JPH1069558A - Paper money identifying method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately identify paper sheets at high speed without being affected by dirt or distortion by performing truth/false discrimination through identifying processing using 1st wavelength light and further, performing truth/ false discrimination while using 2nd wavelength light. SOLUTION: Multilevel gradation data based on a photosensor for paper moneys are fetched (S1). The predictive processing of an irregular component such as a dirty component is performed while using data based on the light of a 1st wavelength (in an infrared color) in the fetched gradation data (S2). It is discriminated whether the paper money to be identified is truth or false (S3). Concerning the paper money to be identified discriminated as false, it is fixed as false as it is. Concerning the paper money to be identified discriminated as truth, further, the differential square sum between the data of a representative point selected by a separate mask and the same data in the reference waveform of truth is calculated in the data provided by the light of a 2nd wavelength (in red) (S4). The discrimination is performed by comparing the value of this differential square sum with a predetermined threshold value (S5).

Description

DETAILED DESCRIPTION OF THE INVENTION

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for identifying paper sheets such as banknotes and securities, and more particularly to a method for identifying highly accurate and high-speed judgments by suppressing the influence of various stains on the identified banknotes. About possible identification methods.

2. Description of the Related Art Japanese Patent Laid-Open No.
-215293. In this publication, a bill is divided into a plurality of zones, detection data for each zone is compared with reference data predetermined for each zone, and the bill is identified based on a comparison result in each zone. In the bill discriminating method, a plurality of bills are set in accordance with the front and back, the orientation, and the displacement at the time of discrimination, and the data of each zone are totaled for one bill, and a standard value is determined by a ratio value to the total value. It is stored as pattern data, a total value of the detection data is obtained, and a ratio value to the total value is calculated as detection pattern data, and whether or not the detection pattern data is within an allowable value range of the reference pattern data is determined. Is determined, and the absolute value of the difference between the reference pattern data and the detected pattern data is calculated for each of the zones, and the total is calculated. The total value banknote identification method and performing to the bill validator determines whether less than the allowable value is disclosed.

However, in the above-mentioned conventional technology, there is a problem that a fake bill is erroneously recognized as a genuine bill because variations in identification due to dirt, distortion and other reasons are allowed. Yes, this causes a reduction in identification accuracy. The present invention has been made in order to solve the problems caused by such a conventional method, and a method for accurately and quickly identifying paper sheets without being affected by dirt, distortion, and the like. The purpose is to provide.

According to the method of the present invention, a genuine bill is obtained from an image of a bill to be identified, which is taken in using light of a first wavelength.
A first step of judging a counterfeit note, and a true step based on representative pixel data selected by a counterfeit note judging mask of an image of a banknote to be identified taken using light of a second wavelength different from the first wavelength. It consists of a second step of determining a voucher or a counterfeit voucher. The mask is optimized by using a genetic algorithm so as to ensure bill identification accuracy. Then, the second step is applied only to the image of the identified banknote determined to be genuine in the first step.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a perspective view of a bill discriminating method according to an embodiment of the present invention;
FIG. 1 is a flowchart showing the basic algorithm of the present embodiment. In the figure, when a program is started by inserting a bill (step S1), multi-valued light / dark data by an optical sensor of the bill is captured. The optical sensor emits light of two wavelengths selected from light emitting diodes that emit light of various wavelengths such as red, infrared, blue, and green, and a bill to be identified that exits from the light emitting diode. And a light receiving element for receiving the light that returns after the light has arrived. A process of predicting an irregular component such as a dirt component is performed using data of light of a first wavelength (for example, infrared color) of the captured grayscale data. In step S3, it is determined whether the identified bill is genuine or fake. The banknote to be identified determined to be a counterfeit note in this step S3 is determined as a counterfeit note as it is. On the other hand, among the data obtained by the light of the second wavelength (for example, red), the data of the representative point selected by the separation mask and the data of the genuine bill are further included in the bill to be identified determined to be genuine in step S3. The sum of squared differences between the reference waveform and the same data is calculated (step S4). The value of the sum of squared differences calculated in step S5 is compared with a predetermined threshold value to determine a genuine note or a fake note. As a result of this determination, if the calculated value is smaller than the threshold value, a genuine bill is determined, and if the calculated value is larger than the threshold, a fake bill is determined. Next, details of the flowchart of FIG. 1 will be described. In the predictive identification process of step S2, as shown in the flowchart of FIG. 2 and the conceptual diagram of FIG. 3, when a bill to be identified is inserted into a bill validator (not shown) in step S20, an LED (light emitting diode) is inserted. With the image sensor (line sensor) consisting of a photodetector and a light receiving element, the image data of the bill surface is represented by a luminance value (sensor value) on the vertical axis and position information (point) on the horizontal axis.
(See FIG. 3A). Since the number of data points used in the calculation in the denomination / direction determination is only used for the denomination and direction determination, it does not need to be all the points of the bill, but the minimum number of points required for the denomination and direction determination Is fine. In step S21, of the obtained waveform data (banknote data) based on the second wavelength light, three denominations and four directions (one denomination in FIG. 3) previously obtained from the genuine bill by the image sensor are used. Are compared with the reference waveform data for each direction of front right A, front left B, back right C, and back left D in the case of four directions (step S2).
2) The minimum sum of squares of the difference for each point is calculated (see FIG. 3B). Next, the obtained sums of squared differences (matching degrees) in the respective directions are compared, and in step S23, the direction of the direction-specific reference waveform data having the minimum value is determined as the bill insertion direction (see FIG. 3C). When the determination of the denomination / injection direction is completed, a transport deviation correcting process is performed next. In this process, as shown in FIG. 4, a deviation width K (for calculation) is set in step S51. This shift width K is a value between a minimum shift width value at which a transport shift can occur and a maximum shift width, and starts from the minimum value. Then, in step S52, data shifted by K, which is the set input signal of the bill conveyed, is created. Next, in step S53, the step S
From the data shifted by K created in 52, data at a plurality of target locations is extracted. In step S54, the absolute value sum of the difference between the extracted data and the corresponding data of the reference waveform is calculated. If the total value obtained in step S55 is the minimum, the value of K at that time is temporarily recorded as the calculated deviation width. The above operation is repeated from the minimum value to the maximum value of K. By doing this,
Every time the value of K changes between the minimum value and the maximum value, a cumulative value of the difference from the reference waveform is obtained, and the value of K which is the minimum cumulative value is updated each time. Then, by setting the finally remaining value of K as a target conveyance deviation width, the conveyance deviation value can be accurately obtained. If further accuracy is required, instead of calculating the absolute value of the difference in step S54, the square of the difference is calculated (step S541). It is also possible to determine as the deviation width. Step S5
The extraction of the data at a plurality of locations 3 is performed by a method as shown in FIG. That is, in step S531, a basic representative waveform is created from a banknote (closed note) free from dirt and tear.
In step S532, a displacement representative value K is set and a displaced basic representative waveform is created in the same manner as in the case of calculating the transport displacement width. In step S533, a difference value (absolute value) between each position of the shifted basic representative waveform and the original basic representative waveform.
Record In the above operation, the value of K is changed from the minimum value to the maximum value, and the difference value is sequentially recorded, and in step S534, the minimum value of the difference value at each position is calculated. Finally, a plurality of points are selected in ascending order of the smallest difference value obtained in step S535, and this point is set as a plurality of points from which data should be extracted. In the right half of FIG. 5, a conceptual diagram of a waveform in the middle is shown, and in the left half, a conceptual diagram of a step of selecting a plurality of locations is shown. After the transport deviation correcting process, the level of the captured image of the banknote to be identified is adjusted, and the process proceeds to a process of reducing the identification error due to an irregular component such as a dirt component from the image. This processing is performed using, for example, an auto (AR) regression model. That is, as shown in FIGS. 6 to 8, the present processing method is largely divided into a pre-processing and a processing at the time of inserting a bill.
FIG. 8 shows a flowchart of the operation. The pre-process is a process that is created by learning a fluctuation component estimation model of bill sensor input data. As shown in FIG. 7, first, in step S151, a plurality of genuine bills (new bills) are sensed to The sensor signals of the brightness and density of the above images and characters are obtained, and a reference waveform (for example, an average value data waveform) as reference data is obtained from each sensor signal. Next, in step S152, a fluctuating component such as dirt or distortion is extracted for each genuine note from the genuine note data using the reference waveform. Then, the data of the fluctuation component extracted in step S153 is regarded as a periodic time series signal, and an equation as an autoregressive model is used.

(Equation 1) The coefficients a1, a2, of the equation for contamination at a certain time represented by
... Means to find ap. The learning data in this case is comparable to the time-series signal data of the dirt component when several banknotes are arranged and input. The periodic time series signal of the dirt component created in this way is learned by an autoregressive analysis method in step S4, and as a result of the learning, an estimation model of a dirt variation component of one banknote is created. After the pre-processing is performed in this manner, the flow proceeds to a processing at the time of insertion for performing a true / false determination when a bill is actually inserted. In the bill insertion process, first, a sensor signal from the bill inserted in step S251 is input. Next, the difference between the signal input in step S252 and the reference waveform is calculated to obtain a stain as a variation component.
The distortion component is extracted. In step S253, based on the data of the dirt and distortion components obtained in step S252, dirt estimation using the estimation model is performed by an auto-regression model technique, and a predicted value is calculated. Here, the estimation method will be described. Since the estimation model is obtained by the pre-processing, the dirt component predicted from the estimation model of the autoregressive analysis is calculated based on Equation 1 and the input dirt component data of the banknote. It is calculated (step S253). The waveform of dirt as a fluctuation component of the input bill thus obtained,
From the fluctuation component waveform of the estimation model, the prediction error is calculated in step 254. From this result, if the input bill is within the predetermined prediction error range, it is determined that the bill is genuine. Is determined to be a counterfeit note (step S25).
5). Of the banknotes subjected to the prediction error process as described above, those that are determined to be counterfeit are determined to be counterfeit, and those that are determined to be genuine are further subjected to identification processing using the second wavelength light. . This discrimination process employs a method of using a separation mask for counterfeit bill discrimination to discriminate banknote data to be used in the process and increasing the calculation speed. A method of creating a counterfeit judgment mask will be described with reference to FIG. In order to create a separation mask, a point where the total scale (the number of points used for determination) is within 10 points and the distance scale from the genuine bill (see Equation 2) can be taken large is GA
(Genetic algorithm), and optimization for improving the separation accuracy between a fake note and a true note, which will be described later, and for increasing the calculation speed was performed.

(Equation 2) The standard deviation (σ) of genuine bill is used to calculate the gene evaluation scale.
And the value (Fmin) closest to the genuine bill in the modified bill data. Re-identification is performed using the separation mask thus obtained. This is because, in order to separate a counterfeit bill erroneously identified as a genuine bill from the original genuine bill in the identification processing based on the prediction in the step S2, the sum of squared differences between the masked genuine bill reference waveform and the input waveform is calculated. This is the step of calculating and identifying.

According to the present invention, as described above, the authenticity is determined by the identification processing using the first wavelength light, and then the authenticity is determined using the second wavelength light, so that the identification difficulty is high. The effect of accurately identifying counterfeit bills can be expected.

[Brief description of the drawings]

FIG. 1 is a flowchart showing an embodiment of a bill discriminating method according to the present invention.

FIG. 2 is a flowchart of a denomination / direction determination process.

FIG. 3 is a conceptual diagram of a denomination / direction determination process.

FIG. 4 is a flowchart illustrating a conveyance deviation width calculation method.

FIG. 5 is a flowchart illustrating a method of selecting data at a plurality of locations from input data.

FIG. 6 is a conceptual diagram of a prediction process using an irregular component such as a stain.

FIG. 7 is a flowchart of a pre-processing.

FIG. 8 is a flowchart of a bill insertion process.

FIG. 9 is a conceptual diagram illustrating a method for creating a counterfeit note determination mask.

Claims (3)

[Claims] 1. A first step of judging a genuine note or a counterfeit note from an image of a banknote to be identified captured using light of a first wavelength, and light of a second wavelength different from the first wavelength. And a second step of judging a genuine bill or a fake bill based on representative pixel data selected by an imitation bill determination mask of an image of a bill to be identified taken in using the method. 2. The method according to claim 1, wherein the mask is optimized using a genetic algorithm. 3. The bill discriminating method according to claim 1, wherein the second step is applied to an image of a bill to be discriminated determined as a genuine note in the first step.