured track is possible. However, when measuring on high-speed banknote processing machines under real conditions with tracking variations or skewing of the banknotes in the machine as well as aging or stains on the banknotes, many real banknotes are classified as incorrect using this method. Conversely, with the authenticity criteria described, which are necessarily weak in order to avoid many banknotes being incorrectly classified as false, for example only > 51% agreement along a track, a large number of snippet forgeries are recognized as genuine, so that this method does not solve the problem satisfactorily.

The US 6,393,140 B1 describes a further method for checking banknotes, in which a signal such as color or magnetism is measured at several defined points on the banknote and the relative distances between the measured values and a reference value are determined and then standardized. This procedure enables a local authenticity assessment, but not a reliable completeness check.

The US 2005/0100204 A1 describes a method for authenticating banknotes based on the brightness values of individual pixels. Pixel specific Brightness results in a geometric shape that is compared to a reference shape.

DE 10 2007 044 878 A1 relates to a method for checking the authenticity of banknotes. A first fluorescence intensity is determined at a first measurement time and a second fluorescence intensity is determined for a portion of the banknote at a second measurement time, and a corrected luminescence intensity is determined. The corrected luminescence intensity is compared to a threshold value.

DE 10 2010 021 803 A1 discloses a method for checking the authenticity of a banknote. A spatially resolved measurement of a transmission is carried out. The transmission values are compared with a threshold value and the shape of connected subthreshold areas is evaluated.

However, especially when measuring on fast-running banknote processing machines with processing speeds of up to over 12 m/s, additional problems arise beyond a slow ticket inspection that must be addressed using special procedures and algorithms. Only then can a reliably functioning completeness check be possible, even under such difficult conditions. For example, on a banknote processing machine it is not always guaranteed that the denomination or position of the document of value and thus the reference distribution to be compared is known at the time of evaluation.

Furthermore, the achievable spatial resolution of the feature signal can be dramatically reduced compared to usual resolutions of optical image sensors in the visible: The spatial resolution can be limited both by the detector technology used and by intrinsic time properties of the feature substance, such as the attack time of a luminescent substance. Especially with track-based sensors, the pixel size can be in the range of a few mm or even a few cm. In order to be able to derive the completeness of the value document from the feature measurement as reliably as possible in such situations, the information from each individual measurement pixel must be adequately evaluated and used for the completeness check.

The aim of the present invention is to provide a reliable completeness measurement or completeness check of modern value documents for the detection of so-called chip forgeries under the conditions of fast-running banknote processing machines (ie measurement at relative speeds, for example of 1-13 m/s, preferably 6-12 m/s between banknotes and sensor). The combination of various security features on the document of value and their interaction with the sensor-based measurement of the machine-readable feature also occurs when the machine-readable feature material is present homogeneously often exhibits a complex modulation pattern of the measured intensity of the feature signal. This makes direct completeness assessment considerably more difficult. In particular, it is assumed that there is no denomination or location information available at the time of the measurement or the completeness assessment, which is often encountered in reality.

It is therefore an object of the present invention to provide a method or a device which carries out a reliable assessment of the completeness of a document.

This task is solved by a method or a device for checking the completeness of valuable documents with the features of the independent claims. Advantageous refinements and developments of the invention are specified in the claims dependent thereon. Accordingly, a method for checking completeness and/or

Authenticity of valuable documents proposed according to claim 1. According to the invention, at least one document of value has at least one machine-readable feature substance in at least two different locations. In one step, the document of value is stimulated at least locally. This can be done, for example, by means of electromagnetic radiation, for example light with a wavelength in the visible spectral range. Additionally or alternatively, the document of value can be subjected to magnetism.

Furthermore, according to the invention, a feature intensity with regard to the machine-readable feature substance is recorded at several different locations on the document of value. An optical and/or magnetic detection unit can be used for this purpose. The recording unit forms accordingly a characteristic value for each measuring location. The feature intensity is determined from the feature value in relation to the location of the measurement locations of the value document. The locally limited surface element of the location-related feature intensity and/or the location-related reflectance value can be understood below as a pixel. A feature value or a large number of, in particular connected, feature values can be used. Furthermore, a partial aspect of the feature value, for example a specific wavelength when detecting a spectral range, can be used to generate or detect the feature intensity.

In a further step, the location-related feature intensities are classified using a threshold value. Using the threshold value, a location-related classification of the feature intensity can be carried out for each location-related feature intensity, for example into locally real or locally fake. Furthermore, location-related boundaries of an expected location distribution of the machine-readable feature material are determined. These limits preferably reflect the longitudinal extent and/or width extent, particularly preferably the surface extent, of the document of value. Furthermore, errors in the structure, for example areas of counterfeiting of a document of value, in particular if the length falls below a minimum, can be determined based on the limits.

In a further step, the location-related distribution of the classified feature intensities is evaluated. At least two classified feature intensities are evaluated in relation to each other and/or to the specific location-related boundaries.

The feature intensities at the individual measurement locations are preferably considered locally real with above-threshold intensity or with sub-threshold intensity classified as locally inauthentic. These measurement locations are also referred to below as classified pixels.

To evaluate the location-related distribution of the classified feature intensities, the number and spatial distribution of subthreshold and/or suprathreshold feature intensities can be determined, for example. As an alternative to a, possibly local, threshold value, a reference feature intensity can be used as a comparison value.

Documents of value are sheet-shaped objects with a front and a back, which represent, for example, a monetary value or an authorization and therefore should not be able to be produced arbitrarily by unauthorized persons. They therefore have features that are not easy to manufacture, in particular to copy, the presence of which is an indication of authenticity, i.e. that they were manufactured by an authorized body. Important examples of such documents of value are coupons, vouchers, checks and especially banknotes.

By using the method according to the invention in the two alternative variants of measuring the machine-readable feature with or without measuring the reflectance values, a reliable assessment of the completeness or authenticity is achieved. In one embodiment, a specific location-dependent threshold value is assigned to at least one location or pixel. A location-dependent threshold value is preferably assigned to a large number of locations or pixels or a group thereof. Using location-dependent threshold values, a more detailed location-dependent classification of location-related feature intensities is possible. In particular, special ones can Location-dependent properties are represented and checked by the location-dependent threshold values.

According to one embodiment of the invention, the local authenticity is determined based on a feature intensity. Several feature values, for example of luminescence radiation, are preferably used to assess the local authenticity. This includes a temporal behavior of the feature intensity such as an attack behavior, a decay behavior, a spectral distribution of the feature intensities and/or spatial information such as track information or a transport position. In particular, a specific comparison of the determined feature values can be carried out with the expected feature values of a genuine document of value and taken into account when determining the feature intensity. For example, despite the presence of significant luminescence intensity, the feature intensity can be determined to be zero, for example, if the spectral distribution of the luminescence radiation does not match the expected spectrum.

With the present invention, a local authenticity of the value document can therefore be determined based on a local feature value. In addition, an assessment of the entire value document for completeness and/or authenticity is possible.

In one embodiment of the invention, reflectance values are recorded in a spatially resolved manner at several different locations of the document of value. The measurement locations of the reflectance values preferably essentially correspond to the measurement locations of the feature intensities. The area of the measurement location of a reflectance value can be larger or smaller than the area of the measurement location of the corresponding feature intensity. The measurement locations preferably have an equal area. The measurement location of a reflectance value can also be offset, preferably overlapping, to the measurement location of a feature intensity. The spatially resolved detection of the reflectance values is preferably carried out at the same time as the spatially resolved detection of the feature intensity values.

According to one embodiment, the recorded location-related reflectance values can be parameters of a characteristic curve for location-dependent or location-related threshold values.

According to the invention, in one embodiment, track completeness is determined. In each case, feature values, i.e. location-related feature intensities and, if necessary, location-related reflectance values, can be recorded along a measurement track on the value document and taken into account to determine the completeness of the track.

For example, by using the invention as a single-track sensor, the data from this measurement track is evaluated. Essentially, however, only one-dimensional trace completeness can then be checked and evaluated. For this purpose, the individual feature intensities and, if applicable, remission values of the pixels are first compared individually with a minimum value and classified as real or fake, or accordingly, for example as sub-threshold, above-threshold or average. The length of the document of value can be determined from the distance between the two outermost measuring points with a signal intensity above a minimum threshold.

Furthermore, the measured length of the document of value can preferably be measured in a transport direction by edge detection from a feature intensity curve be determined. Feature intensities are recorded in a spatially resolved manner in the feature intensity curve. This means that the feature intensity curve includes value points that result from the feature intensity and the associated location. In the simplest case, the extreme spatial positions are determined at which an average threshold (upper threshold - lower threshold)/2 of the feature intensities is reached. Quantile values are preferably used instead of the upper threshold and lower threshold, e.g. B. 75% (= almost white) and e.g. B. 5% quantiles (= almost black) corresponding to the maximum feature intensity. The measured length of the value document results from the difference between the two location-related feature intensities, whereby the size of the pixel or measurement location distance is generally taken into account, in particular added.

In one embodiment, the accuracy of the length determination can be increased by interpolating the feature intensity curve between the measured points, preferably linearly (alternatively spline), and thus determining the length with subpixel precision.

The determined or determined length is then compared with a known or predetermined minimum length, which corresponds, for example, to the true length of the shortest denomination variant of a banknote series.

If the minimum length is not reached, it can definitely be assumed that the item is a counterfeit. The completeness can be determined quantitatively simply by the ratio of the number of pixels with above-threshold feature intensity, or real pixel, to the number of pixels corresponding to the measured length (assuming constant transport speed or spatial resolution).

The measurement of the feature sensor is preferably triggered by a light barrier (e.g. the banknote processing machine) in such a way that the measuring points of different documents of value can always be assigned to different data sets. The evaluation for completeness from the length determination usually affects exactly one document of value.

In principle, however, areas with significantly reduced feature intensity can also occur within a single (real) value document, up to completely disappearing feature intensity, if features such as metallized holograms, security strips, etc. are opaque to the excitation radiation and/or radiation emanating from the value document cover machine-readable feature material. The - depending on the denomination - the maximum possible width of the opaque covers as well as their position relative to the front and/or rear edge in the transport direction are taken into account for the completeness assessment.

In one embodiment for calculating or determining track completeness, a sensor has several measurement tracks, such as. B. 2, 4, 6, 8, 10, 20 or more measurement tracks, so that a two-dimensional distribution of the feature intensities is recorded. To assess completeness, a threshold value classification of the individual location-related feature intensities is also initially carried out. A convex hull is then calculated around the determined supra-threshold location-related feature intensities. Furthermore, known or predetermined location-related subthreshold feature intensities, for example by a background system, are compared with the determined location-related supra-threshold feature intensities encompassed by the convex hull, for example a number. For example, is the number of determined above-threshold Feature intensities smaller than the known number of above-threshold feature intensities can, for example, be assumed to be an undesirable hole in the document of value. This process allows the detection of holes or opaque spots within the document of value, which are unwanted and therefore indicate a forgery.

Instead of a convex hull for above-threshold location-related feature intensities, the evaluation can be carried out on sub-threshold location-related feature intensities. Furthermore, an evaluation with regard to any recorded and known or predetermined remission values is possible.

In one embodiment, the convex hull is calculated separately for each line, i.e. in this case the interval between the front and rear ends of the line. For each interval, subthreshold location-related feature intensities are marked as spurious or appropriate.

In one embodiment, a two-dimensional convex hull is calculated using the positions of all pixels above the threshold, e.g. using the Graham algorithm. For example, all subthreshold pixels with positions within the convex hull are marked as spurious or equivalent.

In one embodiment, subthreshold location-related feature intensities within the convex hull can be rejected if, for example, their measurement locations lie within occurring patterns, such as a transparent window or a metallic LEAD strip.

Furthermore, the two-dimensional distribution of the pixels above the threshold is preferably analyzed and evaluated. In particular, this will be the case Occurrence of larger holes checked. In particular, location-related feature intensities that are classified as inauthentic or corresponding are determined and identified, marked and counted into two-dimensional connected areas. Lying e.g. For example, if there are more than 2, 3, 5, ... (resolution-dependent) related location-related feature intensities that are classified as spurious or accordingly, a potentially missing area is recognized. The position and geometric extent of the areas classified as fake or corresponding are then analyzed and compared with patterns that occur in a known manner, such as a transparent window or a metallic LEAD strip. In particular, the shape, maximum width and relative position to the edges or corners of the document of value are checked for plausibility and, if there are deviations, classified as "incomplete" or similar.

In an embodiment with very different resolution of the measurement in the (x) track direction and y direction (number of tracks), neighboring pixels in the line direction that are classified as spurious or correspondingly can be specifically counted and multiple pixels can be evaluated in this direction.

The individual tracks are then evaluated analogously to the single-track sensor described above, which provides several measured values for track completeness.

In one embodiment, the authenticity and/or completeness of the value document is recognized if there is at least a certain ratio between the number and a spatial distribution of the classified pixels, feature intensities and/or reflectance values.

In one embodiment, the measured length can be determined from the maximum value of the individual track lengths and, in the event of deviations in the other track lengths, the existence of a lack of completeness can be concluded, preferably taking a tolerance value into account.

Analogous to track completeness, column completeness can be determined, in particular by taking several measurement tracks into account.

In one embodiment, the spatially resolved detection of the feature intensities can include measuring a spectral luminescence intensity of a luminescent substance. Accordingly, the document of value can be checked for the presence or absence of a luminescent substance and checked for authenticity and completeness according to its local assignment or distribution. Furthermore, the spatially resolved detection can include a spectral measurement of a Raman band and/or a so-called surface enhanced Raman spectroscopy (SERS). In addition, the detection can include a measurement of an absorption band with respect to a specific spectral range, for example infrared, and/or the measurement of magnetic properties.

The detection of feature intensities can be preceded by the detection of feature values. Feature values can include measurement results, for example with regard to a spectrum. In particular, the feature values are specifically processed to provide feature intensities. The feature values can be assigned a filter, for example to evaluate a spectral range, in particular a wavelength. Furthermore, the feature values can be assigned an algorithm. The feature values can be sensor values from which feature intensities are ultimately determined. The feature values can each include a variety of different feature intensities.

The recording of feature intensities and/or reflectance values, if applicable, can take place both on a front side and on a back side of the value document. In particular, feature intensities and/or, if applicable, reflectance values can be recorded on the same and/or opposite side, in particular in relation to the measurement location. Preferably, the feature intensities and/or reflectance values are recorded at the same, opposite locations on the front and back.

In one embodiment, location-dependent threshold values can be determined by a characteristic curve which depends on the feature intensities determined on the opposite side of the value document at the respective location

In one embodiment, transmission values are recorded in a spatially resolved manner, preferably by time-delayed illumination as part of reflectance measurements on the front and back and/or by time-delayed illumination as part of the measurement of feature values or the detection of feature intensities on the front and back of the document of value.

In one embodiment, a combined classification can be carried out at several measurement locations, taking into account data tuples assigned to the measurement locations. The data tuples include at least one feature intensity and at least one of the following components: a further feature intensity, a remission value, and/or a transmission value.

Furthermore, the above-mentioned task is achieved by a sensor or a sensor unit and/or a banknote processing machine, which are designed to carry out a method set out here.

The sensor can be part of the sensor unit and/or the banknote processing machine.

Preferably, the local excitation of the document of value, in particular the feature substance, takes place with the aid of excitation radiation. The feature substance preferably has a luminescent substance or a Raman-active substance or a substance that can be detected by surface enhanced Raman spectroscopy (SERS). Furthermore, the feature substance can have magnetic properties. In addition or as an alternative to the list, any characteristic substance with machine-testable properties is conceivable. In the present case, the characteristic substance can also be viewed as a marker.

In one embodiment, the excitation radiation can be spectrally narrow-band, broad-band, or a superposition of various narrow-band and/or broad-band radiation components.

In one embodiment, the document of value is illuminated with test radiation to check the presence of a document substrate at the respective measuring point, to measure the size of the document of value and/or to measure a reflectance value.

To detect feature intensities and/or reflectance values, according to one embodiment of the invention, the excitation radiation and/or the test radiation is measured in a spatially resolved manner.

The documents of value to be checked for completeness within the scope of this invention are equipped with at least one machine-readable feature substance that was introduced or applied at least along a track in the direction of movement of the document of value. The machine-readable feature substance preferably comprises at least one luminescent marker (luminescent substance), particularly preferably inorganic phosphors based on host lattices doped with rare earth or transition metal ions.

The machine-readable feature substance is preferably distributed homogeneously over the surface of the document of value or introduced homogeneously into the volume of the document of value (paper or polymer). Alternatively, it can be printed over the entire surface or in partial areas of the document of value, but at least along a track over the length, or in the case of transverse transport over the width of the document. In the case of a luminescent feature substance, it can either emit at a shorter wavelength (anti-Stokes luminescence or upconverter) and/or at a longer wavelength than the excitation wavelength (Stokes luminescence). Anti-Stokes emitters are not preferred because they typically have significantly lower brightness.

It is preferably a document of value made of paper which has a Stokes luminescent substance introduced into the paper volume in a homogeneous distribution via the pulp during paper production.

In a preferred variant, there are at least two independently measurable feature substances in the document of value, which are either equally or differently spatially distributed. These can be, for example, two independent feature substances introduced into the substrate of the document of value (polymer or paper). Alternatively, a feature substance can be present in the substrate and a second feature substance can be printed on.

The general structure of the sensor is described as follows. To carry out the method according to the invention, a suitable sensor for the machine-readable feature is required. In the case of a luminescence feature or a SERS feature, this is typically designed for the spectrally resolved detection of the feature substance. The feature sensor is preferably installed in a machine for automated checking or sorting of valuable documents, in particular a banknote processing machine. This transports the valuable documents to be checked linearly through the detection area of the sensor device in a predetermined transport direction.

The feature sensor may include a luminescence sensor. The luminescence sensor is preferably designed as a detection device for spectrally resolved detection of the luminescence radiation in at least one predetermined spectral detection range and delivers detection signals which reflect at least one, in particular spectral, property of the detected luminescence radiation. The spectral resolution can be achieved either by dispersive elements, such as. B. diffraction gratings in reflection or transmission or through suitable filters in front of the respective detector elements. The spectral resolution of the detector has at least two wavelength channels, preferably >4, particularly preferably >8 different wavelength channels.

To excite the luminescent radiation emanating from the document of value, the sensor illuminates it in a detection area with an excitation radiation. This is matched to the luminescent substance used to mark the document of value and is in the optical range, i.e. in the UV, VIS or IR spectral range. The excitation radiation can be spectrally narrow-band, broad-band or a superposition of various narrow-band and/or broad-band radiation components.

The luminescence sensor is preferably additionally equipped with a remission sensor. Here it illuminates the document of value with test radiation in addition to the excitation radiation. This is used to check for the presence of the document substrate at the currently illuminated location or to measure the size of the document of value and/or to measure the reflectance. In one variant, the test radiation preferably has a spectral distribution that at least partially or completely overlaps with the spectral detection range of the detection device. In this case, the reflectance of the valuable document can be determined directly without the need for a separate detector.

In an alternative variant, in addition to the illumination device for the test radiation, there is also a separate test radiation detector, together with any necessary illumination, collimation and/or imaging optics, with which, in addition to the luminescence radiation, the remission is also measured in a spatially resolved manner and via the geometric imaging properties of the two Detection channels are each assigned to the associated measuring locations of the luminescence radiation.

The illumination surfaces of the excitation radiation and the test radiation preferably overlap strongly spatially in the detection range of the sensor or are largely identical, so that the spatial assignment of the measured values can take place directly.

Furthermore, the sensor has a control and evaluation device which controls the emission of excitation radiation or test radiation and receives the signals from the detection device(s), processes them and evaluates them for authenticity or completeness.

The test radiation and the excitation radiation are generated with suitable light sources such as incandescent lamps, flash lamps, LEDs or laser diodes, in particular edge emitters or VCSELs. Additional filters or phosphor converters may be required to generate the desired spectra. The reflectance is typically determined in the visible spectral range either in a broad or, alternatively, narrowly restricted wavelength range. Alternatively, the remission can also be determined in a non-visible spectral range such as UV or NIR.

In a first step, the luminescence signal obtained during each measurement cycle can be evaluated locally for each individual measuring point. This can include the evaluation of a spectral distribution, for example after an offset or background correction, whereby any signal contributions introduced by scattered light or by the amplifier or evaluation electronics are eliminated. The correction parameters required for this can either be preset or determined dynamically using suitable dark measurements. These can be carried out, for example, when there is currently no document of value in the detection range of the sensor and/or one (or more) measuring point on the document of value itself is “sacrificed” and instead a dark measurement is carried out without excitation and without test lighting.

Optionally, the measured spectra can be standardized with preset or separately measured illumination intensities or reflectance values etc. measured on special calibration substrates.

Furthermore, the local authenticity of the document of value is checked on the basis of the measured luminescence signal. This can be done on the basis of the spectral distribution or can also evaluate the rise and/or decay behavior. At least one intensity value is calculated, which represents a measure of the local luminescence intensity and, together with the measurement location, i.e. i.e., e.g. B. the x-y coordinate formed from the track and transport position is saved.

Likewise, the reflectance value is determined in the case of narrow-band test lighting, or the reflectance values of several spectral channels in the case of spectrally resolved reflectance measurement. The determined reflectance value is saved together with the measurement location, i.e. the transport position.

In general, further evaluation takes place in two stages: First, the feature measurements are classified into measurement locations with above-threshold feature intensity (real or analog) and measurement locations with below-threshold feature intensity (fake or analog). Completeness is then determined based on the number and distribution of spatially resolved feature intensities classified as spurious.

Case 1 describes an evaluation without denomination information and without reflectance measurement. In this most difficult case, the sensor only measures the machine-readable feature, without having any further information about the present document of value or about its true or apparent size. This means that only the measurement data distribution of the machine-readable feature is available for the completeness assessment. Nevertheless, even on the basis of this limited information, a well-founded statement about completeness can be made.

In reality, counterfeits or incomplete documents of value in which narrow vertical structures or stripes have been cut out occur relatively often. In order to be able to recognize these efficiently, an assessment of column completeness is useful. Here the number of subthreshold pixels is determined column by column and compared with a threshold value. If this threshold (e.g. 2 or 3) is exceeded in a column, the value document will be rejected as incomplete. This means that these classes of forgeries with vertically extended manipulations are particularly effectively detected.

A different evaluation preferably takes place between the edge tracks and center tracks. This makes it possible to detect missing measurement areas that occur due to the document of value tilting during transport and to reduce the frequency of documents of value that are incorrectly classified as incomplete. In one embodiment, for example, track completeness can generally be ignored during the evaluation. Alternatively, the edge trace can be evaluated in the shortened form within the extent recognized by the reflectance measurement.

In a preferred case, several feature substances that can be measured independently of one another are present in the document of value. From these, separate characteristic values are advantageously recorded and these are evaluated or evaluated. If a characteristic value is present at a measurement location, it immediately follows that the second characteristic substance must also be measurable at this location - taking into account the spatial distribution of the second characteristic substance.

In general, in this case, a union of the convex hulls of the distributions of the two characteristic substance measurements can be used as a measure of the geometric extent of the document of value.

This means that the suspected case of counterfeiting, in which a track further out provides an apparently longer banknote length than a track further in, can be correctly identified. This means in particular that if there is an external edge track n with a valid first measured value, then the track n, but at least the neighboring track (n-1) further inside, is also fully evaluated for the second feature with regard to track completeness. The evaluation is of course carried out taking into account the expected target distribution of the respective characteristic substance. This procedure is applied analogously for the top and bottom tracks.

In a preferred case, the completeness check is carried out by a machine-dependent evaluation, in which the actual geometric conditions with regard to the transport of valuable documents are taken into account. Depending on the machine model, the transported documents of value can be aligned either along the lower edge or centered, for example. This means that when processing different denominations with different sizes (especially widths) depending on the machine different traces of feature signals can be expected. Since these transport properties always remain constant, they are advantageously taken into account when assessing completeness and are parameterized when installing the sensor. In particular, it is defined which tracks should always be complete (center track(s) vs. bottom track or second bottom track to take skewing into account).

To assess the completeness of the document of value, both the trace completeness and the area completeness are preferably evaluated and finally combined to form a measure of completeness. A detected lack of trace completeness can lead to the entire value document being recognized as incomplete, even if the surface completeness may still be within an accepted tolerance threshold.

A particularly reliable assessment of completeness is carried out by checking at the level of the pixels (pixel completeness), at the level of the measurement tracks (track completeness) and by evaluating the two-dimensional distribution of the measured values obtained (area completeness or two-dimensional completeness).

Figur 1:

Eine schematische Darstellung einer Ausführungsform eines Verfahrens gemäß der Erfindung;

Figur 2a:

Ein erstes Diagramm gemäß einer Ausführungsform zur Klassifizierung auf Pixelebene;

Figur 2b:

Ein weiteres Diagramm gemäß einer Ausführungsform zur Klassifizierung auf Pixelebene;

Figur 3:

Eine schematische Darstellung einer Kennlinie für Schwellenwerte zur Klassifikation auf Pixelebene;

Figur 4:

Eine schematische Darstellung des zeitlichen Ablaufs der Beleuchtung für Remissions- bzw.;

Figur 5a:

Eine schematische Darstellung zur Klassifikation auf Pixelebene bei beidseitiger Merkmalsmessung;

Figur 5b:

Eine schematische Darstellung einer weiteren Kennlinie zur Klassifikation auf Pixelebene bei beidseitiger Merkmalsmessung;

Figur 6:

Ein Kurvenverlauf von Merkmalsintensität, Remissionswerts sowie eines dynamisch ermittelten Schwellenwerts zur Klassifikation auf Pixelebene;

Figur 7:

Eine schematische Darstellung von Remissionswerten einer zu prüfenden Banknote;

Figur 8:

Eine schematische Darstellung von Merkmalsintensitäten einer zu prüfenden Banknote;

Figur 9:

Eine schematische Darstellung von Merkmalsintensitäten einer zu prüfenden unvollständigen Banknote;

Figur 10a:

Eine schematische Darstellung ortsbezogener Verteilung klassifizierter Merkmalsintensitäten;

Figur 10b:

Eine schematische Darstellung ortsbezogener Verteilung klassifizierter Merkmalsintensitäten einer unvollständigen Banknote;

Figur 11:

Eine Darstellung von Transmissionswerten einer Banknote;

Figur 12:

Eine weitere schematische Darstellung einer pixelweisen Klassifikation ; und

Figur 13:

Eine schematische Darstellung einer kombinierten Klassifikation von Merkmalswerte.

Further features and advantages of the invention result from the present description of exemplary embodiments of the invention as well as further alternative embodiments in connection with the following drawings, which show:

Figure 1:

A schematic representation of an embodiment of a method according to the invention;

Figure 2a:

A first diagram according to an embodiment for pixel-level classification;

Figure 2b:

Another diagram according to an embodiment for pixel-level classification;

Figure 3:

A schematic representation of a threshold characteristic curve for pixel-level classification;

Figure 4:

A schematic representation of the timing of the illumination for remission or;

Figure 5a:

A schematic representation of pixel-level classification with bilateral feature measurement;

Figure 5b:

A schematic representation of another characteristic curve for classification at the pixel level with feature measurement on both sides;

Figure 6:

A curve of feature intensity, reflectance value and a dynamically determined threshold value for classification at the pixel level;

Figure 7:

A schematic representation of reflectance values of a banknote to be tested;

Figure 8:

A schematic representation of feature intensities of a banknote to be checked;

Figure 9:

A schematic representation of feature intensities of an incomplete banknote to be checked;

Figure 10a:

A schematic representation of spatial distribution of classified feature intensities;

Figure 10b:

A schematic representation of the location-related distribution of classified feature intensities of an incomplete banknote;

Figure 11:

A representation of transmission values of a banknote;

Figure 12:

Another schematic representation of a pixel-wise classification; and

Figure 13:

A schematic representation of a combined classification of feature values.

In the Figure 1 A process sequence for checking a document of value according to the invention is shown schematically.

In a first step S1, a document of value is provided. The value document includes at least one machine-readable feature substance. The feature substance is arranged at at least two different locations, preferably arranged over a substantial area of the document of value. The machine-readable feature material preferably extends partially over the entire surface extent of the document of value.

In a step S2, the document of value is excited at least locally, preferably with electromagnetic radiation. The excitation can be done by irradiating the entire document of value. Preferably, the document of value is irradiated in certain areas, particularly preferably at specific points. A sensor unit is used to detect a spatially resolved feature value, in particular a feature intensity, with respect to the machine-readable feature material at several different locations on the value document (S3a). The detection usually concerns the surface section of the document of value which was excited by means of electromagnetic radiation, with the excited section preferably having an area that is the same or larger than the detected area or point.

Preferably essentially at the same time as step 3a, a reflectance value is recorded (S3b) in a spatially resolved manner with respect to the feature values recorded in step 3a, whereby several reflectance values can also be recorded, which relate to different wavelengths, for example.

In a step S4, the feature values and the preferably recorded reflectance value are evaluated in a spatially resolved manner in accordance with steps S2, S3a and, if necessary, S3b. The feature values are compared with expected reference signals and a feature intensity is determined for the spatially resolved feature values. The location-related feature intensities are preferably normalized.

Based on the evaluation from step S4, a classification of the location-related feature intensities takes place in step S5. The classification is based on a lower threshold value of the feature intensities (see Fig. 2a ) or a combined use of a lower and upper threshold value of the feature intensities (see. Fig. 2b ) or a use different threshold values of the feature intensities, in particular depending on one or different remission values ( Fig. 3 ).

The evaluation of a feature value and the classification of a feature intensity can be carried out independently of the acquisition of further feature values. Thus, for a feature intensity, step S4 can preferably take place immediately after step S3a and/or for one or more feature intensities, step S4 can take place after the multiple feature intensities have been detected according to S3a. Analogously, for a feature intensity, step S5 can preferably take place immediately after step S4 and/or for one or more feature intensities, step S5 can take place after the evaluation of the multiple feature intensities according to S4.

In step S6, a location-related distribution of the feature intensities is determined based on the evaluation from step S4 or alternatively based on the classification of the feature intensities from step S5. Expected location-related limits of the distribution of the characteristic substance are derived from the location-related distribution. These location-related limits are determined either from the distribution of the classified location-related feature intensities, for example by calculating the convex hull of the above-threshold feature intensities, or by taking into account further measured values, in particular the remission values.

Subsequently, in step S7, the location-related distribution of the classified feature intensities obtained in step S5 is evaluated. The evaluation is carried out in particular with regard to the relative position of the pixels classified above or below the threshold to one another and, according to the invention, with regard to the relative position of the pixels classified below the threshold relative to those determined in S6 Limits of the expected spatial distribution of the machine-readable feature substance.

Based on the evaluation from step S7, a completeness measure is finally determined for the entire value document, which can be used for authenticity evaluation or, for example, for sorting decisions in a banknote processing machine.

In the diagrams now given, the colors yellow with reference symbol "ge", green with reference symbol "g", black with reference symbol "s", red with reference symbol "r" and blue with reference symbol "b" are used. All color information listed is to be understood as an example only and serves illustrative purposes only. Of course, values or other designations can be used instead of the color information.

In the Figures 2a and 2b An intensity field is shown for a scanned pixel, with the threshold values for feature or remission signals used for classification at the pixel level being entered as an example according to one aspect of the present invention.

The classification of the pixels as real/fake is done by way of example with reference to Figure 1 as follows. In order to evaluate a valuable document for authenticity and/or completeness, a pixel-based classification is carried out.

All measuring points or pixels that have reflectance values above a certain threshold R ₁ in the reflection channel must also provide sufficient feature intensity in the feature signal in order to be recognized as a real part of the document of value. The feature intensity must therefore be higher than a lower threshold of the feature intensity M _min . This classification of all pixels using fixed threshold values can be clearly seen using the 4-field table Fig. 2a represent.

Figure 2b shows thresholds for feature intensity and reflectance values, respectively, for pixel-level classification in a modified 4-quadrant scheme using a lower threshold M _min , R ₁ and an upper threshold M _max . All pixels that are sufficiently bright (ie reflectance value R > reflectance threshold R ₁ ) and provide a sufficiently intense feature signal (feature intensity M > minimum feature intensity M _min (lower threshold value of the feature intensity)) are evaluated as "green". Pixels that are too dark (R < R ₁ ), such as those found in B. can occur through holes in the document of value, are classified "black", while existing areas of the document of value (R > R ₁ ), ie, a very high reflectance value is detected, are classified as "red" without sufficient characteristic signal as suspected of being a counterfeit, in particular as a snippet forgery become. If there are areas with insufficient remission but with sufficient feature intensity, these are classified as “yellow” feature excess. This can e.g. B. occur when there is heavy soiling (with special spectral behavior of the illuminated surfaces) or in window areas with invisible features.

Furthermore, according to Fig. 2b an upper threshold for the expected feature intensity M _max is used. Here all areas with an excess of feature signal can then be classified as “yellow”. The combined evaluation of remission and feature intensity at the pixel level always allows easy consideration of otherwise problematic situations, such as: B. a run-up (ie y-offset) or skew Document of value in the processing machine as a result of a transport disruption.

In a further embodiment, the reflectance signal at the pixel level is used to normalize the feature signal (only in the linear range) for the purpose of soiling or overprinting correction. Edge effects are also taken into account if the value document edge only partially overlaps with the measurement pixels and therefore reduced feature and remission intensities are detected.

Alternatively, the threshold for the feature intensity required for authenticity recognition can advantageously be dynamically adjusted pixel by pixel based on the measured remission signal. Here a characteristic curve or a characteristic field for authenticity detection is defined, as in Figure 3 shown.

Figure 3 shows a characteristic curve for the threshold values for classification at the pixel level. The presence of a document is recognized for reflectance values R above a reflectance threshold R ₁ . This threshold can be set uniformly for all tracks or, preferably, parameterized individually for each track based on reference measurement values for white or black samples.

If a very dark area is registered on the document, a reduced threshold value for the feature intensity M is also applied (M ₁ > M). If there are correspondingly brighter areas (R ₁ <R <R ₂ ), the required feature intensity threshold is preferably increased accordingly between M ₁ and M ₄ . In particularly strongly reflective areas (R > R ₂ ), it can be assumed that there is no normal securities substrate but rather a metallic reflector such as. B. a hologram, Security strips or something similar. Since these are typically opaque to optical radiation, the threshold value for the feature signal is correspondingly reduced to M ₃ , since the covered areas may only be able to provide a greatly reduced signal contribution. If the spatial resolution of the feature sensor is not significantly higher than the dimensions of the opaque structures, masking will not occur digitally but will usually occur partially. This is taken into account by gradually reducing the feature threshold between M ₄ and M ₃ in the range R ₂ < R < R ₃ . In order to ensure strong detection of counterfeits, a minimum of feature signal M ₂ can be required even with very high reflectance values R > R ₃ . For a particularly strict evaluation, M ₂ = M ₃ can also be chosen. In these classification variants, a hologram strip is marked in “red”. Alternatively, M ₂ can also be parameterized to very low values, which results in a classification of reflective hologram strips as "green".

Due to an only partial overlap between the value document and the measurement pixel, "red" pixels can randomly appear at the edge of the value document, which must be treated or tolerated separately in the further evaluation. Alternatively, the creation of these red edge pixels can be prevented by appropriately parameterizing the threshold value characteristic for R ₁ or M ₁ . Here, M ₁ (relative to the maximum intensity) is set lower than R ₁ , so that due to the purely geometric loss of intensity, which affects both remission and feature intensity equally, the situation cannot arise that R > R ₁ but already M <M ₁ is.

If several independently measurable characteristic substances are present, the characteristic measured values can of course be evaluated both individually and in combination, analogous to the case described in the case without reflectance measurement.

Pixel completeness:

The first check for completeness is now carried out on a pixel basis: within the recognized area of the value document, the number of measuring points or pixels classified as "red" must not exceed a certain threshold. In the strictest interpretation with a threshold of 0, this means that there must not be a single measurement location with insufficient feature intensity for the document of value to be recognized as complete. In other variants, individual "red" pixels can be tolerated.

Here again the ratio of the number of all green pixels relative to the number of all pixels within the extent of the value document can be formed and checked against a minimum threshold. This corresponds to an area share or the area-related degree of completeness.

Trace completeness:

The trace lengths determined from the reflectance measurements are used as a benchmark for evaluating trace completeness. To calculate the track completeness measure, the number of pixels classified as "green" in this track is divided by the number of all pixels within this track length. Alternatively, a somewhat stricter test criterion is obtained if, in order to calculate the measure of track completeness, the number of pixels classified as "green" in this track is divided by the number of pixels corresponding to the maximum length of the value document.

Another test criterion is the number of neighboring "red" pixels within the length of the value document and within a track. If this exceeds a defined threshold, the track is counted as incomplete. To parameterize this threshold, it makes sense to use the maximum width of "red" areas that appear in real value documents, such as: B. the maximum extent of hologram patches or similar is taken into account.

Analogous to the procedure described above for determining the trace completeness without reflectance measurement, measurement traces in the edge position can also be evaluated differently from central traces, although the corresponding positional uncertainties are much lower here due to the reflectance measurement.

Two-dimensional completeness:

In the preferred case that the sensor has several measurement tracks, the two-dimensional distribution of the feature intensity or the two-dimensional distribution of the classified pixels is also evaluated here, as already described above.

Using the convex shell around the pixels with above-threshold remission, holes or opaque spots within the document of value can be localized. The occurrence of larger holes is specifically checked. For this purpose, "red" subthreshold neighboring pixels are searched for within the extent of the value document determined by the convex shell and two-dimensionally connected areas are counted and identified/marked. Lying e.g. For example, if there are more than 2, 3, 5, ... (depending on the resolution) connected red pixels, a potentially missing area is recognized. The location and geometric extent of the "red" areas are analyzed and compared to patterns that occur in a known manner, such as: B. compared to a transparent window or a metallic hologram strip. In particular, the shape, maximum width and relative position to the edges or corners of the document of value are checked for plausibility and, if there are deviations, classified as "incomplete".

Here too, an assessment of column completeness can be carried out to efficiently detect counterfeits or incomplete documents of value with vertical manipulation structures. Here the number of red pixels is determined column by column and compared against a threshold value. If this threshold (e.g. 2 or 3) is exceeded in a column, the value document will be rejected as incomplete.

For those classes of counterfeits in which sections of the genuine document of value have been replaced in the edge area by, for example, a photocopy, the combined evaluation of the remission and the intensity of the feature creates a real qualitative advantage: By precisely determining the actual extent of the document of value, these counterfeits can now be reliably identified . In particular, a targeted check can be carried out for the presence of edge columns classified as “red” (which were determined by counting the red pixels in the column direction). The outermost two columns are preferably evaluated in order not to over-evaluate or incorrectly evaluate the red edge pixels that occur randomly due to edge effects.

In an embodiment with very different resolution of the measurement in the (x) track direction and y direction (number of tracks), this is taken into account by specifically "red" neighboring pixels in the line direction are counted and multiple pixels are rated as particularly serious in this direction. In particular, the maximum width of a hologram strip (or similar security features such as metal paint) can be taken into account by directly classifying valuable documents with a larger number of red pixels in the higher-resolution measuring direction as incomplete than a defined threshold value.

Double-sided measurement

In particularly preferred variants, the authenticity sensor comprises two sub-sensors that allow the feature intensity on each document of value to be measured on both sides. A reflection channel is preferably available at least on one side - or particularly preferably on both sides - with which the (track) length as well as the exact position and orientation of the document of value are determined.

In one embodiment, the two sub-sensors are controlled centrally in order to synchronize the timing of the excitation or measured value recording for both sub-sensors. Alternatively, two individual, independent sensors are used for the front and rear, which are synchronized in a master/slave configuration by one of the two sensors ("master"). For example, this master sensor determines the operating mode and specifies time delays to be maintained for the measurement pulses and/or measurement recording after a trigger signal.

Furthermore, different sensor architectures can preferably be used for the master or slave sensor. For example, one of the sensors can use more complex measurement technology than the other sensor be equipped and check the characteristic values with a higher accuracy or a higher spectral resolution.

The two partial measurements from the front and back are then evaluated in combination. The measurement data is assigned to the respective measurement locations on the value document, the location-related data tuples from (Remission, Feature1, Feature2) or (Remission1, Remission2, Feature1, Feature2) are formed and evaluated.

The position or timing of the two measurements (front, back) is preferably coordinated with one another so that the document of value is measured at the same pixel positions on the front and back. Particularly preferably, the measurement takes place (almost) simultaneously, i.e. a measuring point at a location on the document of value is recorded almost simultaneously from the front and from the back.

In addition to the simpler and clearer evaluation of the measured values obtained in this way, this offers the advantage that crosstalk between the front and back measurements, which can usually not be prevented, does not lead to artifacts and interference signals, but instead amplifies the feature signal to be measured.

The illumination of the first partial sensor can also advantageously be used for a transmission measurement with the detector part of the second partial sensor if the two illumination light pulses have a small time offset, so that the transmission signal can be recorded separately in time from the remission signal 2. This temporal sequence of light pulses or detections is in Fig. 3 shown schematically. In this case, Transmission, Remission1, Remission2 stand for each measuring location as well as feature1, feature2 are available as a database for the completeness assessment. This enables a complete completeness assessment even with existing opaque (metallic) or transparent (window) security features, which could otherwise hinder the completeness check of certain parts of the value document.

The illumination for the reflectance measurement (alternatively: feature measurement) of the front and back is carried out with a slight time delay, so that detector 2 can determine the transmitted portion of the illumination 1 independently and undisturbed by the illumination 2, as shown in Figure 3 is shown.

In the simplest case, the sum (or the average or the maximum) of feature 1 and feature 2 is formed at each measurement location and then classified and evaluated according to the processes described above.

A more accurate evaluation is achieved when individual thresholds are applied for Feature1 and Feature2. These can depend on remission as well as on the other characteristic value. In place of the characteristic curve just described for pixel-by-pixel red/green evaluation, a corresponding characteristic diagram is used here. This can be precisely adapted/parameterized to the typical optical effects that occur on real documents of value.

The Figures 5a and 5b show a map for the threshold values for classification at the pixel level when measuring features on both sides. In the Figure 5a Classification takes place based on static threshold values of feature values (M _1,min , M _2,min ). With the map in Figure 5b one occurs Classification taking into account interaction effects, such as B. Reflection on metallic surface structures applied on one side.

For example, if a (reflective, therefore opaque) metallic strip is applied to one side of a banknote B1, it is to be expected that the feature value 1 is very low on one side, but the expected feature value 2 is due to the reflections that occur compared to the immediate surroundings (or opposite the average over the entire banknote). This can be represented by the appropriate parameterization of the threshold map. Conversely, when overprinting on side B1 with black, spectrally broadband absorbing (soot) ink, the reflectance value and feature value1 are low, whereas feature value2 is at a normal level.

The parameterization of the classifier advantageously depends on the location, i.e. H. e.g. B. relative to the front edge, relative to the corners, or specific position within the convex hull, etc. This allows correct treatment of absorptive and reflective interference depending on the effects that may occur in these areas (depending on the position and denomination). In both cases, the corresponding area can be reliably assessed as genuine by measuring the features on both sides despite the insufficient feature intensity on one side.

This allows complete proof of completeness regardless of the BN design, even in difficult situations with (one-sided) covers/shading caused by opaque elements such as aluminum-coated hologram strips. This means that areas of the value document can also be reliably checked for completeness/authenticity that cannot be assessed with only one-sided measurement.

In a preferred variant, the complete existing data set from (transmission, remission1, remission2, feature intensity1, feature intensity2) is combined and classified and evaluated. In addition to areas with opaque, absorbing or reflective coverings, holes or window areas in particular can be reliably identified via the transmission signal and their position and extent can be checked in comparison to the values permissible for genuine documents of value. Further exemplary embodiments are described below.

Example 1: Here a spectrally resolving single-track luminescence sensor with reflectance measurement is used for the completeness check. The sensor is operated on a banknote processing machine at a transport speed of 11 m/s and is used to check the authenticity and completeness of banknotes with a luminescence marker inserted into the paper that is coordinated with the luminescence sensor. The banknotes have a reflective hologram stripe on the front in the right area.

Figure 6 shows a feature curve (O), a reflectance curve (x) and the dynamically calculated feature threshold (dashed) of a genuine and complete banknote. Both remission and feature intensity are significantly modulated. By using a remission-dependent threshold when classifying the feature intensity, completeness can still be determined correctly.

Example 2: Here, a spectrally resolving 11-track luminescence sensor with reflectance measurement is used for the completeness check. The sensor is installed on a banknote processing machine at a transport speed of 11 m/s operated and used to check the authenticity and completeness of banknotes with a luminescent marker inserted into the paper. The banknotes have a reflective hologram stripe on the right side of the front and a transparent window on the left side.

Figure 7 shows a representation of the measured reflectance values of the banknote. High reflectance occurs particularly in the area of the reflective hologram strip, while very low reflectance occurs in the transparent window.

Figure 8 shows a representation of the feature intensity of the banknote. White corresponds to high intensity, while black corresponds to low intensity. In the area of the window (left) and the hologram strip (right) only very low feature intensity can be detected.

Example 3:

For comparison, appropriately prepared snippet fakes with approx. 10% fake content were measured.

Figure 9 shows a representation of the feature intensity of an incomplete banknote with a diagonally inserted strip of a copy without a feature.

Figure 10a shows a pixel-by-pixel classification of the banknote ( Fig. 7-8 ) with dynamic threshold. The low feature intensity in the area of the hologram strip could be taken into account by the dynamic threshold, while the lack of feature intensity in the window area was not possible Remission signal is marked red. (0 = black, 1 = red, 2 = yellow, 3 = green)

Figure 10b shows a pixel-by-pixel classification of the incomplete banknote ( Fig. 9 ) with dynamic threshold. The low feature intensity in the area of the hologram strip could be corrected by the dynamic threshold, while the missing feature intensity in the window area is marked in red due to a lack of remission signal. The missing feature area is correctly recognized and also marked in red. (0 = black, 1 = red, 2 = yellow, 3 = green)

Example 4:

The banknote of Fig. 7-8 was measured again with a sensor setup with measurement on both sides. Feature1 (front), Feature2 (rear), Remission1 (front), Remission2 (rear) and the transmission were measured.

Figure 11 shows transmission data of the banknote

To classify the measurement pixels, the front and back were classified separately using a dynamic feature threshold and then combined separately according to the following assignment of the class assignments determined on the front (classification 1) and back (classification 2) to form an overall classification for each pixel, as in Figure 12 is shown.

The window area was then recognized based on the high transmission >85 and classified accordingly as a “window” (4).

Figure 12 shows a pixel-by-pixel classification of the measurement data determined on both sides of the complete test banknote with dynamic threshold and transmission measurement. (0 = black, 1 = red, 2 = yellow, 3 = green, 4 = light blue) Despite the technically difficult architecture of the banknote with metallic reflective and transparent window areas, all areas are reliably checked for local authenticity and completeness is correctly assessed.

In the Figure 13 A combination of characteristic values classified on both sides is shown schematically, after which the value document or banknote is also assessed for authenticity and/or completeness.

Claims (20)

1. A method for checking the completeness and/or authenticity of documents of value, wherein at least one document of value comprises at least one machine-readable characteristic material at at least two locations, having the steps of:

   - at least local excitation of the document of value (S2)

   - spatially resolved detection of a characteristic intensity (M) with respect to the machine-readable characteristic material at several different locations of the document of value (S3a);

   - location-based classification of the location-based characteristic intensities by means of a threshold value (S5);

   - determining location-based limits of an expected location distribution of the machine-readable characteristic material (S6); and

   - determining a completeness measure for the entire document of value based on an evaluation of a location-based distribution of the classified characteristic intensities (S7), wherein the evaluation is performed with respect to a relative location of classified pixels relative below a threshold to the determined limits of the expected location distribution of the machine-readable characteristic material.
2. Method according to claim 1, characterized in that the classification (S5) of the location-based characteristic intensities (M) is carried out based on location-dependent threshold values.
3. The method according to any of the preceding claims, characterized by the step of spatially resolved detection of remission values (R) (S3b) at multiple different locations of the document of value.
4. The method according to claim 3, characterized in that the threshold value is configured as a location-dependent threshold value determined from a characteristic curve dependent on the remission value (R) determined at the respective location.
5. The method according to claim 3 or 4, wherein the measurement locations of the remission values (R) overlap with the measurement locations of the characteristic intensities (M) and are preferably identical.
6. The method according to any of the preceding claims, characterized by the step of calculating a track completeness by comparing the number of measurement locations with the characteristic intensity (M) above a threshold with the number of detected measurement locations within a convex envelope of the measurement locations with a characteristic intensity (M) above a threshold or, if applicable, within a convex envelope of the measurement locations with a remission value (R) above the threshold.
7. The method according to any of the preceding claims, characterized by the step of testing a two-dimensional distribution of the classified characteristic intensities (M) relative to a convex envelope of the measurement locations with a characteristic intensity (M) above a threshold or if remission values (R) have been detected, relative to a two-dimensional distribution of the measured values of the remission measurement, wherein preferably the testing of the two-dimensional distribution of the classified measurement locations comprises a calculation of a column completeness.
8. The method according to any preceding claim, characterized in that the characteristic intensities (M) are detected along at least one measurement track on the document of value.
9. The method according to any of the preceding claims, characterized in that the spatially resolved detection of the characteristic intensities (M) (S2) with respect to the machine-readable characteristic material comprises the measurement of a spectral luminescence intensity of a luminescence material and/or the spectral measurement of a Raman band of a Raman-active material and/or a material detectable by surface-enhanced Raman spectroscopy (SERS) and/or the spectral measurement of an absorption band of a substance absorbing in the infrared spectral range and/or the measurement of the magnetic properties of a ferromagnetic material.
10. The method according to any one of the preceding claims, characterized in that a local authenticity of the document of value is tested by means of at least one characteristic value (M).
11. The method according to any one of the preceding claims, characterized in that a number and a spatial distribution of classified measurement locations below a threshold is compared with reference values.
12. The method according to any one of the preceding claims, characterized in that the document of value is moved at a speed of 1-13 m/s, preferably 6-12 m/s, during the measurement.
13. The method according to any one of the preceding claims, characterized in that the spatially resolved detection of the characteristic intensities (M) of the machine-readable characteristic material and/or, if applicable, of the remission values (R) takes place on the front and rear of the document of value, in particular at the same, opposite locations of the front and rear.
14. Method according to claim 13, characterized in that the location-dependent threshold values are determined by a characteristic curve which depends on the characteristic intensity (M) determined on the opposite side of the document of value at the respective location.
15. The method according to any one of the preceding claims, characterized in that spatially resolved transmission values of the document of value are detected.
16. The method according to claim 15, characterized in that the transmission measurement is performed by means of time-delayed illumination within the scope of remission measurements on the front and rear side and/or by means of time-delayed illumination within the scope of the measurement of characteristic values on the front and rear side.
17. The method according to any one of the preceding claims, characterized in that, at several measurement locations, a combined classification is performed in consideration of the data tuple associated with the measurement locations, wherein the data tuple comprises at least one characteristic intensity (M) as well as at least one of the following components: a further characteristic intensity (M), a remission value (R), and/or a transmission value.
18. A sensor for detecting a characteristic intensity (M) and/or a characteristic value, wherein the sensor is configured to perform a method according to any of claims 1 to 17.
19. A sensor unit having a sensor, wherein the sensor is configured to detect at least one of a characteristic intensity (M), a characteristic value, a remission value (R) and/or a transmission value, in particular according to claim 18, and wherein the sensor unit is configured to perform a method according to any of claims 1 to 17.
20. A banknote processing machine having a sensor unit according to claim 19, a sensor according to claim 18, and/or configured to perform a method according to any of claims 1 to 17.

Images