JP2013062648A - Individual identification device, individual identification method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an individual identification device etc. which when identifying an article provided with taggants, prevents erroneous recognition of the distribution positions of the taggants caused by the pattern printed on the article with dot gradation, thereby being capable of accurately extracting feature points.SOLUTION: An individual identification device 100 calculates (S11) the pixel size of a taggant 12 in a taken image on the basis of the resolution of the taken image and the actual size of the taggant 12, determines (S12) a predetermined filter size on the basis of the pixel size of the taggant 12 calculated at the S11, and applies (S13) smoothing filter to the taken image on the basis of the predetermined filter size determined at the S12. The predetermined filter size is desirably the largest filter size of the pixel sizes of the taggant 12 and smaller than this.

Description

  The present invention relates to an individual identification device for identifying an individual based on a taggant attached to the surface of an article.

  Conventionally, a manufacturing number is given to an industrial product, a product package, etc., and it is used for manufacturing management and physical distribution management. The production number is printed as a code such as a character or a barcode at a predetermined position of the article. In addition, serial numbers are printed on public certificates such as certificates and securities such as gift certificates for the purpose of preventing counterfeiting and authenticating authenticity. However, when a production number for individual identification is given for the purpose of production management or physical distribution management, it is often clearly printed because it aims to clearly identify or identify the machine. When given in the form of a code or a two-dimensional code, the design of the original product may be impaired. In addition, for the purpose of preventing forgery, printing of characters, barcodes, and the like may be easily forged or altered, and the effect is insufficient.

  In recent years, a method for assigning an individual ID using an IC tag has been proposed as a means for individual identification. Each IC tag can be assigned a unique ID that is difficult to rewrite and can be read in a non-contact manner, so that it is possible to identify an individual by providing an IC tag on the back surface or inside of a product or the like. However, the IC tag has a problem that the unit price is high and it is difficult to spread.

In order to solve these problems, it is known to form a light diffraction structure (hereinafter also simply referred to as a hologram) such as a hologram or a diffraction grating in a part of a credit card or securities to prevent forgery.
In addition, as in the label of Patent Document 1, at least on the pressure-sensitive adhesive layer or light-diffractive structure forming layer of the label in which the light diffraction structure forming layer, the pressure-sensitive adhesive layer, and the coating material are sequentially formed on one side of the transparent plastic substrate. By mixing the fine particles, the hologram and the taggant (additive for tracking), which are authenticity discrimination elements, are present to prevent forgery and transparent as in the label of Patent Document 2. On one side of the plastic substrate, at least a light-sensitive structure layer, a reflective material layer, a pressure-sensitive adhesive layer, and a temperature-sensitive color-changing material layer are formed in a partial region between any one of the labels on which the coating material is sequentially formed, There has been proposed a method in which a metal material having a lower refractive index than that of the light diffraction structure layer is formed in the reflective material layer, and an optically detectable substance is mixed in the pressure-sensitive adhesive layer to prevent forgery. And, when the above-mentioned fine fine particles, for example, having a characteristic of emitting light such as fluorescence in a certain wavelength range by irradiating white light, ultraviolet light or infrared light, are fine when performing forgery determination. The fineness was enlarged with a magnifying glass or the like, and the authenticity was determined by confirming the characteristics of the emitted light.

  Further, Patent Document 3 describes a technique for determining authenticity by analyzing the characteristics of the surface of an individual itself without using the hologram and taggant as described above. In Patent Document 3, for example, the transparency of the paper (light / dark pattern resulting from the entanglement of the fibrous material forming the paper) is read with a scanner or the like as the feature amount of the surface of the individual. Compare the surface features of the individual to be judged with a pattern, calculate the correlation value while moving the comparison area, calculate the normalized score of the maximum correlation value, and determine true / false Is described.

JP 2008-261967 A JP 2008-281912 A Japanese Patent No. 4103826

  However, in the labels disclosed in Patent Document 1 and Patent Document 2, it is difficult to forge by combining holograms, taggants, or temperature-sensitive color-changing materials, but the presence or layers of holograms, taggant, temperature-sensitive color-changing materials, etc. If the structure is analyzed and imitated, it may be difficult to determine authenticity. Further, the method of Patent Document 3 is applicable when the individual surface has random characteristics, and cannot be applied to those having few individual surface characteristics. Further, in order to improve the accuracy of the authenticity determination, it is necessary to make the original paper quality special or to perform special processing on the surface, which may increase the manufacturing cost. Therefore, it is desired to be able to perform individual identification and authenticity determination based on unique characteristics that can be easily given to articles but cannot be separated from individual articles.

  In particular, in order to perform individual identification with high accuracy, it is necessary to extract feature points derived from taggants more accurately. For example, when the design constituting the design of the article is printed with halftone gradation, there may be dots (hereinafter referred to as “halftone dots”) that constitute halftone dots of a hue that overlaps the taggant hue. There is sex. For this reason, if a feature point derived from a taggant is extracted from an image of an article taken based on the hue, a feature point derived from a halftone dot is also extracted, and the identification accuracy is significantly reduced.

  The present invention has been made in view of such a problem, and when identifying an article to which a taggant is applied, erroneous recognition of the distribution position of the taggant resulting from the pattern of the article printed by halftone gradation is performed. An object of the present invention is to provide an individual identification device or the like capable of preventing and accurately extracting feature points.

  In order to solve the above-described problems, the first invention is to provide an article having a base material and a taggant distribution layer fixed in parallel to the surface of the base material, and a pattern printed with halftone dots. An identification device for identifying, comprising: storage means for storing, as reference feature point data, distribution position information of the taggant assigned to a reference article that is the article serving as a reference for individual identification; and the article to be identified By applying a smoothing filter to a photographed image of a certain target article with a predetermined filter size, smoothing means for making the target object data, and the distribution position information of the taggant based on the target object data The object feature point extracting means to be extracted as feature point data, the object feature point data extracted by the object feature point extracting means, and the reference feature point data are compared. The subject article and said reference article is an individual identification device, characterized in that it comprises discriminating means for discriminating whether or not the same individual, the by.

  The second invention is an individual identification method for individually identifying an article having a base material and a taggant distribution layer fixed in parallel to the surface of the base material and having a pattern printed by halftone gradation. Storing the taggant distribution position information given to the reference article, which is the article serving as a reference for individual identification, as reference feature point data; and a captured image of the target article, which is the article being identified. On the other hand, by applying a smoothing filter with a predetermined filter size, a smoothing step to obtain object data, and an object for extracting the taggant distribution position information as object feature point data based on the object data By comparing the object feature point data extracted by the object feature point extraction step, the object feature point extraction step and the reference feature point data, The target object and the reference article is an individual identification method which comprises a determination step of determining whether or not the same individual, the.

  According to the individual identification device of the first invention and the individual identification method of the second invention, it has a base material and a taggant distribution layer fixed in parallel with the surface of the base material, For the article on which a pattern is printed, the taggant distribution position information given to the reference article, which is the article serving as a reference for individual identification, is stored as reference feature point data, and is the article to be identified. A smoothing filter is applied to the photographed image of the target article with a predetermined filter size to obtain target object data. Based on the target data, the distribution position information of the taggant is extracted as target feature point data. Then, by comparing the extracted object feature point data with the reference feature point data, it is determined whether or not the target article and the reference article are the same individual.

  Thereby, when identifying an article to which a taggant is given, it is possible to prevent misrecognition of the distribution position of the taggant caused by the pattern of the article printed with halftone gradation, and to extract feature points with high accuracy.

  The smoothing unit according to the first aspect of the present invention includes a calculating unit that calculates a pixel size of the taggant based on the resolution of the captured image and the actual size of the taggant, and the pixel of the taggant calculated by the calculating unit. Desirably, a determination unit that determines the predetermined filter size based on a size, and a filter unit that applies a smoothing filter to the captured image based on the predetermined filter size determined by the determination unit. . This makes it possible to clearly distinguish the taggant region from the other regions by hue or the like even for an article on which a pattern is printed with halftone gradation.

  In the first aspect of the invention, it is preferable that the determining means sets the predetermined filter size to be equal to or smaller than the taggant pixel size. This makes it possible to extract only feature points derived from taggant without extracting feature points derived from halftone dots.

  Further, the object feature point extraction means in the first invention extracts pixels in a predetermined hue range as a taggant area, and calculates the center of gravity of the taggant area, thereby obtaining the distribution position information of the taggant as the object. It is desirable to extract as feature point data. As a result, even if the shadow around the taggant is extracted as the taggant region, if the shadow region is small, the feature point extraction result is not greatly affected.

  A third invention is a program described in a computer-readable format, and includes a base material and a taggant distribution layer fixed in parallel with the surface of the base material, and the pattern is represented by halftone dots. A storage step of storing, as reference feature point data, the distribution position information of the taggant assigned to the reference article, which is the article that is a reference for individual identification, and the article to be identified. By applying a smoothing filter to the photographed image of the target article with a predetermined filter size, a smoothing step to obtain target data, and based on the target data, the distribution position information of the taggant is a target feature. An object feature point extraction step to extract as point data, an object feature point data extracted by the object feature point extraction step, It said target object and said reference article is a program program to execute a determining step of determining whether or not the same individual, the process comprising the computer by comparing the reference feature data.

  According to the third invention, it is possible to cause a computer to function as the individual identification device of the first invention.

  According to the present invention, when identifying an article provided with a taggant, it is possible to prevent misrecognition of the taggant distribution position caused by the pattern of the article printed by halftone gradation, and to accurately extract feature points. Can be provided.

The figure explaining the articles | goods 1 to which this invention is applied (1st Embodiment) Examples of structures of taggants 12A and 12B having a reflective metal layer 3 Example of structure of taggant 12C having multilayer thin film 4 having different dielectric constants Example of structure of taggant 12D having optical diffraction structure layer 5 Example of the structure of the taggant 12E having the reflective layer 6 that emits predetermined reflected light with respect to the specific irradiation light Flow chart explaining the procedure of individual identification processing The figure explaining the taggant 12 and the shadow 9 which are contained in a picked-up image The figure explaining the shadow 9 of the taggant 12 Hardware configuration diagram of the individual identification device 100 according to the present invention An example of an article 1 to which a taggant 12 is attached and a pattern is printed with halftone dots Flow chart explaining the flow of fine substance distribution analysis processing Flow chart explaining the flow of smoothing processing Smoothing filter example Example of smoothing processing Example of extracting feature points without executing smoothing Example of extracting feature points after executing smoothing Example of reference article and target article and image data to be read (in case of comparison at absolute position) Example of reference article and target article and image data to be read (in case of comparison at relative position) The figure explaining calculation of the relative position information of feature point data Example of feature points extracted from reference image data and object image data Flowchart explaining the thinning process The figure explaining the article | item 8 to which this invention is applied (2nd Embodiment). The figure which shows an example of the taggant 82 which a predetermined | prescribed character, a figure, a symbol, a pattern, or what combined these was attached | subjected and which has a predetermined three-dimensional shape

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.

[First Embodiment]
First, an article 1 to which the present invention is applied will be described with reference to FIG.
FIG. 1A is a top view of the article 1, and FIG. 1B is a cross-sectional view taken along the line AA in FIG.
The article 1 has a taggant (tracking additive) distribution layer 11 on its substrate 10. In the taggant distribution layer 11, a plurality of fine substances (hereinafter referred to as taggant) 12 having reflectivity different from that of the substrate 10 are randomly arranged. The taggant 12 of the taggant distribution layer 11 is disposed on the base material 10 of the article 1 by, for example, printing on the base material 10 by being mixed with printing ink, or by applying it by being mixed with an adhesive or the like. The Thereby, each taggant 12 is arranged at a random position.

  In FIG. 1, the article 1 is illustrated as having only the base material 10 and the taggant distribution layer 11, but the upper surface of the taggant distribution layer 11 is further covered with a transparent plastic or the like to protect the taggant distribution layer 11. You may do it. Moreover, as the base material 10, any material may be used according to the function, property, design, etc. of the article 1. Further, the surface on which the taggant distribution layer 11 is formed is not limited to a flat surface, and may be a curved surface or uneven. The taggant distribution layer 11 may be provided on the entire surface of the article or may be a part thereof.

Next, the taggant 12 will be described with reference to FIGS. 2-5 are cross-sectional views of various embodiments of taggants 12A-12E.
The taggant 12 is a fine fine particle having a size (about several μm to several hundred μm) that can be visually recognized by its magnified shape and surface optical characteristics when magnified with a loupe.

  In the present invention, as the taggant 12, for example, a metal piece, one having a reflective metal layer 3 as shown in FIG. 2, or a thin film having a different dielectric constant as shown in FIG. 4, those having the light diffraction structure layer 5 as shown in FIG. 4, and those having the reflective layer 6 having specific reflection characteristics with respect to the predetermined irradiation light as shown in FIG. Is preferred.

  Note that the taggants 12A, 12B, 12C, 12D, and 12E in FIGS. 2 to 5 are shown with a circular cross-sectional shape for the sake of explanation, but are limited to this in the first embodiment of the present invention. The shape of the taggant 12 may be arbitrary. For example, the material used as the base material 120 may be crushed and a random shape may be used as the base material 120 of the taggant 12.

  The taggant 12A in FIG. 2 is obtained by forming the reflective metal layer 3 on the surface of the base material 120 of the taggant 12.

When the reflective metal layer 3 is an opaque layer, it may be a thin film having a small refractive index, and in addition to commonly used aluminum, for example, chromium, iron, cobalt, nickel, copper, silver, gold, A metal such as magnesium, lead, tin, cadmium, bismuth, titanium, zinc, or indium, an oxide, a nitride thereof, or an alloy of these metals is used.
When the reflective metal layer 3 is a transparent layer, a thin film having a high refractive index may be used, and zinc sulfide, titanium oxide, aluminum oxide, silicon oxide, antimony sulfide, or the like is used.

The reflective metal layer 3 is formed by a thin film forming method such as a vacuum deposition method, a sputtering method, or an ion plating method.
The thickness of the reflective metal layer 3 is set according to the purpose. For example, the thickness may be 0.005 μm to 0.1 μm.

  The reflective metal layer 3 may be applied to the entire surface of the substrate 120 of the taggant 12A or may be applied to a part thereof. Further, for example, the reflective metal layer 3 may be applied as a design made up of characters, figures, symbols, patterns, etc., or combinations thereof.

  For the base material 120, for example, polyethylene terephthalate (polyester), polyvinyl chloride, polycarbonate, polyimide, polyamide, cellulose triacetate, polystyrene, acrylic, polypropylene, and polyethylene may be used.

  Further, like the taggant 12B shown in FIG. 2B, the reflective metal layer 3 may be covered with a transparent coating layer 31 to be protected. The material of the covering layer 31 is preferably a plastic film such as polyethylene, wax, silicon, or polyester film.

  By having the reflective metal layer 3 like the taggants 12A and 12B, the distribution of the taggant 12 can be easily confirmed, and authenticity determination by a loupe (magnifying glass) can be easily performed. Moreover, it becomes easy to extract as a feature point in the fine substance distribution analysis process (FIG. 11) described later.

The taggant 12C in FIG. 3 is obtained by forming a plurality of thin films having different dielectric constants on the surface of the substrate 120. For example, pigments (pearl pigments) obtained by coating a base material 120 such as natural mica flakes (mica flakes) with metal oxides such as titanium oxide and iron oxide, synthetic alumina flakes, synthetic silica flakes, borosilicate glass flakes, titanium oxides A pigment (effect pigment) coated with a metal oxide such as titanium oxide or iron oxide on a base material 120 such as coating or synthetic mica flakes (aluminum oxide, magnesium oxide, silicon dioxide, fluorine compound, etc.) is used as taggant 12C. it can.
The thickness of the multilayer thin film layer 4 is set according to the purpose. For example, the thickness may be 0.005 μm to 0.1 μm.

  Since the color of the taggant 12C changes depending on the viewing angle, it is easy to perform authenticity determination using a loupe. In addition, there is an effect that it is easy to extract as feature points in the fine substance distribution analysis process (FIG. 11) described later.

  The taggant 12D in FIG. 4 is obtained by forming the light diffraction structure layer 5 on the surface of the base material 120. The light diffractive structure layer 5 is a layer in which fine hologram unevenness is formed, but the light diffraction structure layer 5 itself can be formed using various materials capable of forming hologram fine unevenness. For example, transparent thermoplastic resin such as polyvinyl chloride, acrylic resin, polystyrene, polycarbonate, or unsaturated polyester, melamine, epoxy, polyester (meth) acrylate, urethane (meth) acrylate, epoxy (meth) acrylate, polyether Transparent thermosetting resins such as (meth) acrylate, polyol (meth) acrylate, melamine (meth) acrylate, and triazine acrylate can be used. Furthermore, the above-mentioned thermoplastic resin and the above-mentioned thermosetting resin are mixed and used, and further, a thermoformable substance having a radical polymerizable unsaturated group, or a radical polymerizable unsaturated monomer therefor. May be used that are ionizing radiation curable.

The light diffraction structure layer 5 may be applied to the entire surface of the taggant 12D or may be applied to a part thereof.
Further, the light diffraction structure layer 5 may be protected by covering it with a transparent coating layer (not shown).

Formation of the fine irregularities of the hologram on the optical diffraction structure 5 is performed by using the original plate on which the diffraction grating and the interference fringes of the hologram are recorded in an irregular shape as a press mold, and preparing the above resin as a coating composition on the base material Is applied by means of gravure coating method, roll coating method, bar coating method, etc. to form a coating film, and the above-mentioned original plate is overlaid thereon, and both are heated and pressure-bonded by appropriate means such as a heating roll. Can be done. Moreover, when using a photopolymer, after apply | coating a photopolymer on a base material similarly, it can replicate by overlapping the above-mentioned original plate and irradiating a laser beam.
The thickness of the light diffraction structure layer 5 is set according to the purpose. For example, the thickness may be 0.005 μm to 0.1 μm.

  Further, in the taggant 12D of FIG. 4, a reflective metal layer may be further provided in order to increase the diffraction efficiency of the fine irregularities of the hologram. The reflective metal layer is made of chromium, iron, cobalt, nickel, copper, silver, gold, germanium, aluminum, magnesium, antimony, lead, tin, cadmium, bismuth, selenium, gallium, indium, rubidium, or the like, or The oxide, nitride, alloy of these metals, etc. can be used. The reflective metal layer is preferably formed by a thin film forming method such as a vacuum deposition method, a sputtering method, or an ion plating method, but may be formed by printing using a metallic ink containing a metallic pigment.

  By having the light diffraction structure layer 5 like the taggant 12D, confirmation with a loupe becomes easy. In addition, in the fine substance distribution analysis process described later, optical reading is facilitated and feature points are easily extracted. Further, the forgery prevention effect is enhanced by irradiating light of a specific wavelength (laser light or the like) to reproduce the hologram design and determining the hologram design together with the determination of the distribution of the taggant.

  Moreover, the hologram designs given to the individual taggants 12D may be the same or different. When a different hologram design is given, the security effect becomes higher. On the other hand, when the same hologram design is given, the cost can be reduced as compared with the case where a different hologram design is given.

  The taggant 12E of FIG. 5 is formed by forming a reflective layer 6 that emits light of a specific wavelength with respect to predetermined irradiation light. The reflective layer 6 is formed, for example, by applying a resin containing a fluorescent pigment on the surface of the base material 120 or by mixing the fluorescent pigment in printing ink and printing.

The material used for the inorganic phosphor used as the fluorescent pigment is, for example, an ultraviolet light-emitting phosphor or an infrared light-emitting phosphor. The ultraviolet light emitting phosphor is excited by ultraviolet light, and has a spectrum peak emitted when returning to a lower energy level in a wavelength band such as blue, green, and red. For example, Ca ₂ B ₅ O ₉ Cl: Eu 2+ , CaWO ₄ , ZnO: Zn ₂ SiO ₄ : Mn, Y ₂ O ₂ S: Eu, ZnS: Ag, YVO ₄ : Eu, Y ₂ O ₃ : Eu, Gd ₂ O ₂ S: Tb, La ₂ O ₂ S : Tb, Y ₃ Al ₅ O ₁₂ : Ce, etc., which can be used alone or in combination with several selected from them. The fluorescence spectrum has peaks outside the blue, red, and green wavelength ranges. In addition, the infrared light emitting phosphor has a characteristic of receiving visible light having a wavelength λ2 upon receiving excitation light having a wavelength λ1, and has a property of λ1 ≠ λ2 and λ1> λ2, and its composition is, for example, YF ₃ : Er + Yb _{, Y 3 OCl 7: Er +} Yb, NaLnF 4: Er + Yb (Ln = Y, Gd, La), BaY 2 F 8: Er + Yb, (PbF 2 -GeO 2): Er + Yb, (PbF 2 -GeO 2): Tm + Yb etc. Each of them emits visible light (λ2) having a visible peak of the emission spectrum at 450 nm to 650 nm upon receiving infrared light of excitation light (λ1) 800 to 1000 nm.

The reflective layer 6 may be applied to the entire surface of the taggant 12E or may be applied to a part of the surface. Further, for example, the reflective layer 6 may be provided as a design such as a character, a figure, a pattern, or a pattern.
Further, the reflective layer 6 may be covered with a transparent coating layer for protection.
The thickness of the reflective layer 6 is set according to the purpose. For example, the thickness may be 0.005 μm to 0.1 μm.

  By having the reflective layer 6 with the specific irradiation light like the taggant 12E, it becomes easy to confirm with a loupe. Further, in the fine substance distribution analysis process described later, optical reading is facilitated, and extraction as feature points is facilitated. Moreover, since it does not emit light under normal white light, it is highly concealed and it is easy to prevent imitation.

Next, an individual identification method for the article 1 will be described.
First, as shown in FIG. 6, using a detector or the like that emits predetermined irradiation light for inspection, the emitted light of the taggant 12 imparted to the taggant distribution layer 11 of the article 1 is emitted, and a loupe or the like is used. By magnifying and visually recognizing the characteristics, the authenticity is roughly determined (step S1). The article 1 determined to be true in step S1 is further subjected to a fine substance distribution analysis process (step S2) using the individual identification device 100 such as a computer, thereby determining true / false.

  The taggant distribution layer 11 of the article 1 includes a taggant 12. For this reason, when an image is read (photographed) by irradiating light from the light source, a shadow is generated in each taggant 12, and the shadow 9 of the taggant is also reflected in the captured image. FIG. 7 is an example of a photographed image, and an area 9 is a shadow. Since the light source position is different between FIG. 7A and FIG. 7B, the position of the shadow 9 is also different.

As shown in FIG. 8, the taggant 12 is a flat structure, and the thickness (= vertical length) is about several μm while the maximum length in the horizontal direction is 100 μm to several 100 μm. And the taggant 12 is distributed so that it may become parallel with respect to the surface of the base material 10, and it adheres in parallel with the surface of the base material 10 by using a transparent plastic as a sticking agent. That is, the taggant 12 is in a state of floating in parallel with the surface of the substrate 10 in the taggant distribution layer 11.
And as shown in FIG. 8, the shadow 9 of the taggant 12 is made when light scatters between the taggant 12 and the base material 10. In general, since the hue of an object is a hue having a wavelength of light reflected from the object, a shadow 9 having the same hue as that of the taggant 12 is formed.

Here, the shadow 9 means “the shape of the object that can be formed by light in addition to the object”. That is, the hue is not limited to black. The “taggant shadow” in the present invention is caused by light scattered between the taggant 12 and the substrate 10 as described above.
In general, since the hue of an object is a hue having a wavelength of light reflected from the object, the hue of the shadow 9 is the same as the hue of the taggant 12. However, the saturation is lower in the shadow 9 than in the taggant 12. For example, if the taggant 12 is blue, the shadow 9 is light blue.

  When a pattern is printed on the article 1 with halftone gradation, there may be a halftone dot having a hue close to the hue of the taggant 12 or the hue of the shadow 9. For this reason, if a feature point derived from the taggant 12 is extracted based on the hue of the image in which the article 1 is photographed, a feature point derived from the halftone dot is also extracted, and the identification accuracy is significantly reduced.

  As will be described later, in the feature point extraction process (steps S102 and S106 in FIG. 11), the individual identification device performs HSV conversion on the image data of the article 1 and extracts pixels in a predetermined hue range as taggant regions. The feature points derived from the taggant 12 are extracted by calculating the center of gravity of the taggant region. Therefore, even if the shadow 9 around the taggant 12 is extracted as a taggant region, if the shadow 9 region is small, the coordinates of the center of gravity of the taggant region do not change so much, and the feature point extraction result is not greatly affected.

  On the other hand, halftone dots generally appear with a certain regularity in the entire image. Therefore, when the hue of the halftone dot is included in the predetermined hue range when the taggant area is extracted, many feature points derived from the halftone dot are extracted. If the number of feature points derived from the halftone dots is larger than the number of feature points derived from the taggant 12, even a counterfeit product not provided with the taggant 12 is erroneously determined as a true article. Will end up.

  Therefore, in the fine substance distribution analysis process in step S2, the feature point extraction process is performed after the smoothing process (steps S101 and S105 in FIG. 11) is performed on the image obtained by photographing the taggant distribution layer 11 of the article 1. Therefore, feature points derived from halftone dots are not extracted. Then, the extracted feature point group (distribution position information of the taggant 12) is used for matching as the feature amount of the individual.

  For example, when the taggant 12 is mixed and applied to the printing ink, even if the printing device is the same model, each printing device has its own wrinkles, and strictly the same finished state cannot be obtained. Therefore, different printing results are obtained depending on the printing apparatus, the ink to be used, the ink remaining amount, the printing setting, and various conditions such as the temperature and humidity during printing. In addition, the distribution of taggant varies depending on the mixing ratio of taggant. In the present invention, the distribution position of the taggant 12 of the true article is obtained in advance, stored as reference feature point data, and compared and collated with the distribution of the taggant 12 of the article to be compared. False).

Next, the individual identification device 100 that performs the fine substance distribution analysis process will be described.
FIG. 9 is a block diagram illustrating a hardware configuration of the individual identification device 100.

As shown in FIG. 9, the individual identification device 100 includes a control unit 101, a storage unit 102, an input unit 103, a display unit 104, a media input / output unit 105, a communication I / F unit 106, a peripheral device I / F unit 107, and the like. Are connected via a bus 109. In addition, an image reading device 108 and a light source 110 are connected to the peripheral device I / F unit 107.
Each device including the control unit 101, the storage unit 102, the input unit 103, the display unit 104, the media input / output unit 105, the communication I / F unit 106, the peripheral device I / F unit 107, and the bus 109 of the individual identification device 100 is For example, it is configured by a computer or the like.

The control unit 101 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like.
The CPU calls and executes a program stored in the storage unit 102, ROM, recording medium, or the like in a work memory area on the RAM, and drives and controls each unit connected via the bus 108. The ROM permanently holds a computer boot program, a program such as BIOS, data, and the like. The RAM temporarily stores the loaded program and data, and includes a work area used by the control unit 101 for performing various processes described later.

  The storage unit 102 is an HDD (hard disk drive), and stores a program executed by the control unit 101, data necessary for program execution, an OS (operating system), and the like. These program codes are read by the control unit 101 as necessary, transferred to the RAM, and read and executed by the CPU.

  The input unit 103 is an input device such as a keyboard, a mouse, a touch panel, a pointing device such as a tablet, or a numeric keypad, and outputs input data to the control unit 101.

The display unit 104 includes, for example, a display device such as a liquid crystal panel or a CRT monitor, and a logic circuit (such as a video adapter) for executing display processing in cooperation with the display device, and is input under the control of the control unit 101. Display information is displayed on a display device.
Note that the input unit 103 and the display unit 104 may be integrated as a touch panel type input / output unit.

The media input / output unit 105 is a media input / output device such as a floppy (registered trademark) disk drive, PD drive, CD drive, DVD drive, and MO drive, and performs data input / output.
The communication I / F 106 includes a communication control device, a communication port, and the like, and is a communication interface that mediates communication with the network, and performs communication control.

  A peripheral device I / F (interface) 107 is a port for connecting a peripheral device to a computer, and the computer transmits and receives data to and from the peripheral device via the peripheral device I / F 107. The peripheral device I / F 107 is configured by USB, IEEE 1394, RS-232C, or the like, and usually has a plurality of peripheral devices I / F. The connection form with the peripheral device may be wired or wireless.

The image reading device 108 (photographing device) is a scanner, a CCD camera, or the like, and is a device that optically reads an image and acquires it as image data. The image reading device 108 is connected to the individual identification device 100 via the peripheral device I / F 107. Alternatively, the image reading apparatus 108 may be configured to be connected to the individual identification apparatus 100 via the communication I / F 106. The image reading device 108 outputs the read image data to the control unit 101. The control unit 101 stores the acquired image data in a predetermined memory area of the RAM or the storage unit 102.
The light source 110 irradiates the article 1 with light from a predetermined direction when photographing the article 1 to be photographed.
The bus 109 is a path that mediates transmission / reception of control signals, data signals, and the like between the devices.

Next, the flow of the fine substance distribution analysis process will be described.
FIG. 10 is an example of the article 1 to which the taggant 12 is applied and the pattern is printed with halftone gradation. FIG. 11 is a flowchart for explaining the flow of the fine substance distribution analysis process. FIG. 12 is a flowchart illustrating the smoothing process. FIG. 13 is an example of a smoothing filter. FIG. 14 is an execution example of the smoothing process. FIG. 15 is an example in which feature points are extracted without executing smoothing processing. FIG. 16 is an example in which feature points are extracted after the smoothing process is executed. 17 and 18 are examples of the reference article and the target article and the image data to be read. FIG. 19 is a diagram for explaining the relative positions of feature points. FIG. 20 is an example of feature points extracted from the reference image data and the object image data.

  The original images in FIGS. 10 and 14 to 16 are all color images. The color image is converted to a grayscale image due to limitations of the patent drawing. The article 1 shown in FIGS. 10 and 14 to 16 is a card-shaped medium on which a color picture is printed with halftone gradation. Moreover, the taggant 12 which has various hues is attached | subjected to the articles | goods 1 shown by FIG. 10, FIG. 14-16.

  In the fine substance distribution analysis process described below, it is assumed that a pattern is printed on the article 1 with halftone gradation. FIG. 10A is an image obtained by photographing a part of the article 1 provided with the taggant by the image reading device 108. FIG. 10B is an enlarged image of the rectangular area X1 (shown by a thick black line) in FIG.

  As can be seen from FIG. 10B, the pattern of the article 1 shown in FIG. 10 is printed with halftone gradation. Further, two taggants 12 exist in the rectangular region X2 (shown by a black thick line) in FIG.

  As shown in the flowchart of FIG. 11, the control unit 101 of the individual identification device 100 first performs pre-processing (steps S <b> 100 to S <b> 103). In the pre-processing, the taggant distribution layer 11 applied to the reference article (true article) is optically read using the image reading device 108 (step S100). The part to be read may be the entire taggant distribution layer 11 or a part thereof.

Next, the control unit 101 performs a smoothing process on the read image (hereinafter referred to as “captured image”) (step S101).
By performing the smoothing process, it becomes possible to extract feature points, which will be described later, more precisely, and the accuracy of individual identification is improved.

Here, the smoothing process in step S101 will be described.
As shown in the flowchart of FIG. 12, the control unit 101 calculates the pixel size of the taggant 12 in the captured image based on the resolution of the captured image and the actual size of the taggant 12 (step S11).

  As shown in FIG. 7, the flat surface of the taggant 12 has various shapes such as a circle, a heart shape, and a polygon. Therefore, in the present invention, the actual size of the taggant 12 is the length of one side of the circumscribed square of the taggant 12 when viewed from the vertical direction. In addition, the pixel size of the taggant 12 in the captured image is the number of pixels corresponding to the length of one side of the circumscribed square of the taggant 12 in the captured image.

A specific example in step S11 will be described. For example, the imaging resolution of the image reading apparatus 108 is “1200 dpi”. In this case, the diameter of one dot is 25.4 mm ÷ 1200 × 10 ³ ≈21.2 μm. Further, for example, the actual size of the taggant 12 (= the length of one side of the circumscribed square) is 150 μm. In this case, the pixel size of the taggant 12 is 150 ÷ 21.2≈7.08 pixels.

  Next, the control unit 101 determines a predetermined filter size based on the pixel size of the taggant 12 calculated in step S11 (step S12). And the control part 101 applies a smoothing filter to a picked-up image based on the predetermined filter size determined in step S12 (step S13).

  As shown in FIG. 13, the smoothing filter is, for example, a Gaussian filter. FIG. 13A shows a Gaussian filter having a filter size of “3” (3 × 3). FIG. 13B shows a Gaussian filter having a filter size of “5” (5 × 5). In both cases, the shaded area is the pixel of interest. In the Gaussian filter, not only “3” and “5” but also “7”, “9”,..., Odd filter sizes can be defined.

  Note that the smoothing filter is not limited to a Gaussian filter. Applicable averaging filters include, for example, averaging filters, weighted averaging filters (generalization of Gaussian filters), median filters, and the like. For any filter, an odd filter size can be defined.

  For example, if the predetermined filter size is a filter size larger than the pixel size of the taggant 12, the hue of the taggant 12 itself is smoothed (blurred), and the taggant 12 is extracted in the feature point extraction process described later. It becomes impossible to extract the feature points derived from. On the other hand, if the predetermined filter size is too small, the hue of halftone dots cannot be sufficiently smoothed (cannot be blurred), and feature points derived from halftone dots are also extracted in the feature point extraction process described later. Resulting in.

  Therefore, the predetermined filter size is preferably the largest filter size within the taggant 12 pixel size. For example, in the above-described specific example, since the pixel size of the taggant 12 is “7.08 pixels”, the filter size of 7.08 or less, that is, the filter size = 3, 5, or 7 The large filter size is “7”. Thereby, in the feature point extraction process described later, only the feature points derived from the taggant 12 can be extracted without extracting the feature points derived from the halftone dots.

  FIG. 14A is an enlarged image of the rectangular area X2 (illustrated by a thick black line) in FIG. There is one taggant 12 in each of the elliptical areas Y1 and Y2 (illustrated by thick black lines). Although it cannot be confirmed because grayscale conversion is performed, the hue of the taggant 12 existing in the elliptical area Y1 is a hue close to red. Further, the hue of the taggant 12 existing in the elliptical region Y2 is a hue close to green. On the other hand, each halftone dot constituting the pattern of the article 1 is constituted by four types of halftone dots of red, blue, white and gray. In addition, the pattern of the article 1 has a large number of blue and gray areas as a whole.

  For example, when a pixel in a predetermined hue range is extracted as a taggant region so that a taggant 12 having a hue close to red is extracted from the image data shown in FIG. It will be extracted as a region.

  FIG. 14B shows the result of performing the process of step S13 on FIG. Although it cannot be confirmed because of grayscale conversion, most of the pixels in the ellipse area Y3 have a hue close to red. In the elliptical area Y4, most of the pixels have a hue close to green. On the other hand, in other areas, most pixels have a hue close to blue or gray. As described above, by performing the processing in step S13, the region of the taggant 12 and the other region can be clearly distinguished by the hue even for the article 1 on which the design is printed by the halftone gradation. it can.

  When the smoothing process in FIG. 12 ends, the control unit 101 stores the image data after the smoothing process in the RAM as reference image data, and proceeds to step S102 of the fine substance distribution analysis process in FIG.

  The control unit 101 extracts the distribution position of the taggant 12 included in the reference image data as a feature point (step S102).

  Here, the feature point extraction process in step S102 will be described. The control unit 101 performs HSV conversion on the reference image data (image data after the smoothing process), and calculates hue data. Then, the control unit 101 extracts pixels in a predetermined hue range from the hue data as a taggant region, calculates the center of gravity of the taggant region, and extracts the distribution position information of the taggant 12 as reference feature point data.

  FIG. 15A is the same as the image shown in FIG. FIG. 15B shows the result of the feature point extraction process performed on FIG. That is, FIG. 15B shows the result of performing feature point extraction processing on image data that has not been subjected to smoothing processing. In FIG. 15B, since the color image is grayscale-converted, pixels with high luminance (white or gray on the drawing) correspond to pixels extracted as taggant regions, and low luminance (black on the drawing). ) Corresponds to a pixel that is not extracted as a taggant region. In the result shown in FIG. 15B, obviously, a portion where the taggant 12 does not exist is also extracted as a taggant region. That is, misrecognition of the distribution position of the taggant 12 due to the pattern of the article 1 printed with halftone dots occurs.

  On the other hand, FIG. 16A shows the result of performing the smoothing process in step S101 on the image shown in FIG. FIG. 16B shows the result of the feature point extraction process performed on FIG. That is, FIG. 16B shows the result of the feature point extraction process performed on the image data that has been subjected to the smoothing process. In FIG. 16B, since the color image is grayscale-converted, pixels with high luminance (white or gray on the drawing) correspond to pixels extracted as taggant regions, and low luminance (black on the drawing). ) Corresponds to a pixel that is not extracted as a taggant region. In the result shown in FIG. 16B, obviously, a part where the taggant 12 does not exist is not extracted as a taggant area, and only a part where the taggant 12 exists is extracted as a taggant area. That is, there is no erroneous recognition of the distribution position of the taggant 12 due to the pattern of the article 1 printed with halftone gradation.

The distribution position of the taggant 12 is extracted as a feature point by the above-described feature point extraction processing, position information of the extracted feature point is obtained, and stored as reference feature point data in the RAM or the storage unit 102 (step S103).
Here, the position information of the feature points may be absolute position information or relative position information.

When the absolute position information is adopted, as shown in FIG. 17, the reading range of the image data needs to be the same position, the same direction, and the same range (the same shape and the same area) for the reference article and the target article to be read later. There is. For this reason, it is desirable to add a marker or the like indicating the reading position to the article 1 in advance.
The absolute position information is obtained by normalizing the read image data with a predetermined number of pixels and obtaining the two-dimensional absolute position coordinates (X, Y) of each feature point with the center point as the origin of the normalized image data, for example. Good.

When the relative position information is employed, the reading range of the image data needs to be the same for the reference article 1A and the target article 1B to be read later, as shown in FIGS. 18 (a) and 18 (b). Absent.
As shown in FIG. 19, the relative position information is set data of distances (relative distances) between a certain feature point and a predetermined number of feature points around the feature point. The relative distance set data is repeatedly calculated for each feature point in the reading range.

  When the reference feature point data is acquired from the image data read from the reference article by the processing of step S100 to step S103, the control unit 101 next proceeds to the main processing (step S104 to step S110).

In this process, the individual identification device 100 optically reads the taggant distribution layer 11 attached to the target article (step S104).
When the absolute position information of the feature point is obtained at the time of calculating the reference feature point data (step S102), the target article in step S104 is read using the same image reading device 108 as the reference article and under the same conditions. In addition, as shown in FIG. 17, the reading direction, position, and range are the same as those for reading the reference article. The control unit 101 holds the read image data in the RAM as target image data.

  On the other hand, when the relative position information of the feature points is obtained at the time of calculating the reference feature point data (step S103), the reading of the target article in step S104 is performed under the same conditions using the same image reading device 108 as that of the reference article. Although the reading is performed, the reading direction, position, and range may be different from the reading direction, position, and range of the reference article as shown in FIGS. The control unit 101 holds the read image data in the RAM as target image data.

  Next, the control unit 101 performs a smoothing process on an image read from the target article (hereinafter referred to as a captured image) (step S105). The smoothing process in step S105 is the same as the smoothing process performed in step S101. That is, the control unit 101 calculates the pixel size of the taggant 12 in the captured image based on the resolution of the captured image and the actual size of the taggant 12 (step S11). Next, the control unit 101 determines a predetermined filter size based on the pixel size of the taggant 12 calculated in step S11 (step S12). And the control part 101 applies a smoothing filter to a picked-up image based on the predetermined filter size determined in step S12 (step S13).

  Next, the control unit 101 performs feature point extraction processing for extracting the distribution position of the taggant 12 as target feature point data from the target image data after the smoothing processing (step S106). Here, the feature point extraction process performed on the target article is the same as the feature point extraction process performed on the reference article. That is, the control unit 101 performs HSV conversion on the target object image data (image data after the smoothing process), and calculates hue data. Then, the control unit 101 extracts pixels in a predetermined hue range from the hue data as a taggant region, and calculates the center of gravity of the taggant region, thereby extracting the distribution position information of the taggant 12 as object feature point data. .

The control unit 101 calculates the position information of the extracted feature points and stores them in the RAM as object feature point data.
When the absolute position information is calculated as the reference feature point data, the absolute position information is obtained as the object feature point data.

  On the other hand, when the relative position information is calculated as the reference feature point data, the same shape and the same area as the reference feature point data are first obtained from the reading range B of the object feature point data as shown in FIG. The comparison area 19-1 is cut out, and relative position information (set data of relative distances) of each feature point is obtained as object feature point data for the comparison area 19-1. Next, for example, another comparison region 19-2 is cut out by shifting the cut position by one pixel in the x direction, and relative position information (set of relative distances) of each feature point is obtained as object feature point data for this comparison region 19-2. Data). By repeating this, object feature point data is obtained for each of the comparison regions 19-1, 19-2,..., 19-N.

  Next, the control unit 101 collates the reference feature point data stored in the RAM or the storage unit 102 with the object feature point data obtained in step S106, and determines whether or not they match (step S107). ).

The reference feature point data and the object feature point data can be collated, for example, by obtaining a normalized cross-correlation (NCC: Normalized Cross-Correlation, or ZNCC: Zero-mean Normalized Cross-Correlation). Specifically, the similarity R _NCC in the following formula (3), the similarity R _{ZNCC in the} formula (4), and the like may be obtained as correlation values.

  Here, T (i, j) is the value of the luminance value of the reference image, I (i, j) is the value of the luminance value of the image to be identified, and (i, j) of the coordinates is the reference image. When the reading range A is m pixels × n pixels, the upper left (or lower left) coordinates are (0, 0), and the lower right (or upper right) coordinates are (m−1, n−1). T bar and I bar are the average luminance value of the reference image and the average luminance value of the image to be identified, respectively.

  If the maximum value among the correlation values obtained for all the comparison regions is equal to or greater than a predetermined threshold (when the correlation value is a similarity), the target article is determined to be true. On the other hand, when the maximum correlation value is below the predetermined threshold, it is determined that the reference article and the target article are different individuals (false) (when the correlation value is similarity).

  It should be noted that the “matching” of collation does not have to be limited to exact matching, and includes those within a predetermined allowable range. Further, the allowable range may be arbitrarily set according to the accuracy required for authenticity determination.

If the collation result is “match” (step S108; Yes), the control unit 101 determines that the result is true, and displays the result on the display unit 104, transmits the result to a predetermined result transmission destination, The output process such as registration in the list is performed (step S109). Further, if the collation result is “mismatch” (step S108; No), it is determined to be false, and the result is displayed on, for example, the display unit 104, transmitted to a predetermined result transmission destination, or stored in a predetermined list. Output processing such as registration is performed (step S110).
Thereafter, if there is a next target article, the main process of steps S104 to S110 is repeated, the result is output, and the fine substance distribution analysis process is terminated.

A specific example of the individual identification processing will be described with reference to FIGS.
Sample No. Reference numeral 1 denotes image data 15 read from a reference article and subjected to a smoothing process. Sample No. 1R is Sample No. The same reference article as 1 is re-read under the same conditions, and the image data 16 is smoothed. Sample No. 2, no. Reference numeral 3 denotes image data 17 and 18 which are read from an article in which the taggant distribution layer 11 is formed under conditions different from those of the reference article (sample No. 1) and smoothed.

  In FIG. 1 showing the reference feature point data extracted from Sample No. 1, Sample No. The image 26 showing the object feature point data extracted from 1R, sample No. 2 showing the object feature point data extracted from the sample No. 2; An image 28 showing the object feature point data extracted from 3 is shown.

  For such an example, when the individual identification device 100 performs the fine substance distribution analysis process shown in FIG. 1R is determined to be “match”, and the target article No. 1 in which the taggant distribution layer 11 is formed under conditions different from the reference article 2, No. 3 is determined as “mismatch”.

  When the relative position information is used, first, the taggant distribution layer 11 applied to the reference article (true article) is optically read using the image reading device 108. A range to be read (read range A) is a part of the taggant distribution layer 11. The control unit 101 extracts feature points from the image data read by the image reading device 108, and for each extracted feature point, first, an arbitrary feature point is set as a reference point as shown in FIG. Then, t adjacent points P1, P2,..., Pt adjacent to the reference point are selected, and each distance from the reference point to each adjacent point is calculated. Next, the reference point is changed, t adjacent points P1 to Pt adjacent to the reference point are newly selected, and each distance from the reference point to each adjacent point is calculated. This is repeated for all feature points, and the set data of the relative distances between each feature point and the t feature points adjacent thereto is obtained. This is stored in the storage unit 102 as reference feature point data.

  Next, the taggant distribution layer 11 applied to the target article is optically read. Here, the reading range B of the target article is wider than the reading range A of the reference article, as shown in FIG. The reading range B includes at least a part of the reading range A.

  Next, as shown in FIG. 18C, the control unit 101 sets an arbitrary point in the reading range B (for example, the upper left of the reading range B) as a cutout position, and is the same as the reading range A of the reference image data. A range having the same shape and the same area is cut out as a comparison region 19-1. Specifically, for example, a comparison area of “64 pixels × 64 pixels” (size of 1/8 of the actual image) is cut out from the reading range B of “512 pixels × 512 pixels”. The size of one pixel depends on the reading accuracy of the scanner.

  Note that the size of the comparison area (that is, the reference image reading range A) is determined by a trade-off between the identification accuracy and the calculation speed. That is, when the size of the comparison area is increased (the number of pixels is increased), the identification accuracy increases, but the calculation speed decreases. On the other hand, reducing the size of the comparison area (decreasing the number of pixels) decreases the accuracy but increases the calculation speed.

  The shape of the comparison area (that is, the shape of the reading range A) is not limited to a square, but may be a rectangle or the like.

  The control unit 101 obtains the set data of the relative distance of each feature point for the cut out comparison area 19-1. Then, another comparison region 19-2, 19-3,..., 19-N is cut out while moving the cut-out position, and set data (object feature point data) of the relative distance of each feature point for each comparison region. Ask for.

  When the reference feature point data and the object feature point data are collated, the object feature point data in each comparison area is compared with the reference feature point data, and the objects that match (including those within the allowable range) are also compared. If there is feature point data (relative distance set data), they are determined to be the same.

  A detailed method is described in International Publication No. WO2006 / 092957 for comparison between images having different reading angles. In the individual identification processing according to the present embodiment, the method disclosed in this document (selecting n points in the vicinity of feature points and obtaining, for example, the combination of the cross ratios of the points as their relative position information. The comparison between images may be performed using a method used as a feature amount for matching).

  In the above-described embodiment, when the number of feature points to be extracted is large as a result of the image processing, the thinning process shown in FIG. 21 is applied in the feature point extraction process in steps S102 and S106 in FIG. May be.

  First, the control unit 101 compresses (samples at regular intervals) the read image data (reference image data and object image data) (step S201). Next, the original number of pixels is restored, and a thinned image is created (step S202). Thereafter, the control unit 101 performs image processing such as (A) and (B) described above on the thinned image and extracts feature points (step S203).

  By using the compressed image in this way, feature points can be thinned out and a number of feature points suitable for matching can be extracted. As a result, it is possible to improve the accuracy of collation and to speed up the calculation.

  As described above, according to the individual identification device 100 of the first embodiment, the image obtained by optically reading the taggant distribution layer 11 attached to the reference article is subjected to the smoothing process, and the reference image data Is subjected to predetermined image processing to extract a feature point (position of the taggant 12), and is stored in the storage unit 102 as reference feature point data. Further, the taggant distribution layer 11 attached to the target article to be identified is read by the same method as the reading of the reference image data, the same image processing is performed on the target image data subjected to the smoothing process, and the object feature is obtained. Extract point data. Then, by comparing and comparing the extracted object feature point data with the reference feature point data stored in the storage unit 102, it is determined whether or not the target article and the reference article are the same individual. Is output.

  Thereby, when identifying the article 1 to which the taggant 12 is given, the misrecognition of the distribution position of the taggant 12 due to the pattern of the article 1 printed by the halftone gradation is prevented, and the feature point is extracted with high accuracy. be able to.

[Second Embodiment]
Next, a second embodiment of the present invention will be described.
In the second embodiment, an example in which the individual identification method according to the present invention is applied to an article 8 in which a taggant 82 artificially imparted with a predetermined shape is imparted to the surface of the substrate 80 will be described.

22A is a top view of the article 8, FIG. 22B is a cross-sectional view taken along the line AA of FIG. 22A, and FIG. 22C is an enlarged view of the taggant distribution layer 81 using a loupe.
As shown in FIG. 22, the article 8 has a taggant distribution layer 81 on the substrate 80. In the taggant distribution layer 81, a plurality of taggant 82 having reflectivity different from that of the base material 80 are randomly arranged. The taggant 82 of the taggant distribution layer 81 is arranged on the base material 80 of the article 8 by, for example, printing on the base material 80 mixed with printing ink, or by applying it mixed with an adhesive or the like. The Thereby, each taggant 82 is arranged at a random position.

  In FIG. 22, the article 8 is illustrated as having only the base material 80 and the taggant distribution layer 81, but the upper surface of the taggant distribution layer 81 may be further covered with a transparent plastic or the like. Further, as the base material 80, any material may be used according to the function, property, design, etc. of the article 8. Further, the surface of the article 8 forming the taggant distribution layer 81 is not limited to a flat surface, and may be a curved surface. The taggant distribution layer 81 may be provided on the entire surface of the article or may be a part thereof.

  The taggant 82 is a fine fine particle having a size (about several μm to several hundred μm) that allows the shape and optical characteristics of the surface to be visually recognized when magnified with a loupe.

  As the taggant 82 in the second embodiment, a predetermined character, figure, symbol, pattern, or a combination of these (hereinafter referred to as a design) is used, and a taggant 82 having a predetermined three-dimensional shape is employed. That is, a predetermined design is artificially given so that it can be distinguished from dust or dust. Further, the shape is created so as to be a predetermined shape.

Further, the shapes of the taggants 82 distributed in the taggant distribution layer 81 may be the same or different.
For example, in the example shown in FIG. 22C, taggant 82 having a surface shape of a circle, a triangle, a quadrangle, or a cross shape is mixed. In this way, the taggant 82 having different shapes may be mixed and randomly arranged in the taggant distribution layer 81.
When distributing different shapes of taggant 82, the individual identification by the individual identification process described later is improved. On the other hand, the distribution of the taggant 82 having the same shape can be realized at a lower cost than the case where the taggant 82 having a different shape is provided.

  The design of the taggant 82 is attached so as to be visible from the surface of the taggant 82. For example, it may be applied to the surface of the taggant 82, and the upper surface thereof may be covered with a transparent material.

FIG. 23 is an example of a taggant 82, where (a) is a top view, (b) is a side view, and (c) is a cross-sectional view taken along line AA of FIG. 23 (b). The shape is a hexagonal column, and the letter “D” (design 7) is given to a part.

  Further, the taggant 82 may be formed of a material having optical reflectivity, or a reflective material layer 71 having optical reflectivity may be provided on the surface.

  That is, as shown in FIG. 23C, a design forming layer 72 may be provided on the surface of the base material 73, and a reflective material layer 71 may be further formed on the upper surface thereof.

The design forming layer 72 is provided with the design 7 by printing, engraving or the like. The design 7 is formed using a dye having a color different from that of the base material 73.
For the base material 73 of the taggant 82, metal, resin, or the like is used as in the taggant 12 of the first embodiment.

  The reflective material layer 71 is formed using a material having reflectivity different from that of the base material 81 of the article 8 and the dye 75 of the design 72. For example, similarly to the taggant 12 of the first embodiment, a film having the reflective metal layer 3 (FIG. 2), a thin film having different dielectric constants coated (multilayer thin film 4) (FIG. 3), optical It is preferable to employ one having a diffractive structure layer 5 (FIG. 4), one having a reflective layer 6 having specific reflection characteristics with respect to predetermined irradiation light (FIG. 5), and the like.

The method of individual identification of the article 8 is the same as that in the first embodiment.
In the second embodiment, since each taggant 82 of the taggant distribution layer 81 has a design and a predetermined shape, the accuracy of authenticity determination is improved at the stage of visual recognition with a loupe (step S1).
Further, by performing the fine substance distribution analysis process in step S2, the distribution position information of the taggant 82 arranged at random is stored as reference feature point data, and based on this, the authenticity of the target article is determined. It becomes possible to determine accurately.

  It should be noted that the shape and properties of the article and taggant to which the present invention is applied, the forming method, the design to be applied, and the like are examples, and are not limited to those described in the above-described embodiment. In addition, it is obvious that those skilled in the art can come up with various changes and modifications within the scope of the technical idea disclosed in the present application, and these naturally belong to the technical scope of the present invention. It is understood.

DESCRIPTION OF SYMBOLS 100 ... Individual identification device 101 ... Control part 102 ... Memory | storage part 108 ... Image reading device 1 ... Reference | standard article 11 ... Taggant distribution layer 12 ... Taggant (fine material)
3 ... Reflective metal layer 4 ... Coating layer 5 ... Light diffraction structure layer 6 ... Reflection layer by predetermined irradiation light 7 ... Design ( (Characters, figures, symbols, patterns, or combinations of these)
72... Design formation layer 71... Reflective material layer 8 .. Article 81... Taggant distribution layer 82... Taggant 9. Reference image data 16, 17, 18, 19 ... Object image data 19-1, 19-2, ..., 19-N ... Comparison area 25 ...・ ・ ・ ・ ・ ・ ・ ・ ・ Reference feature point data 26, 27, 28 ・ ・ ・ ・ ・ ・ ・ ・ ・ ・ ・ Object feature point data

Claims (6)

An individual identification device that individually identifies an article having a base material and a taggant distribution layer fixed in parallel with the surface of the base material, and having a pattern printed by halftone gradation,
Storage means for storing, as reference feature point data, distribution position information of the taggant given to a reference article that is the article serving as a reference for individual identification;
Smoothing means for obtaining object data by applying a smoothing filter with a predetermined filter size to a photographed image of a target article that is the article to be identified;
Object feature point extracting means for extracting the taggant distribution position information as object feature point data based on the object data;
Discriminating means for discriminating whether or not the object article and the reference article are the same individual by comparing the object feature point data extracted by the object feature point extracting means and the reference feature point data. When,
An individual identification device comprising: The smoothing means includes
Calculation means for calculating the pixel size of the taggant based on the resolution of the captured image and the actual size of the taggant;
Determining means for determining the predetermined filter size based on the pixel size of the taggant calculated by the calculating means;
Filter means for applying a smoothing filter to the captured image based on the predetermined filter size determined by the determination means;
The individual identification device according to claim 1, comprising:   3. The individual identification apparatus according to claim 2, wherein the determination unit sets the predetermined filter size to be equal to or smaller than a pixel size of the taggant.   The object feature point extracting means extracts pixels in a predetermined hue range as a taggant region, and calculates the center of gravity of the taggant region, thereby extracting the distribution position information of the taggant as the object feature point data. The individual identification device according to claim 3. An individual identification method for individually identifying an article having a base material and a taggant distribution layer fixed in parallel with the surface of the base material, and having a pattern printed with halftone dots,
A storage step of storing, as reference feature point data, the distribution position information of the taggant given to a reference article that is the article serving as a reference for individual identification;
A smoothing step to obtain object data by applying a smoothing filter with a predetermined filter size to a photographed image of a target article that is the article to be identified;
An object feature point extracting step for extracting the distribution position information of the taggant as object feature point data based on the object data;
A determination step of determining whether the target article and the reference article are the same individual by comparing the object feature point data extracted in the target feature point extraction step with the reference feature point data. When,
An individual identification method comprising: A program written in a computer-readable format,
For an article having a base material and a taggant distribution layer fixed in parallel to the surface of the base material and having a pattern printed with halftone dots, the reference article which is the article serving as a reference for individual identification A storage step of storing the distribution position information of the given taggant as reference feature point data;
A smoothing step to obtain object data by applying a smoothing filter with a predetermined filter size to a photographed image of a target article that is the article to be identified;
An object feature point extracting step for extracting the distribution position information of the taggant as object feature point data based on the object data;
A determination step of determining whether the target article and the reference article are the same individual by comparing the object feature point data extracted in the target feature point extraction step with the reference feature point data. When,
A program for causing a computer to execute processing including