WO2012099435A9 - Procédé de différenciation de billets de banque grâce à une approche bayésienne - Google Patents

Procédé de différenciation de billets de banque grâce à une approche bayésienne Download PDF

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Publication number
WO2012099435A9
WO2012099435A9 PCT/KR2012/000548 KR2012000548W WO2012099435A9 WO 2012099435 A9 WO2012099435 A9 WO 2012099435A9 KR 2012000548 W KR2012000548 W KR 2012000548W WO 2012099435 A9 WO2012099435 A9 WO 2012099435A9
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banknote
feature vector
representative
unit cell
bill
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PCT/KR2012/000548
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English (en)
Korean (ko)
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WO2012099435A3 (fr
WO2012099435A2 (fr
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최의선
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노틸러스효성 주식회사
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Priority claimed from KR1020110006275A external-priority patent/KR101232683B1/ko
Priority claimed from KR1020110006280A external-priority patent/KR101232684B1/ko
Application filed by 노틸러스효성 주식회사 filed Critical 노틸러스효성 주식회사
Publication of WO2012099435A2 publication Critical patent/WO2012099435A2/fr
Publication of WO2012099435A9 publication Critical patent/WO2012099435A9/fr
Publication of WO2012099435A3 publication Critical patent/WO2012099435A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/84—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
    • 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
    • G07D7/206—Matching template patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis

Definitions

  • the present invention relates to a banknote discrimination method using a Bayesian approach, and more particularly, after dividing a banknote image generated by scanning a banknote into a predetermined number of unit cells, a sensor measured in each unit cell. After calculating the representative value representing each unit cell by using the data, extracting the feature vector of the banknotes using the calculated representative value of each unit cell, and reducing the dimension of the extracted feature vector through the linear feature extraction method.
  • GML Geographicsian Maximum Likelihood
  • Bayesian Bayesian (Bayes) is able to recognize papers relatively accurately and determine the authenticity of paper money. ian) relates to a banknote discrimination method using an approach.
  • banknote recognition automation equipment As banknote recognition automation equipment is popularized in all of you, there is a demand for a high-speed banknote discrimination apparatus capable of processing various bills at high speed.
  • most banknote discrimination devices are designed to be unsuitable for high speed due to low hardware performance, and the time constraints to recognize paper stocks in a short time and to determine the authenticity of bills require a lot of time for complex data processing and algorithms. It cannot be allocated.
  • the paper recognition algorithm is developed and applied in various forms according to the type or configuration of the bill data acquisition sensor.
  • the acquisition sensor has a one-dimensional array structure
  • the acquired one-dimensional multichannel data is processed using a method such as neural network learning.
  • This method has a problem of gain in terms of recognition speed due to a small amount of acquired data, but a decrease in recognition rate when the number of books is increased or when various bills in various countries are recognized.
  • a differential module device using a two-dimensional contact image sensor is widely developed. Since the two-dimensional contact image sensor is configured to take images of both sides of banknotes or both sides, the amount of data acquired is enormous. Therefore, it is necessary to set a template for a region having high discrimination power between the papers from the acquired banknote images.
  • this template setting process requires a lot of time and computational complexity due to the complex process such as quantization, binary code conversion of vector tablets, and coordinates, and high data resolution is required for accurate comparison. Was not possible.
  • counterfeiting is discriminated using optical or magnetic characteristics of the banknotes, and among them, authenticity determination using infrared data is performed by using an IR sensor of a one-dimensional array structure. After measuring the IR signal strength at a specific position, the counterfeit is determined by determining the presence of a signal compared to a reference value.
  • this one-dimensional array structure method is limited in the amount of data compared to the importance of IR authenticity determination, there is a disadvantage that is sensitive to changes in the skew (shift) and shift (change) environment that occurs during the transfer of bills.
  • an object of the present invention is to divide a bill image generated by scanning a bill into a predetermined number of unit cells, and then calculate a representative value representing each unit cell using sensor data measured in each unit cell. After extracting the feature vectors of banknotes using the calculated representative value of each unit cell as a factor, reducing the dimension of the extracted feature vectors through the linear feature extraction method, GML (Gaussian Maximum) Likelihood)
  • GML Garnier Maximum
  • the present invention provides a method for discriminating banknotes using a Bayesian approach that can recognize paper types relatively accurately and determine the authenticity of banknotes.
  • the present invention generating a bill image using the sensor data obtained by scanning the entire bill, dividing the generated bill image into a predetermined number of unit cells, the Calculating the representative value representing each unit cell by using the obtained sensor data for each divided unit cell, extracting the feature vector of the bill using the calculated representative value for each unit cell, the linear feature extraction method Extracting a representative feature vector by reducing the dimension of the feature vector of the extracted banknote by using a method and applying a Gaussian Maximum Likelihood (GML) classification method to the extracted representative feature vector to perform book type recognition on the banknote It provides a banknote discrimination method using a Bayesian approach that is configured to include.
  • GML Gaussian Maximum Likelihood
  • the banknote discrimination method using the Bayesian approach according to the present invention does not require a separate template setting for a region having high discrimination power between banknote images, as it uses the whole banknote image. Compared to this, high-speed data processing is possible, and it can be applied even when the input image has ultra low resolution.
  • the approximate area (or entire area) of the IR authenticity image is first templated to measure not only the intensity of the IR signal but also the similarity of the dominant shape of the pattern. Since the average value calculation is used after applying the block / mesh structure, it is possible to perform a fast recognition process and also apply to a low resolution CIS image.
  • FIG. 1 is a flow chart illustrating a method of recognizing the winding type of a banknote by applying a banknote discrimination method using a Bayesian approach according to a first embodiment of the present invention.
  • FIG. 2 illustrates an example of dividing a scanned banknote image into a predetermined number of unit cells in recognizing a paper type of a banknote by applying a banknote discrimination method using a Bayesian approach according to the first embodiment of the present invention.
  • FIG. 3 illustrates an example in which a representative value is calculated using an average value of sensor data for each unit cell in recognizing the paper currency of a banknote by applying the banknote discrimination method using the Bayesian approach according to the first embodiment of the present invention.
  • FIG. 4 is a flowchart illustrating a method of determining the authenticity of a banknote by applying a banknote discrimination method using a Bayesian approach according to a second embodiment of the present invention.
  • 5 is an example of dividing into a predetermined number of unit cells for each template region obtained in determining the authenticity of a banknote by applying the banknote discrimination method using the Bayesian approach according to the second embodiment of the present invention.
  • FIG. 6 illustrates an example in which a feature vector is calculated using representative values of sensor data for each unit cell in determining authenticity of a banknote by applying a banknote discrimination method using a Bayesian approach according to a second embodiment of the present invention. that.
  • FIG. 1 is a flowchart illustrating a method of recognizing the paper type of a bill by applying a bill discrimination method using a Bayesian approach according to the first embodiment of the present invention.
  • a bill image is generated by scanning the entire bill to be recognized using a contact image sensor (CIS) (S110), The generated paper image is divided into a predetermined number of unit cells (S120).
  • CIS contact image sensor
  • the bill image generation method in order to prevent only a part of the bill image is scanned by the shaking or vibration that may occur in the transfer operation, it is generally formed larger than the bill size. Therefore, the scanned image includes a bill image and a margin image of the surroundings. Accordingly, it is determined whether the banknotes are aligned, and if there is a tilt, the tilt is corrected by the angle, and only the banknote image excluding peripheral margins is extracted.
  • the number of unit cells for dividing the banknote image may vary depending on the size and resolution of the generated banknote image, typically 10 to 40 unit cells in the horizontal direction and 4 to 20 in the vertical direction It is good to divide each into unit cells.
  • Figure 2 shows an example of dividing the scanned banknote image into 14 ⁇ 7 unit cells according to the first embodiment of the present invention.
  • a representative value representing each unit cell is calculated using sensor data measured in each of the divided unit cells (S130), and a feature vector of a banknote having a calculated representative value for each unit cell is extracted. (S140).
  • sensor data measured in each of the divided unit cells (S130)
  • a feature vector of a banknote having a calculated representative value for each unit cell is extracted.
  • S140 As shown in FIG. 3, there are a plurality of pixels respectively corresponding to the CIS array in one unit cell, and each pixel has sensor data measured at each corresponding CIS pixel.
  • the sensor data is used to calculate one scalar value (representative value) representing each unit cell.
  • the representative value representing each unit cell is an average of sensor data constituting each unit cell, Various factors such as variance or maximum may be applied, and it is preferable to use an average value of sensor data in a unit cell that can most effectively reflect the characteristics of sensor data in each unit cell.
  • each unit cell is composed of 42 unit pixels in total, and assuming that sensor data is obtained for each pixel, 42 units present in the unit cells [7, 2] are present. 9, which is an average value of sensor data of one pixel, is calculated as a representative value A 7,2 of the unit cells [7, 2], and the calculated representative value is a factor of the feature vector representing the unit cell.
  • a representative value representing each unit cell is calculated in this manner, and a bill image feature vector is extracted using the representative value calculated for each unit cell, as shown in FIG.
  • the bill image divided into four unit cells extracts a feature vector X having 14 ⁇ 7 factors.
  • the feature vector (X) of the banknote is extracted by the above-described method, it is possible to discriminate the class of paper by comparing the extracted feature vector factors.
  • a step of reducing the dimension of the feature vector is performed (S150). The reason for reducing the dimension of the extracted feature vector is to reduce the object of computation by removing unnecessary parts of the feature vector, and to extract only the representative feature vectors that are important for the recognition of the denomination. Accordingly, the dimension of the extracted feature vector is reduced by applying the linear feature extraction method, and only the predetermined feature vector representing the feature of the bill image is selected.
  • the linear feature extraction method is a method of analyzing statistical characteristics of sensor data, and the principal component analysis (PCA) and linear discriminant analysis (LDA) are representative examples.
  • Principal Component Analysis (PCA) is a non-historical statistical technique that can effectively find image features and is the most optimal technique for performing dimensional reduction, but it is not ideal for classification purposes, such as judging species. Therefore, in the present embodiment, it is effective to reduce the dimension of the feature vector using the linear discrimination method (LDA), and the steps of reducing the dimension of the extracted feature vector using the linear discrimination method (LDA) will be described below. do.
  • Linear Discrimination is a technique of calculating the optimal linear discrimination matrix ⁇ T which maps the extracted feature vectors into the class space and enables class identification based on Euclidean distance.
  • the ratio between the class variance ( ⁇ 2 B : Between class variance) and the class variance ( ⁇ 2 W : Within class variance) for each volume of tens of thousands of note image feature vectors ( ⁇ 2 B / ⁇ 2 W ) Finding the linear discrimination matrix ⁇ T which can maximize, and apply the linear discrimination matrix to the extracted feature vector (X), the dimension of the extracted feature vector (X) as shown in Equation 1 below
  • Equation 1 By reducing the value, a representative feature vector Y that better represents the feature of the banknote image can be obtained.
  • the representative feature vector Y calculated as described above has a relatively lower dimension than the feature vector X originally extracted from the bill image, and at the same time more effectively represents the feature of the bill image.
  • the present invention recognizes the paper-species for the corresponding bill using the Gaussian Maximum Likelihood (GML) classification method on the obtained representative feature vector (S160). That is, using the average vector and variance matrix previously calculated for each class by using the representative feature vector calculated through the above process, it is calculated which probability class is the most likely to be included, and the probability value corresponds to the highest class. Sort the bills. For example, when classifying domestic species, the average vector and variance matrix of each of 1,000 won, 5,000 won, 10,000 won, and 50,000 won bills are used to calculate the probability that the representative feature vector will be included in the four types. The bill is classified by the paper type whose calculated value corresponds to the largest value.
  • GML Gaussian Maximum Likelihood
  • a method of calculating the probability that the representative feature vector is included in the corresponding paper type may be performed by Equation 2 below.
  • Equation 2 ML is a value representing the probability that the representative feature vector is included in the denomination class. According to Equation 2, the higher the probability that the representative feature vector is included in the denomination class, It is more likely that the species is. Therefore, the ML value to be included in the class is calculated by using the representative feature vector and the mean vector and variance matrix of each kind previously stored in the database, and the calculated values are compared with each other to compare the calculated values. As a judgment of the paper species of the bill, the paper sheet recognition is performed.
  • the present invention can perform the scoop recognition by using the Bayesian approach based on the representative value for each unit cell for the bill image, compared to the conventional scoop recognition method using a conventional neural network circuit In addition, even low-resolution bill images can be recognized relatively accurately.
  • FIG. 4 is a flowchart illustrating a method of determining the authenticity of banknotes by applying a banknote discrimination method using a Bayesian approach according to a second embodiment of the present invention.
  • the IR authenticity security element In order to determine the authenticity of a banknote by applying the banknote discrimination method using the Bayesian approach according to the present invention, first, by using the infrared sensor to obtain the sensor data obtained by scanning the whole banknote to determine the authenticity, the IR authenticity security element
  • the template regions in which the infrared pattern exists are specified based on the prior position information of the apparatus (S210), and the specified template regions are divided into a predetermined number of unit cells (S220).
  • the number of unit cells for dividing the template region may vary according to the scan area of the specified template region, and typically 2 to 20 unit cells in the horizontal direction and 2 to 10 units in the vertical direction for each template region. Splitting into cells is fine.
  • FIG. 5 shows an example of dividing a predetermined number of unit cells for each template region acquired according to the second embodiment of the present invention.
  • a representative value representing each unit cell is calculated using the sensor data measured in each divided unit cell (S230), and a feature vector of a banknote having a calculated representative value for each unit cell is extracted. (S240).
  • S230 the sensor data measured in each divided unit cell
  • S240 a feature vector of a banknote having a calculated representative value for each unit cell is extracted.
  • S240 a plurality of pixels respectively corresponding to the IR sensor array exist in one unit cell, and each pixel has sensor data measured by each corresponding IR sensor.
  • the sensor data is used to calculate one scalar value (representative value) representing each unit cell.
  • the representative value representing each unit cell is an average of sensor data constituting each unit cell, Various factors such as variance or maximum may be applied, and it is preferable to use an average value of sensor data in a unit cell that can most effectively reflect the characteristics of sensor data in each unit cell.
  • each unit cell is composed of 42 unit pixels in total, and assuming that sensor data is obtained for each pixel, each unit cell exists in the unit cell A [1,4]. 9, which is an average value of the sensor data of 42 pixels, is calculated as the representative value A 1,4 of the unit cell A [1,4], and the calculated representative value is a factor of the feature vector representing the unit cell.
  • a representative value representing each unit cell is calculated in this manner, and a feature vector having a representative value calculated for each unit cell is extracted. Accordingly, the template region divided as shown in FIG. A feature vector (X) having as many factors as the number of unit cells is extracted.
  • the authenticity of the bill is determined by comparing the extracted feature vector factors.
  • the feature vector factor is too large, it is difficult to perform a robust and fast authenticity determination.
  • a step of reducing the dimension of the feature vector is performed (S250). The reason for reducing the dimension of the extracted feature vectors is to reduce the computational object by removing unnecessary portions of the feature vectors, and to extract only representative feature vectors that are important for authenticity determination. Accordingly, the dimension of the extracted feature vector is reduced by applying the linear feature extraction method, and only a predetermined feature vector representing the characteristic of the infrared pattern is selected.
  • the linear feature extraction method is a method of analyzing statistical characteristics of sensor data, and the principal component analysis (PCA) and linear discriminant analysis (LDA) are representative examples.
  • PCA principal component analysis
  • LDA linear discriminant analysis
  • PCA principal component analysis
  • it is effective to reduce the dimension of the feature vector by using principal component analysis (PCA), which is a non-statistical statistical technique that can effectively find image features.
  • PCA principal component analysis
  • feature vectors extracted by using principal component analysis (PCA) The steps for reducing the dimension of the circuit will be briefly described.
  • Principal component analysis extracts a few major factor values that can represent the feature vector from several factors constituting the extracted feature vector (X), which is composed of the major factor values
  • PCA Principal component analysis
  • the representative feature vector (Y) calculated as described above has a relatively lower dimension than the feature vector (X) extracted initially, and at the same time more effectively represents a feature for authenticity determination of banknotes.
  • One of the characteristics of the principal component analysis described above is that for a data group distributed over the same time, a vector of directions having a large degree of variance can be obtained.
  • the eigenvalues and corresponding eigenvectors can be obtained.
  • the vector with the higher eigenvalue becomes an important element of the data group, and the smaller eigenvalue vector This means that the vector is less important than the first vector. Therefore, when principal component analysis is performed using tens of thousands of pneumatic features, eigenvalues for pneumococcal and corresponding eigenvectors can be obtained. do.
  • the method of calculating the probability that the representative feature vector is included in the corresponding pneumatic class region may be performed through a process similar to that of [Equation 2] described in the case of the first embodiment.
  • ML shown in [Equation 2] is a value representing the probability that the representative feature vector is included in the pneumococcal class of the subject species, and the higher the probability that the representative feature vector is included in the pneumococcal class increases the probability of the pneumoconiosis. . Therefore, the ML value to be included in the authenticity class is calculated using the representative feature vector and the authenticity class average vector and the variance matrix previously stored in the database. When the calculated value is less than or equal to the preset reference value, the banknote is determined as counterfeit.
  • the present invention determines the authenticity of banknotes using a Bayesian approach based on a representative value for each unit cell for the region in which the infrared pattern exists, thereby determining authenticity of the banknote authenticity using a conventional neural network. Not only can it be performed quickly, but the authenticity of banknotes can be accurately determined even with a low resolution banknote image.
  • the banknote discrimination method using the Bayesian approach according to the present invention is capable of processing data at a higher speed than the conventional discrimination method, and can be applied even when the input image has an ultra low resolution. Can be applied.

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Abstract

La présente invention concerne un procédé permettant de différencier des billets de banque grâce une approche bayésienne. Plus particulièrement, la présente invention concerne un procédé de différenciation de billets de banque grâce à une approche bayésienne, qui comprend la division d'une image de billet de banque générée par la numérisation d'un billet de banque en un nombre prédéterminé de cellules unités, le calcul d'une valeur représentative de chaque unité à l'aide des données de capteur mesurées dans chaque cellule unité, l'extraction d'un vecteur de caractéristique de billet de banque qui utilise la valeur représentative calculée de chaque unité comme facteur, la réduction de la dimension du vecteur de caractéristique extrait via une extraction de caractéristique linéaire, et la réalisation de la différenciation de billets de banque sur le vecteur de caractéristique, dont la dimension est réduite, en utilisant une classification du maximum de vraisemblance gaussienne (GML). Selon la présente invention, en comparaison aux procédés conventionnels de différenciation de billets de banque à l'aide d'un réseau neutre, la reconnaissance de la classe de billets de banque et le repérage de billets de banque contrefaits peut être réalisée rapidement, la reconnaissance de la classe de billets de banque peut être réalisée de façon relativement précise, et un billet de banque contrefait peut être différencié même à partir d'une image de billet de banque ayant une faible résolution.
PCT/KR2012/000548 2011-01-21 2012-01-20 Procédé de différenciation de billets de banque grâce à une approche bayésienne WO2012099435A2 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR10-2011-0006280 2011-01-21
KR1020110006275A KR101232683B1 (ko) 2011-01-21 2011-01-21 베이시안 접근법을 이용한 권종 인식 방법
KR1020110006280A KR101232684B1 (ko) 2011-01-21 2011-01-21 베이시안 접근법을 이용한 지폐 진위 감별 방법
KR10-2011-0006275 2011-01-21

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2586568A (en) * 2019-04-18 2021-03-03 Gerard Rohan Jayamanne Don A technique for detecting counterfeit banknotes, travelers cheques or money orders by a method to be used with smart phone applications (apps)

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JP6858525B2 (ja) 2016-10-07 2021-04-14 グローリー株式会社 貨幣分類装置及び貨幣分類方法
WO2020003150A2 (fr) * 2018-06-28 2020-01-02 3M Innovative Properties Company Détection de nouveauté à base d'images d'échantillons de matériau

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JPH09171552A (ja) * 1995-10-18 1997-06-30 Fuji Xerox Co Ltd 画像認識装置
GB2344446A (en) * 1998-12-02 2000-06-07 Mars Inc Classifying currency items
KR100751855B1 (ko) * 2006-03-13 2007-08-23 노틸러스효성 주식회사 웨이블렛 변환을 이용한 권종인식방법
KR101056666B1 (ko) * 2006-03-13 2011-08-12 노틸러스효성 주식회사 웨이블렛 변환을 이용한 권종인식방법

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2586568A (en) * 2019-04-18 2021-03-03 Gerard Rohan Jayamanne Don A technique for detecting counterfeit banknotes, travelers cheques or money orders by a method to be used with smart phone applications (apps)

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