EP3400584A1 - Vérification de l'intégrité d'un document de valeur - Google Patents

Vérification de l'intégrité d'un document de valeur

Info

Publication number
EP3400584A1
EP3400584A1 EP16819440.5A EP16819440A EP3400584A1 EP 3400584 A1 EP3400584 A1 EP 3400584A1 EP 16819440 A EP16819440 A EP 16819440A EP 3400584 A1 EP3400584 A1 EP 3400584A1
Authority
EP
European Patent Office
Prior art keywords
feature
value
measurement
location
remission
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP16819440.5A
Other languages
German (de)
English (en)
Other versions
EP3400584B1 (fr
Inventor
Wolfgang Rauscher
Erich KERST
Thomas Happ
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Giesecke and Devrient Currency Technology GmbH
Original Assignee
Giesecke and Devrient Currency Technology GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Giesecke and Devrient Currency Technology GmbH filed Critical Giesecke and Devrient Currency Technology GmbH
Publication of EP3400584A1 publication Critical patent/EP3400584A1/fr
Application granted granted Critical
Publication of EP3400584B1 publication Critical patent/EP3400584B1/fr
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/06—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency using wave or particle radiation
    • G07D7/12—Visible light, infrared or ultraviolet radiation
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/06—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency using wave or particle radiation
    • G07D7/12—Visible light, infrared or ultraviolet radiation
    • G07D7/1205—Testing spectral properties
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/16—Testing the dimensions
    • G07D7/162—Length or width
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/20—Testing patterns thereon
    • G07D7/2016—Testing patterns thereon using feature extraction, e.g. segmentation, edge detection or Hough-transformation
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/20—Testing patterns thereon
    • G07D7/202—Testing patterns thereon using pattern matching
    • G07D7/205—Matching spectral properties
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B42—BOOKBINDING; ALBUMS; FILES; SPECIAL PRINTED MATTER
    • B42D—BOOKS; BOOK COVERS; LOOSE LEAVES; PRINTED MATTER CHARACTERISED BY IDENTIFICATION OR SECURITY FEATURES; PRINTED MATTER OF SPECIAL FORMAT OR STYLE NOT OTHERWISE PROVIDED FOR; DEVICES FOR USE THEREWITH AND NOT OTHERWISE PROVIDED FOR; MOVABLE-STRIP WRITING OR READING APPARATUS
    • B42D25/00—Information-bearing cards or sheet-like structures characterised by identification or security features; Manufacture thereof
    • B42D25/20—Information-bearing cards or sheet-like structures characterised by identification or security features; Manufacture thereof characterised by a particular use or purpose
    • B42D25/29—Securities; Bank notes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B42—BOOKBINDING; ALBUMS; FILES; SPECIAL PRINTED MATTER
    • B42D—BOOKS; BOOK COVERS; LOOSE LEAVES; PRINTED MATTER CHARACTERISED BY IDENTIFICATION OR SECURITY FEATURES; PRINTED MATTER OF SPECIAL FORMAT OR STYLE NOT OTHERWISE PROVIDED FOR; DEVICES FOR USE THEREWITH AND NOT OTHERWISE PROVIDED FOR; MOVABLE-STRIP WRITING OR READING APPARATUS
    • B42D25/00—Information-bearing cards or sheet-like structures characterised by identification or security features; Manufacture thereof
    • B42D25/30—Identification or security features, e.g. for preventing forgery
    • B42D25/328—Diffraction gratings; Holograms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B42—BOOKBINDING; ALBUMS; FILES; SPECIAL PRINTED MATTER
    • B42D—BOOKS; BOOK COVERS; LOOSE LEAVES; PRINTED MATTER CHARACTERISED BY IDENTIFICATION OR SECURITY FEATURES; PRINTED MATTER OF SPECIAL FORMAT OR STYLE NOT OTHERWISE PROVIDED FOR; DEVICES FOR USE THEREWITH AND NOT OTHERWISE PROVIDED FOR; MOVABLE-STRIP WRITING OR READING APPARATUS
    • B42D25/00—Information-bearing cards or sheet-like structures characterised by identification or security features; Manufacture thereof
    • B42D25/30—Identification or security features, e.g. for preventing forgery
    • B42D25/36—Identification or security features, e.g. for preventing forgery comprising special materials
    • B42D25/373—Metallic materials
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D2207/00—Paper-money testing devices

Definitions

  • the present invention relates to a method and a corresponding device for checking value documents for completeness and authenticity.
  • Forgeries of value documents could be composed of a plurality of sub-documents for which e.g. Sections of true value documents were combined with sections of copies. According to the invention, it is possible to identify such counterfeits safely and to check value dummies for their completeness or authenticity.
  • DE 197 14 519 A1 teaches scanning a document to be tested over the entire surface or along a defined measurement 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 given by the pattern of the marker printed on the marker substance.
  • the general presence of the feature substance jumps in its distribution as well as areas deviating from the expected reference distribution are examined.
  • DE 10 346 636 A1 describes a sensor-based authenticity check of value documents with a luminescence marker, which takes place integrally along a track across the value document. While the addition of the luminescence signal along the measuring track is well suited for the detection of small, noisy spectral signals, this just prevents a small-scale and therefore precise evaluation of the completeness.
  • WO 2011/037750 A2 describes the authenticity recognition of bank notes by detecting a homogeneously distributed IR luminescent substance Measuring tracks and adjustment of the measured modulation of the luminescence intensity by overprinting or applied holograms, stripes, etc. with expected target profiles. In this case, areas with high statistical fluctuation, such as eg security thread or hologram stripes, are excluded from the evaluation and an authenticity decision is made if, for example,> 51% of the measured profile coincides with one of the four position-dependent authenticity references.
  • the achievable spatial resolution of the feature signal can be dramatically reduced in comparison to conventional resolutions of optical image sensors in the visible: the spatial resolution can be determined both by the detector technology used and by intrinsic time characteristics of the feature substance, such as e.g. be limited to the onset of a luminescent. Especially with track-bound 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 as reliably as possible in such situations from the feature measurement, the information of each individual measuring pixel must be adequately evaluated and used for the completeness check.
  • the present invention aims to provide a reliable completeness measurement or completeness check of modern value documents for the detection of so-called snippet counterfeits under the conditions of fast-running banknote processing machines (ie measurement at relative speeds of, for example, 1-13 m / s, preferably 6-12 m / s between banknote and sensor).
  • fast-running banknote processing machines ie measurement at relative speeds of, for example, 1-13 m / s, preferably 6-12 m / s between banknote and sensor.
  • the combination of the most diverse security features on the value document and their interaction with the sensor-based measurement of the machine-readable feature also occurs in the case of homogeneous machine-readable features. Often malstoff a complex modulation pattern of the measured intensity of the feature signal on. This makes the direct completeness assessment considerably more difficult. In this case, it is assumed, in particular, from the case frequently encountered in reality that there is no denomination or position information at the time of the measurement or the completeness assessment.
  • At least one value document has at least one machine-readable feature at at least two different locations.
  • the value document is at least locally stimulated. This can be done for example by means of electromagnetic radiation, for example light having a wavelength in the visible spectral range.
  • a magnetic loading of the value document can take place.
  • a feature intensity with respect to the machine-readable feature substance is detected at a plurality of different locations of the value document.
  • an optical and / or magnetic detection unit can be used.
  • the registration unit forms Add a characteristic value to each measuring location.
  • the feature intensity is determined location-related to the measurement locations of the value document.
  • the localized surface element of the location-related feature intensity and / or the location-related remission value can subsequently be understood as a pixel.
  • a feature value or a plurality of, in particular contiguous, feature values can be used.
  • a partial aspect of the feature value for example a specific wavelength when detecting a spectral range, can be used to generate or capture the feature intensity.
  • the location-related feature intensities are classified based on a threshold value.
  • a location-related classification of the feature intensity can be carried out for each location-related feature intensity, for example in locally true or locally unreal.
  • location-related limits of an expected spatial distribution of the machine-readable feature substance are determined. These limits preferably reflect the longitudinal extent and / or width extent, particularly preferably the area extent, of the value document. Furthermore, based on the limits, errors in the structure, for example falsifications of a value document, in particular when falling below a minimum length, can be determined.
  • the location-related distribution of the classified feature intensities is evaluated.
  • at least two classified feature intensities are evaluated in relation to one another and / or to the specific location-related limits.
  • the feature intensities at the individual measurement locations are preferably given with suprathreshold intensity as locally true or with subliminal intensities. classified as locally spurious. These measuring locations are also referred to below as classified pixels.
  • a reference feature intensity can be used as comparison value.
  • Value documents are sheet-like objects with a front and a back understood that represent, for example, a monetary value or an authorization and therefore should not be arbitrarily produced by unauthorized persons. They therefore have features which are not easy to produce, in particular to be copied, whose existence is an indication of the authenticity, i. the manufacture by an authorized agency. Important examples of such value documents are coupons, vouchers, checks and in particular banknotes.
  • At least one location or pixel is assigned a specific location-dependent threshold value.
  • a plurality of locations or pixels or a group thereof is assigned a location-dependent threshold value. Based on location-dependent thresholds, a more detailed location-dependent classification of the location-related feature intensities is possible. In particular, special the location-dependent properties are represented and checked by the location-dependent threshold values.
  • the local authenticity is determined on the basis of a feature intensity.
  • a plurality of feature values for example of a luminescence radiation, are used for the assessment of the local authenticity.
  • a specific comparison of the determined feature values with the expected feature values of a genuine value document can be carried out and taken into account when determining the feature intensity.
  • the feature intensity may be e.g. are determined to be zero if the spectral distribution of the luminescent radiation does not match the expected spectrum.
  • a local authenticity of the value document can be determined.
  • an evaluation of the entire value document for completeness and / or authenticity is possible.
  • remittance values are recorded spatially resolved at a plurality of different locations of the value document.
  • the measurement locations of the remission values preferably correspond essentially to the measurement locations of the feature intensities.
  • the area of the measurement location of a remission value may be larger or smaller than the area of the measurement location of the corresponding feature intensity.
  • the measuring locations an equal area on.
  • the measuring location of a remission value can also be offset, preferably designed to overlap the measuring location of a feature intensity.
  • the spatially resolved detection of the remission values is preferably carried out simultaneously with the spatially resolved detection of the feature intensity values.
  • the acquired location-related remission values may, according to one embodiment, be parameters of a characteristic for location-dependent or location-related threshold values.
  • the track integrity is determined.
  • characteristic values i. location-related feature intensities and, where appropriate, location-related remission values along a measurement track on the value document are detected and taken into account for determining the track completeness.
  • the data of this measuring track are evaluated by using the invention as a single-track sensor. Essentially, however, only one-dimensional track completion can then be tested and evaluated. For this purpose, first the individual feature intensities and, if appropriate, remission values of the pixels are individually compared with a minimum value and classified in real or spurious, or corresponding, for example, as subliminal, suprathreshold or average. The length of the value document can be determined from the distance between the two outermost measuring points with a signal intensity above a minimum threshold.
  • the measured length of the value document can preferably be determined in a transport direction by an edge detection from a feature input.
  • intensity curve are determined.
  • the feature intensity curve spatially resolved feature intensities are recorded. That is, the feature intensity curve includes value points resulting from the feature intensity and the associated location.
  • the extreme spatial positions are determined at which an averaged threshold (upper threshold - lower threshold) / 2 of the feature intensities is reached.
  • the difference between the two location-related feature intensities results in the measured length of the value document, whereby the size of the pixel or location of the location is usually taken into account, in particular added.
  • 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 subpixel-exactly.
  • the determined length is then compared to a known minimum length, e.g. corresponds to the true length of the shortest denomination variant of a banknote series.
  • the completeness can be determined quantitatively simply by the ratio of the number of pixels with suprathreshold feature intensity, or real pixel to the number of pixels corresponding to the measured length (assuming a constant transport speed or
  • the measurement of the feature sensor is triggered so that the measurement points of different value documents always different
  • a sensor has a plurality of measuring tracks, such as, for example, a sensor. B. 2, 4, 6, 8, 10, 20 or more measuring tracks, so that a two-dimensional distribution of the feature intensities is recorded.
  • a threshold value classification of the individual location-related feature intensities is initially also carried out.
  • a convex hull is calculated around the determined suprathreshold location-related feature intensities.
  • known or predetermined location-related subliminal feature intensities for example by a background system, are compared with the determined location-related suprathreshold feature intensities covered by the convex hull, for example a number.
  • the evaluation can be carried out at subliminal location-related feature intensities. Furthermore, an evaluation with respect to possibly detected and known or predetermined remission values is possible.
  • the convex hull is calculated separately for each row, i. in this case, the interval between the front and back of the row. For each interval, subliminal location-related feature intensities are marked as spurious or corresponding.
  • a two-dimensional convex hull is calculated over the locations of all the suprathreshold pixels, e.g. with the Graham algorithm. For example, all subliminal pixels with locations within the convex hull are marked as spurious or equivalent.
  • subliminal location-related feature intensities within the convex hull may be rejected if, for example, their measurement locations fall within occurring patterns, such as e.g. a transparent window or a metal LEAD strip lie.
  • the two-dimensional distribution of the suprathreshold pixels is preferably analyzed and evaluated.
  • location-related feature intensities classified as incorrect or correspondingly are determined and identified, marked and counted in two-dimensional contiguous areas. Lying z. If, for example, more than 2, 3, 5,... (Depending on the resolution) are associated with location-related feature intensities that are associated with each other as spurious or correspondingly, then a potentially missing area is recognized. Subsequently, the position and geometric extent of the regions classified as spurious or correspondingly are analyzed and compared with patterns that occur in a known manner, such as, for example, a transparent window or a metallic LEAD strip.
  • the shape, maximum width and relative position to the edges or corners of the value document is checked for plausibility and classified in case of deviations as "incomplete” or similar.
  • the individual tracks are evaluated analogously to the single-track sensor described above, which provides several measurements for the track completeness.
  • the authenticity and / or completeness of the value document is recognized if there is at least one specific relationship between the number and a spatial distribution of the classified pixels, feature intensities and / or remission values.
  • the measured length can be determined from the maximum value of the individual determined track lengths and, in the case of deviations of the other track lengths, preferably closed taking into account a tolerance value for the presence of missing completeness.
  • a complete column can be determined, in particular by taking into account a plurality of measuring tracks.
  • the spatially resolved detection of the feature intensities may comprise the measurement of a spectral luminescence intensity of a luminescent substance. Accordingly, the value document can
  • the spatially resolved detection can comprise a spectral measurement of a Raman band and / or a so-called surface-enhanced Raman spectroscopy (SERS).
  • the detection may comprise a measurement of an absorption band with respect to a specific spectral range, for example infrared and / or the measurement according to magnetic properties.
  • Feature values may include measurement results, for example with respect to a spectrum.
  • the feature values are specifically processed to provide feature intensities.
  • the characteristic values can be used with a filter, for example for
  • the characteristic values can be assigned an algorithm.
  • the feature values may be sensor values from which feature intensities are eventually determined.
  • the characteristic values each may comprise a plurality of different feature intensities.
  • feature intensities and / or, if appropriate, remission values can take place both on a front side and on a back side of the value document.
  • feature intensities and / or, if appropriate, remission values can be detected on the same and / or opposite side, in particular with respect to the measurement location.
  • the feature intensities and / or, if appropriate, remission values are recorded at the same, opposite locations of the front and back sides.
  • location-dependent threshold values can be determined by a characteristic which depends on the feature intensities determined at the respective location on the opposite side of the value document
  • transmission values are detected spatially resolved, preferably by time-shifted illumination in the context of remission measurements on the front and back and / or by time-shifted illumination in the context of the measurement of feature values or the detection of feature intensities on the front and back of the value document.
  • a combined classification taking into account data tuples assigned to the measurement locations can be undertaken at a plurality of measurement locations.
  • the data tuples comprise 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.
  • a sensor or a sensor unit and / or a bank-note processing machine which are designed to carry out a method set forth here.
  • the sensor may be part of the sensor unit and / or the bank-note processing machine.
  • the local excitation of the value document, in particular of the feature substance preferably takes place with the aid of an excitation radiation.
  • the feature substance preferably has a luminescent substance or a Raman-active substance or a substance which can be detected by surface-enhanced Raman spectroscopy (SERS).
  • SERS surface-enhanced Raman spectroscopy
  • the feature substance may have magnetic properties.
  • every feature substance with machine-testable properties is conceivable.
  • the feature substance can also be regarded as a marker.
  • the excitation radiation can be spectrally narrowband, broadband, or a superimposition of different narrowband and / or broadband radiation components.
  • the value document is illuminated with a test radiation for checking the presence of a document substrate at the respective measuring point, for size measurement of the value document and / or for measuring a remission value.
  • the excitation radiation and / or the test radiation are measured in a spatially resolved manner in accordance with an embodiment of the invention.
  • the value documents to be checked for completeness in the context of this invention are equipped with at least one machine-readable feature substance which has been introduced or applied at least along a track in the direction of movement of the value document.
  • the machine-readable feature preferably comprises at least one luminescent marker (luminescent substance), more preferably inorganic phosphors based on rare earth or transition metal ion-doped host lattices.
  • the machine-readable feature substance is preferably distributed homogeneously over the surface of the value document or homogeneously into the volume of the document
  • Value document paper or polymer introduced.
  • it may be printed over the entire surface or in subregions of the value document, but at least along a track over the length, or in the case of a transverse transport over the width of the document.
  • this can either be used at shorter wavelength (anti-Stokes wavelength).
  • Luminescence or Upconverter and / or at a longer wavelength than the excitation wavelength (Stokes luminescence) emit.
  • Anti-Stokes emitters are not preferred since they typically have a significantly lower brightness.
  • at least two independently measurable feature substances are present in the value document, which are either spatially or equally distributed. This can be, for example, two independent feature substances introduced into the substrate of the value document (polymer or paper). Alternatively, a feature substance may be present in the substrate and a second feature substance may be printed.
  • a suitable sensor for the machine-readable feature is required.
  • a luminescent feature or a SERS feature it 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 value documents, in particular a bank note processing machine. This transports the value documents to be tested linearly through the detection range of the sensor device in a predetermined transport direction.
  • the feature sensor may include a luminescence sensor.
  • the luminescence sensor is preferably used as a detection device for the spectrally resolved detection of the luminescence radiation in at least one
  • the spectral resolution can be determined either by dispersive elements, such. B. diffraction gratings in reflection or transmission or by suitable filters in front of the respective detector elements can be achieved.
  • the spectral resolution of the detector has at least two wavelength channels, preferably> 4, more preferably> 8 different wavelength channels.
  • the sensor illuminates it in a detection area with an excitation radiation. This is tuned to the luminescent substance used to mark the value document and is in the optical range, ie in the UV, VIS or IR spectral range.
  • the excitation radiation can be spectrally narrowband, broadband or a superposition of different narrowband and / or broadband radiation components.
  • the luminescence sensor is preferably additionally equipped with a reflectance sensor. Here it illuminates the document of value in addition to the excitation radiation with a test radiation. This serves to check for the presence of the document substrate at the momentarily illuminated location or the size measurement of the value document and / or the measurement of the remission.
  • the test radiation preferably has a spectral distribution which coincides with the spectral detection range of the
  • Detection device at least partially or completely overlapped. In this case, the remission of the value document can be determined directly without the need for a separate detector.
  • the luminescence in addition to spatially resolved remission is measured and the geometric imaging properties of the two Detection channels each associated with the associated measurement locations of the luminescence.
  • the illumination surfaces of the excitation radiation and of the test radiation preferably overlap 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.
  • the senor has a control and evaluation device which controls the emission of excitation radiation or test radiation and receives the signals of the detection device (s), processes them and evaluates them with regard to authenticity or completeness.
  • test radiation and the excitation radiation are irradiated with suitable light sources, e.g. Incandescent lamps, flash lamps, LEDs or laser diodes, in particular edge emitter or VCSEL generated. Possibly.
  • suitable light sources e.g. Incandescent lamps, flash lamps, LEDs or laser diodes, in particular edge emitter or VCSEL generated.
  • filters or phosphor converters are required to produce the desired spectra.
  • the remission is typically determined in the visible spectral range either in a broad or alternatively narrowly narrowed wavelength range. Alternatively, the remission may also be in a non-visible spectral region such as e.g. be determined in UV or NIR.
  • the luminescence signal obtained during each measurement cycle can be evaluated locally for each individual measurement point.
  • This can include the evaluation of a spectral distribution, for example, after an offset correction or background correction, whereby any signal contributions introduced by scattered light or by the amplifier or evaluation electronics are eliminated.
  • the necessary correction parameters can either be preset or determined dynamically using suitable dark measurements. These can be carried out, for example, if no value document is currently in the detection range of the sensor and / or one (or more) measuring point is "sacrificed" on the value document itself and, instead, a dark measurement is carried out without excitation and without test illumination.
  • the measured spectra can be normalized with pre-set or separately measured illumination intensities or remission values etc. measured on special calibration substrates.
  • the local authenticity of the value document is checked on the basis of the measured luminescence signal. This can be done on the basis of the spectral distribution or additionally evaluate the arrival and / or decay behavior.
  • at least one intensity value is calculated which represents a measure of the local luminescence intensity and together with the measuring location, i. h., z. B. the x-y coordinate formed from tracking and transport position is stored.
  • the remission value is determined in the case of narrow-band test illumination, or the remission values of several spectral channels in the case of spectrally resolved remission measurement.
  • the further evaluation is carried out in two stages: First, the feature values are classified into locations with suprathreshold feature intensity (real or analog) and locations with subliminal feature intensity (spurious or analog). Subsequently, the completeness is determined on the basis of the number and distribution of spatially resolved feature intensities classified as spurious.
  • Case 1 describes an evaluation without denomination information and without remission measurement. In this most difficult case, the sensor merely measures the machine-readable feature without having any further information about the present value document or about its true or apparent size. Thus, only the measurement data distribution of the machine-readable feature is available for the completeness evaluation. Nevertheless, on the basis of this limited information, a well-founded statement of completeness can be made. In reality, counterfeit or incomplete value documents are relatively common in which narrow vertical structures or stripes have been cut out. To be able to recognize this efficiently is one
  • the number of subliminal pixels is determined column by column and compared with a threshold value. If this threshold (of eg 2 or 3) is exceeded in a column, the value document is rejected as incomplete. As a result, these types of counterfeiting are detected particularly effectively with vertically extended manipulations.
  • a different evaluation takes place between the edge tracks and middle tracks. This makes it possible to detect missing measuring ranges, which occur due to a tilting of the value document in the transport, and to reduce the frequency of value documents erroneously classified as incomplete. In one embodiment, for example, track completeness in the evaluation can generally be ignored.
  • the edge track in the shortened form can be evaluated within the extent recognized by the remission measurement.
  • a combination of the convex hulls of the distributions of the two feature material measured values can be used as a measure of the geometric extent of the value document.
  • the completeness check is carried out by a machine-dependent evaluation, in which the actual geometry proportions are taken into account with regard to the transport of value documents.
  • the orientation of the transported value documents can be either along the lower edge or center-centered, for example. This has the consequence that when processing different denominations with different sizes (especially widths) depending on the machine different tracks may expect feature signals. Since these transport properties always remain constant, they are advantageously taken into account for the assessment of completeness and parameterized during installation of the sensor. In particular, it is defined which tracks should always be completely present (center track (s) vs. lowest track or second lowest track for consideration of a skew).
  • both the trace completeness and the area completeness are preferably evaluated and finally combined into a measure for the completeness.
  • an identified lack of trace completeness can lead to the entire value document being recognized as incomplete, even if the area completeness is perhaps still within an accepted tolerance threshold.
  • a particularly reliable evaluation of the completeness is carried out under examination at the level of the pixels (pixel completeness), at the level of the measuring tracks (trace completeness) as well as at evaluation of the two-dimensional distribution of the measured values obtained (surface completeness or two-dimensional completeness).
  • FIG. 1 A schematic representation of an embodiment of a method according to the invention
  • FIG. 2a shows a first diagram according to an embodiment for classifying at the pixel level
  • FIG. 2b shows another diagram according to an embodiment for classifying at the pixel level
  • FIG. 3 is a schematic representation of a characteristic curve for threshold values for classification at the pixel level;
  • Figure 4 A schematic representation of the timing of the illumination for remission or;
  • FIG. 5a a schematic representation for the classification on the pixel level with bilateral feature measurement
  • FIG. 5b shows a schematic representation of a further characteristic for the classification on pixel level with double-sided feature measurement
  • FIG. 6 a curve of feature intensity, remission value and a dynamically determined threshold value for classification at the pixel level
  • FIG. 7 a schematic representation of remission values of a banknote to be checked
  • FIG. 8 shows a schematic representation of feature intensities of a banknote to be checked
  • FIG. 9 shows a schematic representation of feature intensities of an incomplete banknote to be checked
  • FIG. 10 a a schematic representation of location-related distribution of classified feature intensities
  • FIG. 10b a schematic representation of location-related distribution of classified feature intensities of an incomplete banknote
  • FIG. 11 a representation of transmission values of a banknote
  • FIG. 12 another schematic representation of a pixel-by-pixel classification
  • Figure 13 is a schematic representation of a combined classification of feature values.
  • FIG. 1 shows schematically a method sequence for testing a value document according to the invention.
  • a value document is provided.
  • the value document comprises 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 value document.
  • the machine-readable feature substance extends partially over the entire areal extent of the value document.
  • the value document is at least locally excited, preferably with electromagnetic radiation.
  • the stimulation can be done by irradiating the entire value document.
  • an area-wise, particularly preferably a punctual irradiation of the value document takes place.
  • Machine-readable feature substance detected at several different locations of the value document (S3a).
  • the detection usually relates to the surface portion of the document of value which has been excited by means of electromagnetic radiation, wherein preferably the excited portion has an area equal to or greater than the area or point detected.
  • a remission value is detected spatially resolved with respect to the feature values acquired in step 3a (S3b), it also being possible to detect a plurality of remission values, which relate, for example, to different wavelengths.
  • a step S4 the feature values and the preferably detected remission value are evaluated spatially resolved in accordance with the steps S2, S3a and optionally S3b.
  • the feature values are compared with expected reference signals, and a feature intensity is determined in each case for the feature values acquired spatially resolved.
  • a normalization of the location-related feature intensities takes place.
  • 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 as a function of one or different remission values (FIG. 3).
  • step S4 may preferably take place immediately after step S3a, and / or for one or more feature intensities, step S4 may be performed after the detection of the plurality of feature intensities according to S3a.
  • step S5 can preferably take place immediately after step S4 and / or step S5 can be carried out for one or more feature intensities after the evaluation of the plurality of feature intensities according to S.
  • step S6 based on the evaluation from step S4 or alternatively based on the classification of the feature intensities from step S5, a location-related distribution of the feature intensities is determined.
  • expected location-related limits of the distribution of the feature substance are derived. 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 suprathreshold feature intensities, or by taking into account further measured values, in particular the remission values.
  • step S7 the location-related distribution of the classified feature intensities obtained in step S5 is evaluated. The evaluation takes place, in particular, with regard to the relative position of the pixels classified above or below the threshold, and with regard to the relative position of the subliminally classified pixels relative to those determined in S6 Limits of the expected spatial distribution of the machine-readable feature substance.
  • a completeness measure for the entire value document is determined, which is used for authenticity assessment or, for example, for sorting decisions in one
  • Bank note processing machine can be used.
  • FIGS. 2a and 2b respectively show an intensity field for a sampled pixel, wherein the threshold values for feature or remission signals used for classifying at the pixel level are entered by way of example according to one aspect of the present invention.
  • the classification of the pixels in real / non-genuine takes place by way of example with reference to FIG. 1 as follows. To evaluate a value document for authenticity and / or completeness, a pixel-based classification is performed.
  • Value documents are classified "black” while existing areas of the value document (R> Ri), i. h., a sufficiently high remission value is detected, classified without adequate feature signal as suspected of being forged, in particular as snippet forgery, "red”. If there are areas with inadequate remission but with sufficient feature intensity, these are classified as a "feature excess” "yellow”. This can be z. For example, in the case of heavy soiling (with special spectral behavior of the illuminated surfaces) or in window areas with an invisible feature.
  • an upper threshold for the expected feature intensity Mmax is used.
  • all areas can be classified with an excess of feature signal "yellow".
  • the combined evaluation of remission and feature intensity at the pixel level allows in any case a simple consideration of otherwise problematic situations, such.
  • a run-up (ie y-offset) or skew one Value document in the processing machine as a result of a transport fault.
  • the pixel-level remission signal is used to normalize the feature signal (only in the linear region) for the purpose of fouling or overprint correction.
  • edge effects are taken into account if the value document edge only partially overlaps with the measurement pixels and therefore reduced feature and remission intensities are recognized.
  • the threshold for the feature intensity required for authentication can be adapted pixel-by-pixel on the basis of the measured remission signal.
  • a characteristic curve or a characteristic map for the authenticity detection is defined, as shown in FIG.
  • FIG. 3 shows a characteristic curve for the threshold values for classification on the pixel level.
  • the presence of a document is detected for remission values R above a reflectance threshold Ri.
  • This threshold can be set uniformly for all tracks, or, preferably, for each track, individually parameterized using reference measured values for white or black samples.
  • a reduced threshold for the feature intensity M is also used (Mi> M). If correspondingly lighter areas (Ri ⁇ R ⁇ R 2 ) are present, the required feature intensity threshold is preferably increased correspondingly between Mi and M 4 . At particularly strong reflecting sites (R> R 2 ) it can be assumed that there is no normal security substrate but a metallic reflector such. A hologram, Safety stripes or similar. Since these are typically opaque to optical radiation, the threshold value for the feature signal is correspondingly reduced to M3, since the covered areas under
  • Circumstances can only deliver 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 be digital but will mostly occur partially. This is due to a gradual reduction of the feature threshold between M 4 and M3 in the range
  • R2 ⁇ R ⁇ R3 taken into account.
  • M2 M2
  • M3 M3
  • a hologram strip is marked in "red”.
  • M2 can also be parameterized to very low values, which results in a classification of reflective hologram strips as "green”.
  • the first check for completeness is now made 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 the threshold 0, this means that not a single measurement location with insufficient feature intensity may be present, so that the value document is recognized as complete. In other variants, individual "red" pixels can be tolerated.
  • 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 percentage or the area-related degree of completeness.
  • the trace lengths determined from the reflectance measurements are each used as a yardstick for evaluating the trace completeness.
  • the number of "green" classified pixels in that track is divided by the number of all pixels within that track length.
  • the two-dimensional distribution of the feature intensity or the two-dimensional distribution of the classified pixels is also evaluated here as described above.
  • holes or opaque patches within the value document can be located.
  • the occurrence of larger holes is specifically examined.
  • "red" subliminal subpixels are searched for within the extent of the value document determined by the convex hull, and two-dimensional, contiguous areas are counted and identified / marked. Lying z. B. more than 2, 3, 5, ... (resolution-dependent) before contiguous red pixels, so a potentially missing area is detected. Subsequently, the location and geometric extent of the analyzed "red" areas and patterns occurring in a known manner such. B. a transparent window or a metallic hologram strip aligned. In particular, the shape, maximum width and relative position to the edges or corners of the value document is checked for plausibility and classified in case of deviations as "incomplete".
  • the number of red pixels is determined column by column and compared against a threshold value. If this threshold (of eg 2 or 3) is exceeded in a column, the value document is rejected as incomplete.
  • the maximum occurring width of a hologram strip may be considered by classifying value documents having a larger number of red pixels in the higher resolution measurement direction than a defined threshold directly as incomplete.
  • the authenticity sensor comprises two
  • Partial sensors that allow a two-sided measurement of the feature intensity on each document of value.
  • at least on one side-or more preferably on both sides-a remission channel is also available, with which the (track) length as well as the exact position and orientation of the value document are determined.
  • the two part sensors are centrally controlled in order to synchronize the timing of the excitation or measured value recording for both partial sensors.
  • two separate front and backside sensors are used, which are synchronized in one master / slave configuration by one of the two sensors ("master").
  • this master sensor sets the operating mode and outputs to be maintained time delays for the measuring pulses and / or measured value recording after a trigger signal before.
  • different sensor architectures for the master or slave sensor can preferably be used.
  • one of the sensors with a more complex measurement technique than the other sensor be equipped and check the feature values with a higher accuracy or a higher spectral resolution.
  • the two partial measurements of front and back are then evaluated in combination.
  • the measurement data are assigned to the respective measurement locations on the value document, the location-related data tuples are formed and evaluated (remission, feature, feature 2) or (remission 1, remission 2, feature 2, feature 2).
  • the position or timing of the two measurements (front, back) is coordinated so that the value document at the same pixel positions on the front and back are measured.
  • the measurement takes place (almost) simultaneously, i. that a measuring point at a location of the value document is detected almost simultaneously from the front and from the back.
  • this has the advantage that a mostly unavoidable crosstalk between front and rear side measurement 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 advantageously also be exploited 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 from the remission signal 2.
  • This temporal sequence of the light pulses or detections is shown schematically in FIG.
  • transmission, remission, remission2 are then available for each measuring location as well as feature, feature2 as database for the completeness evaluation. This allows the complete completeness evaluation even with existing opaque (metallic) or transparent (window) security features, which may otherwise hinder the completeness check of certain parts of the value document.
  • the illumination for the remission measurement (alternatively: feature measurement) of the front and the back are slightly offset in time, so that detector 2 can determine the transmitted portion of the illumination 1 independently and undisturbed by the illumination 2, as shown in FIG.
  • the sum (or the mean or the maximum) of feature and feature 2 is formed at each measurement location and then classified and evaluated according to the procedures described above.
  • a more accurate rating is achieved when individual thresholds for feature and feature2 are applied. These can depend both on remission and on the other characteristic value. In place of the characteristic curve described above for the pixel-wise red / green
  • FIGS. 5a and 5b show a characteristic diagram for the threshold values for classification on the pixel level with bilateral feature measurement.
  • FIG 5a is a classification based on static thresholds of
  • Feature value (Wed, min, With the map in Figure 5b is a Classification taking into account change effect effects, such. B. reflection on unilaterally applied metallic surface structures.
  • Example 1 Here a spectrally resolving Emspur luminescence sensor with remission measurement is used for the completeness test.
  • the sensor is operated on a banknote processing machine at 11 m / s transport speed and used for authenticity and completeness testing of banknotes with a luminescence marker which is matched to the luminescence sensor and inserted into the paper.
  • the banknotes have a reflective hologram strip on the front in the right area.
  • FIG. 6 shows a feature curve (O), a remission curve (x) and the dynamically calculated feature threshold (dashed line) of a genuine and complete banknote. Both remission and feature intensity are significantly modulated. Nevertheless, the completeness can be correctly determined by applying a reflection-dependent threshold in the classification of the feature intensity.
  • Example 2 Here, a spectrally resolving 11-track luminescence sensor with reflectance measurement is used for the completeness test.
  • the sensor is placed on a banknote processing machine at 11 m / s trans- operated for speed and authenticity and completeness of banknotes with a luminescence marker introduced into the paper.
  • the banknotes have a reflective hologram strip on the front in the right-hand area and a transparent window in the left-hand area.
  • FIG. 7 shows a representation of the measured remission values of the banknote. High remission occurs especially in the area of the reflective hologram strip, while very low remission is present in the transparent window.
  • FIG. 8 shows a representation of the feature intensity of the banknote.
  • White corresponds to high intensity, while black corresponds to low values.
  • black corresponds to low values.
  • the hologram strip In the area of the window (left) and the hologram strip (right) only very low feature intensity is detectable.
  • FIG. 9 shows a representation of the feature intensity of an incomplete banknote with a diagonally inserted stripe of a copy without a feature.
  • FIG. 10a shows a pixel-wise classification of the banknote (FIGS. 7-8) with a dynamic threshold.
  • the low feature intensity in the area of the hologram strip could be taken into account by the dynamic threshold, while the missing feature intensity in the window area could be taken into account.
  • FIG. 10b shows a pixel-by-pixel classification of the incomplete bank note (FIG. 9) with a dynamic threshold.
  • the small 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 for lack of remission signal.
  • the banknote of FIGS. 7-8 was measured again with a sensor structure with double-sided measurement. Characteristics (front), feature2 (rear), remissionl (front), remission2 (rear) and transmission were measured.
  • FIG. 11 shows transmission data of the banknote For classifying the measurement pixels, the front and rear sides were classified separately with a dynamic feature threshold and then separated according to the following assignment of the class assignments determined on the front (classification) and back (classification) to a total classification for each pixel combined, as shown in Figure 12 is shown.
  • FIG. 13 diagrammatically shows a combination of feature values classified on both sides, according to which evaluation of the value document or of the banknote likewise takes place for authenticity and / or completeness.

Abstract

La présente invention concerne un procédé, un capteur, une unité de détection et une machine de traitement de billets de banque pour vérifier l'intégrité et/ou l'authenticité de documents de valeur. Un document de valeur comprend au moins une substance caractéristique lisible par machine en au moins deux emplacements. D'après le procédé, le document subit une sollicitation (S2) au moins localement au niveau d'emplacements de mesure. Une intensité caractéristique relative à la substance caractéristique lisible par machine est ensuite détectée (S3a) avec une résolution spatiale en plusieurs emplacements différents du document de valeur. Les intensités caractéristiques rapportées aux emplacements sont alors classées (S5), en référence aux emplacements, à l'aide d'une valeur seuil. Une limite rapportée aux emplacements, d'une répartition spatiale attendue de la substance caractéristique lisible par machine est ensuite déterminée (S6). La répartition rapportée aux emplacements des intensités caractéristiques classées est finalement évaluée (ST).
EP16819440.5A 2016-01-05 2016-12-21 Vérification de l'intégrité d'un document de valeur Active EP3400584B1 (fr)

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PCT/EP2016/002156 WO2017118467A1 (fr) 2016-01-05 2016-12-21 Vérification de l'intégrité d'un document de valeur

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RU2018127765A3 (fr) 2020-02-06
US11823522B2 (en) 2023-11-21
DE102016000011A1 (de) 2017-07-06
EP3400584B1 (fr) 2023-11-15
RU2018127765A (ru) 2020-02-06
US20200273279A1 (en) 2020-08-27
ES2968235T3 (es) 2024-05-08
WO2017118467A1 (fr) 2017-07-13

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