JPH1063847A - Pattern collating device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve a recognition ratio. SOLUTION: Second-dimensional discrete Fourier transformation(DFT) is operated to picture data (e) of a fingerprint to be collated, and collation Fourier picture data (f) are prepared. The collation Fourier picture data are synthesized with registered Fourier picture data (b) of a registered fingerprint which is prepared by operating the similar processing, and an amplitude suppression processing and the DFT are operated to synthesized Fourier picture data (d). Then, the correlation peak with the maximum intensity of correlation components is extracted from a correlation component area S0 of synthesized Fourier picture data (h) to which the DFT is operated. A weighting processing corresponding to a distance from this correlation peak is operated to the correlation components of each picture element in the S0. Then, high order (n) picture elements with high intensity are extracted from among those correlation components, and the average is defined as a correlation value, and compared with a threshold value.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a pattern collating apparatus for collating an N-dimensional pattern [for example, voice (one-dimensional), fingerprint (two-dimensional), three-dimensional (three-dimensional)] based on spatial frequency characteristics. Things.

[0002]

2. Description of the Related Art In recent years, in fields requiring personal recognition such as access control to computer rooms and important machine rooms, access control to computer terminals and financial terminals of banks, etc.
A voice collation device and a fingerprint collation device are being adopted in place of conventional passwords and ID cards.

[Prior application] The present applicant has previously filed Japanese Patent Application No. 6-6 / 1994.
No. 8171 proposed a "fingerprint matching device". In this fingerprint collating apparatus, one input image data is created by juxtaposing the original image data (two-dimensional pattern data) of the registered fingerprint and the original image data (two-dimensional pattern data) of the collated fingerprint. Then, a first two-dimensional discrete Fourier transform is performed on the input image data, and a second two-dimensional discrete Fourier transform is performed on the input image data on which the Fourier transform has been performed. From the predetermined correlation component area appearing in the applied input image data (synthesized Fourier image data), upper n pixels having a higher correlation component intensity are extracted, and the average of the extracted n pixel correlation component intensities is calculated as a correlation value. And compare with the threshold. If the correlation value is higher than the threshold value, it is determined that the registered fingerprint matches the collation fingerprint.

[Prior application] The present applicant has previously proposed a "pattern matching device" as Japanese Patent Application No. 7-108526. In this pattern matching device, two-dimensional discrete Fourier transform is performed on the image data (two-dimensional pattern data) of the matching fingerprint to create matching Fourier image data. Then, the matching Fourier image data is combined with the registered Fourier image data of the registered fingerprint created by performing the same processing, and the combined Fourier image data is subjected to amplitude suppression processing (log processing). Apply a two-dimensional discrete Fourier transform. Then, from the predetermined correlation component area appearing in the synthesized Fourier image data subjected to the two-dimensional discrete Fourier transform, upper n pixels having a higher intensity of the correlation component are extracted, and the intensity of the correlation component of the extracted n pixels is extracted. Is used as a correlation value and compared with a threshold value. If the correlation value is higher than the threshold value, it is determined that the registered fingerprint matches the collation fingerprint.

[0005]

However, in the above-mentioned prior application, in the combination of the principal and another person, the data of the upper n pixels having a high intensity of the correlation component is scattered in the correlation component area, and the principal and the principal are not scattered. In some combinations, they are located in the same position to some extent. In the prior application, this is not taken into consideration, and the upper n pixels having a high correlation component intensity are directly extracted from the correlation component area, and the average of the extracted correlation component intensities of the n pixels is obtained as a correlation value. Therefore, the correlation value between the person and the other person greatly varies, and may exceed the threshold value, and the person and the other person may be erroneously determined to be the same.

The present invention has been made to solve such a problem, and an object of the present invention is to provide a pattern matching device capable of increasing a recognition rate.

[0007]

In order to achieve the above object, a first invention (an invention according to claim 1) provides an N-dimensional discrete Fourier transform to an N-dimensional pattern data of a registered pattern to register the data. Create Fourier N-dimensional pattern data, apply N-dimensional discrete Fourier transform to the N-dimensional pattern data of the matching pattern to create matching Fourier N-dimensional pattern data, and register registered Fourier N-dimensional pattern data and matching Fourier N-dimensional pattern data. And performs one of an N-dimensional discrete Fourier transform and an N-dimensional discrete inverse Fourier transform on the resultant Fourier N-dimensional pattern data obtained by the synthesis, and obtains a synthesized Fourier N-dimensional pattern subjected to the Fourier transform. Correlation component for each individual data constituting the N-dimensional pattern data of the correlation component area appearing in the data The peak having the highest intensity is obtained as a correlation peak, and a weighting process according to the distance from the correlation peak is performed on the correlation components of each individual data constituting the N-dimensional pattern data of the correlation component area. The registered pattern is compared with the matching pattern based on the strength of the correlation component.

According to the present invention, the N-dimensional pattern data of the collation pattern is subjected to N-dimensional discrete Fourier transform to generate collation Fourier N-dimensional pattern data, and the same processing as the collation Fourier N-dimensional pattern data is performed. The registered Fourier N-dimensional pattern data created in this way is synthesized, and the synthesized Fourier N-dimensional pattern data is subjected to N-dimensional discrete Fourier transform or N-dimensional discrete inverse Fourier transform. Then, among the correlation components for each data constituting the N-dimensional pattern data of the correlation component area appearing in the synthesized Fourier N-dimensional pattern data subjected to the Fourier transform, the one having the largest intensity is obtained as a correlation peak. A weighting process in accordance with the distance from the correlation peak is performed on the correlation component of each data constituting the N-dimensional pattern data of the correlation component area. Based on the strength of these correlation components, the registered pattern and the matching pattern are compared. Is collated.

The second invention (the invention according to claim 1) is the first invention.
Instead of “performing one of N-dimensional discrete Fourier transform and N-dimensional discrete inverse Fourier transform on synthesized Fourier N-dimensional pattern data” in the invention, “amplitude suppression is performed on synthesized Fourier N-dimensional pattern data After processing, one of N-dimensional discrete Fourier transform and N-dimensional discrete inverse Fourier transform is performed. " According to the present invention, the synthesized Fourier N-dimensional pattern data is subjected to amplitude suppression processing such as log processing and √ processing, and then subjected to N-dimensional discrete Fourier transform or N-dimensional discrete inverse Fourier transform.

The third invention (the invention according to claim 3) is the first invention.
Instead of "creating registered Fourier N-dimensional pattern data by applying N-dimensional discrete Fourier transform to N-dimensional pattern data of a registered pattern" in the invention, "N-dimensional discrete Fourier transform to N-dimensional pattern data of a registered pattern" And then perform amplitude suppression processing to create registered Fourier N-dimensional pattern data. " In addition, instead of “creating a collated Fourier N-dimensional pattern data by applying an N-dimensional discrete Fourier transform to the N-dimensional pattern data of the collation pattern”, an “N-dimensional discrete Fourier transform is performed on the N-dimensional pattern data of the collation pattern”. And then performing amplitude suppression processing to create matching Fourier N-dimensional pattern data. " According to the present invention, N-dimensional discrete Fourier transform is performed on N-dimensional pattern data of a registered pattern, and log
By performing amplitude suppression processing such as processing and √ processing,
Registered Fourier N-dimensional pattern data is created. Further, N-dimensional discrete Fourier transform is performed on the N-dimensional pattern data of the collation pattern, and amplitude suppression processing such as log processing or √ processing is performed, so that collation Fourier N-dimensional pattern data is created.

[0011]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail based on embodiments. FIG. 2 is a block diagram of a fingerprint matching device according to an embodiment of the present invention. In FIG. 1, reference numeral 10 denotes an operation unit, 20 denotes a control unit, and the operation unit 10 includes a numeric keypad 10-1 and a display (LCD).
A fingerprint sensor 10-3 is provided together with 10-2.
The fingerprint sensor 10-3 includes a light source 10-31 and a prism 10-
32, and a CCD camera 10-33. The control unit 20 includes a control unit 20-1 having a CPU,
ROM 20-2, RAM 20-3, hard disk (HD) 20-4, and frame memory (FM) 20-5
, An external connection unit (I / F) 20-6, and a Fourier transform unit (FFT) 20-7, and the ROM 20-2 stores a registration program and a collation program.

[Registration of Fingerprint] In this fingerprint collating apparatus, a user's fingerprint is registered as follows. That is,
Before operation, the user inputs the ID number assigned to the user using the numeric keypad 10-1 (step 301 shown in FIG. 3), and then enters the prism 1 of the fingerprint sensor 10-3.
Put your finger on 0-32. Light source 1 for prism 10-32
Light is irradiated from 0-31 and the prism 10-32
In the concave portion (valley line portion) of the fingerprint that does not contact the surface of
Light from -31 is totally reflected and reaches the CCD camera 10-33. Conversely, in the convex portion (ridge portion) of the fingerprint contacting the surface of the prism 10-32, the total reflection condition is broken, and the light source 10-31
Light from is scattered. As a result, a fingerprint pattern with contrast is obtained, in which the valley portion of the fingerprint is bright and the ridge portion is dark. The pattern of this collected fingerprint (registered fingerprint)
By the A / D conversion, the image is provided to the control unit 20 as a grayscale image (image data: two-dimensional pattern data) having 320 × 400 pixels and 256 gradations.

The control unit 20-1 stores the image data of the registered fingerprint provided by the operation unit 10 in the frame memory 20-
5 (step 302), and a reduction process is performed on the captured registered fingerprint image data (step 303). This reduction processing is performed for 320 × 400 pixels, 2
Original image data of 56 gradations in the x direction (horizontal direction)
In the y direction (vertical direction), thinning is performed at a 3-pixel pitch except for the upper and lower ends by 8 pixels, except for the left and right ends, which are 32 pixels each. Thereby, the image data of the registered fingerprint becomes 6
The image data is reduced to image data of 4 × 128 pixels and 256 gradations (see FIG. 5).

The control unit 20-1 sends the reduced registered fingerprint image data (see FIG. 1A) to the Fourier transforming unit 20-7, and adds the two-dimensional discrete Fourier transform to the registered fingerprint image data. Perform a transform (DFT) (step 30)
4). As a result, the image data of the registered fingerprint shown in FIG. 1A becomes Fourier image data (registered Fourier image data) as shown in FIG. Control unit 20
-1, the Fourier image data is stored as a file in the hard disk 20-4 in correspondence with the ID number as original image data of the registered fingerprint (step 305).

The two-dimensional discrete Fourier transform is described in, for example, “Introduction to Computer Image Processing, edited by Japan Industrial Technology Center, published by Soken Shuppan Co., Ltd., pp. 44-45 (Document 1)”. .

[Fingerprint Verification] In this fingerprint verification apparatus, verification of a user's fingerprint is performed as follows. That is, during operation, the user inputs the ID number assigned to the user using the numeric keypad 10-1 (step 401 shown in FIG. 4), and then enters the prism 10 of the fingerprint sensor 10-3.
Put your finger on -32. Thus, in the same manner as in the case of fingerprint registration, the pattern of the collected fingerprint (collation fingerprint) is 3
As a grayscale image (image data: two-dimensional pattern data) of 20 × 400 pixels and 256 gradations, the control unit 2
Given to 0.

When an ID number is given via the numeric keypad 10-1, the control unit 20-1 operates the hard disk 20-
The Fourier image data of the registered fingerprint corresponding to the ID number is read from the registered fingerprint filed in Step 4 (Step 402). Further, the control unit 20-1 fetches the image data of the collation fingerprint given from the operation unit 10 via the frame memory 20-5 (step 403),
A reduction process similar to that performed in step 303 is performed on the captured image data of the verification fingerprint (step 40).
4). As a result, the image data of the verification fingerprint is 64 × 1
The image data is reduced to image data of 28 pixels and 256 gradations.

The control unit 20-1 sends the reduced image data of the collated fingerprint (see FIG. 1 (e)) to the Fourier transform unit 20-7, and adds the two-dimensional discrete Fourier transform to the image data of the collated fingerprint. Perform a transform (DFT) (Step 40)
5). Thus, the image data of the collation fingerprint shown in FIG. 1E becomes Fourier image data (collation Fourier image data) as shown in FIG.

Next, the control unit 20-1 executes step 405.
Fourier image data of the collation fingerprint obtained in
Is combined with the Fourier image data of the registered fingerprint read in step (step 406) to obtain combined Fourier image data.

Here, the synthesized Fourier image data is expressed as A · e ^j θ when the Fourier image data of the collation fingerprint is A · e ^j θ and A · B when the Fourier image data of the registered fingerprint is B · e ^j φ.
-It is expressed by e ^{j (} θ ^- φ ⁾ . However, A, B, θ, and φ are all functions of the frequency (Fourier) space (u, v).

[0021] ^{and, A · B · e j (} θ - φ) ^{is, A · B · e j (} θ - φ) = A · B · cos (θ-φ) + j · A · B · sin (θ- φ) (1) where A · e ^j θ = α ₁ + jβ ₁ , B · e ^j φ =
When _{α 2 + jβ 2, A =} (α 1 2 + β 1 2) 1/2, B =
_{^{(Α 2 2 + β 2 2}} ) 1/2, θ = tan -1 (β 1 / α 1), φ =
tan ^-1 (β ₂ / α ₂ ). By calculating this equation (1), synthetic Fourier image data is obtained.

A.B.e ^{j (} θ ^- φ ⁾ = A.B.e ^j
θ ・ e- ^j φ = A ・ e ^j θ ・ B ・ e- ^j φ = (α ₁ + j
β ₁ ) · (α ₂ −jβ ₂ ) = (α ₁ · α ₂ + β ₁ ·
The combined Fourier image data may be obtained as β ₂ ) + j (α ₂ β ₁ −α ₁ β ₂ ).

After obtaining the synthesized Fourier image data in this way, the control unit 20-1 performs an amplitude suppression process (step 407). In this embodiment, log processing is performed as amplitude suppression processing. That is, an arithmetic equation of synthesized Fourier image data described above ^{A · B · e j (θ} -
φ ⁾ is taken as log (A · B) · ej ⁽ θ ⁻ φ ⁾ , so that the amplitude A · B is log (A · B)
(A · B> log (A · B)).

FIG. 1D shows the synthesized Fourier image data after the amplitude suppression processing. In the synthesized Fourier image data subjected to the amplitude suppression processing, the influence of the illuminance difference between when the registered fingerprint is collected and when the verification fingerprint is collected is reduced. That is, by performing the amplitude suppression processing, the spectrum intensity of each pixel is suppressed, there is no outstanding value, and more information becomes effective. In addition, by performing amplitude suppression processing, feature points (end points, branch points) and ridge features (vortex, branch), which are personal information, are more emphasized in the fingerprint information, and ridges, which are general fingerprint information, are emphasized. The overall flow and direction can be suppressed.

In this embodiment, log processing is performed as amplitude suppression processing. However, Δ processing may be performed. In addition to log processing and √ processing,
Any processing may be used as long as the amplitude can be suppressed. If all the amplitudes are set to, for example, 1 in the amplitude suppression, that is, if only the phase is used, there is an advantage that the amount of calculation can be reduced and an amount of data can be reduced as compared with the log processing or the √ processing.

After performing the amplitude suppression processing in step 407, the control unit 20-1 sends the synthesized Fourier image data subjected to the amplitude suppression processing to the Fourier transformation unit 20-7.
A second two-dimensional discrete Fourier transform (DFT) is performed (step 408). Thus, the combined Fourier image data shown in FIG. 1D becomes the combined Fourier image data shown in FIG.

Then, the control unit 20-1 executes Step 40
8. The combined Fourier image data obtained in Step 8 is fetched, the intensity (amplitude) of the correlation component of each pixel in a predetermined correlation component area is scanned from the combined Fourier image data, and a histogram of the intensity of the correlation component of each pixel is obtained. From the histogram, the one having the largest correlation component intensity is extracted as a correlation peak (step 409). Then, a weighting process according to the distance from the correlation peak is performed on the correlation component of each pixel in the correlation component area (step 410).
From the correlation components, the upper n pixels (8 pixels in this embodiment) having a higher intensity are extracted, and the average of the intensity of the extracted correlation components of the n pixels is obtained as a correlation value (score) (step 411). .

Then, the control unit 20-1 executes step 41
The correlation value obtained in step 1 is compared with a predetermined threshold value (step 412).
It is determined that the registered fingerprint and the matching fingerprint match (step 4).
13), display to that effect and send out the output for the electric lock. If the correlation value is equal to or smaller than the threshold value, it is determined that the registered fingerprint and the collation fingerprint do not match (step 414), the fact is displayed, and the process returns to step 401.

Here, the correlation component area is shown in FIG.
With respect to the synthesized Fourier image data shown in (h), it is defined as a region S0 surrounded by a white dotted line. FIG. 6 shows a numerical example of the intensity of the correlation component of each pixel before the weighting process is performed on a part of the correlation component area S0. In this figure, the value “950” surrounded by a circle is the maximum value of the intensity of the correlation component. In this case, the maximum value "950" of the intensity of the correlation component is extracted as a correlation peak, and a weighting process according to the distance from the correlation peak is performed on the correlation component of each pixel in the correlation component area S0.

FIG. 7 shows an example of a weighting function used in the weighting process. In this weighting function,
The horizontal axis indicates the pixel position starting from the maximum value (correlation peak) of the intensity of the correlation component, and the vertical axis indicates the weighting coefficient by which the correlation component at that pixel position is multiplied. For example, if the weighting function shown in FIG. 7A is used, a correlation component of 16 pixels around the pixel position extracted from the pixel position extracted as the correlation peak is multiplied by a weighting coefficient falling at a predetermined slope, and the surroundings are multiplied. The intensity of the correlation component of each pixel in the correlation component area S0 exceeding 16 pixels is set to 0. By using such a weighting function, a correlation component in a portion centered on a pixel position extracted as a correlation peak is emphasized. If the weighting function is a function that becomes 0 below the threshold value, only the values above the threshold value need to be compared, so that time and memory can be saved.

Here, before the weighting process is performed, the data of the top n pixels having the high intensity of the correlation component is scattered in the correlation component area S0 in the combination of the principal and the other (see FIG. 8A). ), The combination of the person and the person is gathered at the same position to some extent (see FIG. 8B).
Therefore, when weighting processing is performed in step 410 according to the distance from the correlation peak, pixels having a high correlation component intensity remain in the correlation component area S0 in the combination of the principal and the principal (FIG. 8 ( d)), and only a small amount remains in the correlation component area S0 in the combination of the principal and another person (see FIG. 8C).

Therefore, in step 411, upper n pixels having a higher correlation component intensity are extracted from the correlation component area S0 after the weighting process, and the average of the extracted correlation component intensities of the n pixels is correlated. When calculated as a value, the correlation value increases in the combination of the principal and the principal, whereas the correlation value decreases significantly in the combination of the principal and the principal.
That is, in this embodiment, the correlation value in the combination of the subject and the other person is significantly reduced, the variation is small,
The mistake of erroneously determining the person and the other person as the same is greatly reduced, and the recognition rate is increased.

Here, the threshold value to be compared with the correlation value is:
A total of 100 fingers obtained by inputting fingerprints of index fingers of 10 men and women in their 20s and 50s 10 times each as a sample are used for registration and collation, and are collated 10,000 times, and are obtained from the collation results. . In this case, a correlation value at which the other person exclusion rate becomes 100% is used as the threshold value. Note that the other person exclusion rate need not be 100%, but may be set to an arbitrary rate according to the purpose.

It should be noted that in this embodiment, from each pixel in the correlation component area S0, the upper n
Although the pixels are extracted and the average is used as the correlation value, the sum of the intensities of the correlation components of the upper n pixels may be used as the correlation value. Further, the intensities of the correlation components of all the pixels exceeding the threshold value may be added, and the added value may be used as the correlation value, or the average of the added values may be used as the correlation value. If at least one of the intensities of the correlation components of each pixel is equal to or greater than the threshold value, it may be determined that “match”.
If the number is equal to or more than one, various judgment methods such as judging “match” can be considered.

In this embodiment, the two-dimensional discrete Fourier transform is performed in the Fourier transform unit 20-7, but may be performed in the CPU 20-1. Further, in this embodiment, the reduction processing is performed on the image data of the registered fingerprint in step 303.
The reduction process may be performed at a stage after reading the Fourier image data of the registered fingerprint (between steps 402 and 403). Further, it is not always necessary to perform the reduction processing on the image data of the registered fingerprint or the collated fingerprint, and the Fourier image data may be created using the input image data as it is. If the reduction processing is performed, the capacity of the image memory used for processing the input image data can be reduced accordingly.

Further, in this embodiment, the two-dimensional discrete Fourier transform is performed in step 408 shown in FIG. 4, but the two-dimensional discrete inverse Fourier transform is performed instead of the two-dimensional discrete Fourier transform. You may do so. That is, instead of performing two-dimensional discrete Fourier transform on the synthesized Fourier image data subjected to the amplitude suppression processing,
A dimensional discrete inverse Fourier transform may be performed. 2
The matching accuracy between the two-dimensional discrete Fourier transform and the two-dimensional discrete inverse Fourier transform does not change quantitatively. The two-dimensional discrete inverse Fourier transform is described in the aforementioned reference 1.

Further, in this embodiment, the amplitude suppression processing is performed in step 407 shown in FIG. 4, but the amplitude suppression processing may be omitted. By performing the amplitude suppression processing in step 407, the influence of the illuminance difference between the time when the registered fingerprint is collected and the time when the collation fingerprint is collected in the synthesized Fourier image data is reduced. , Bifurcation points) and features of ridges (vortices, bifurcations) are further emphasized, and the matching accuracy is significantly improved.

In this embodiment, the two-dimensional discrete Fourier transform is performed by performing amplitude suppression processing on the synthesized Fourier image data (step 40).
7, 408), the synthesis may be performed after the amplitude suppression processing is performed on the Fourier image data of the registered fingerprint and the collation fingerprint before synthesis. That is, as shown in FIG. 9A, a step 306 for performing amplitude suppression processing is provided between steps 304 and 305 in FIG. 3, and as shown in FIG. 9B, steps 406 and 407 in FIG. May be replaced.

In such a case, the Fourier image data (registered Fourier image data) of the registered fingerprint subjected to the amplitude suppression processing as shown in FIG. 406 and 407
As a result, Fourier image data (collation Fourier image data) of the collation fingerprint subjected to the amplitude suppression processing as shown in FIG. Then, the Fourier image data of the registered fingerprint and the verification fingerprint subjected to the amplitude suppression processing are combined, and combined Fourier image data as shown in FIG. 1D is obtained.

At this time, the rate of suppression of the amplitude of the combined Fourier image data is smaller than that in the case where the amplitude suppression processing is performed after forming the combined Fourier image data (FIG. 4). Therefore,
When the amplitude suppression processing is performed on the synthesized Fourier image data (FIG. 4), the matching accuracy is improved as compared with the method of performing amplitude suppression processing and then on the synthesized Fourier image data (FIG. 9). In the case where the combined Fourier image data is obtained after the amplitude suppression processing is performed (FIG. 9), the combined Fourier image data is not subjected to the two-dimensional discrete Fourier transform but to the two-dimensional discrete inverse Fourier transform. You may.

For reference, FIG. 10 shows each image of the fingerprint collation process when the collation fingerprint is another person, corresponding to FIG. FIG. 1 is an image of a fingerprint collation process in the case where the collation fingerprint is the principal. When the collation fingerprint is the principal, a portion where the intensity of the correlation component is high occurs in the correlation component area S0. Not in some cases.

In this embodiment, the case of performing fingerprint collation has been described as an example. However, the present invention can be similarly applied to the case of performing voiceprint collation, and can be treated as image data regardless of fingerprints and voiceprints. It can be used for collation of various two-dimensional patterns that can be performed. Further, the present invention is not limited to the two-dimensional pattern collation, and the same can be applied to the N-dimensional pattern collation such as a one-dimensional pattern or a three-dimensional pattern.

In this embodiment, the two-dimensional pattern is obtained as an image, but it is not always necessary to obtain the image as an image. For example, a vibration detector is two-dimensionally arranged at each location, and a two-dimensional pattern (seismic wave) obtained by the two-dimensionally arranged vibration detector is used as a collation pattern, which is collated with a pattern registered in advance. You may do so. In addition, a flow measuring device is two-dimensionally arranged at each part, and a two-dimensional pattern (flow distribution) obtained by the two-dimensionally arranged flow measuring device is set as a collation pattern, and collated with a pre-registered pattern. You may make it. Further, in this embodiment, an example of application to the prior application cited as a conventional example has been described, but the present invention can be similarly applied to the prior application.

[0044]

As is apparent from the above description, according to the present invention, out of the correlation components for each data constituting the N-dimensional pattern data of the correlation component area appearing in the synthesized Fourier N-dimensional pattern data, The one having the maximum intensity is obtained as a correlation peak, and a weighting process in accordance with the distance from the correlation peak is performed on the correlation components of each data constituting the N-dimensional pattern data of the correlation component area. The matching between the registered pattern and the matching pattern is performed based on the strength of the person, and it is possible to significantly reduce the correlation value of the combination of the person and the other person, and erroneously determine that the person and the other person are the same. Mistakes can be greatly reduced, and the recognition rate can be increased.

[Brief description of the drawings]

FIG. 1 is a diagram illustrating a fingerprint matching process in a fingerprint matching device according to the present invention.

FIG. 2 is a block diagram of the fingerprint matching device.

FIG. 3 is a flowchart for explaining a fingerprint registration operation in the fingerprint collation apparatus.

FIG. 4 is a flowchart for explaining a fingerprint matching operation in the fingerprint matching device.

FIG. 5 is a diagram illustrating a reduction process on image data.

FIG. 6 is a diagram illustrating a numerical example of the intensity of the correlation component of each pixel in a part of the correlation component area.

FIG. 7 is a diagram illustrating a weighting function used in the weighting process.

FIG. 8 is a diagram exemplifying the intensity of a correlation component in a correlation component area in a combination of a principal and another person and a combination of the principal before and after the weighting process is performed.

FIG. 9 is a flowchart for explaining another example of the fingerprint registration operation and the fingerprint collation operation.

10 is a diagram showing each image in a fingerprint matching process when the matching fingerprint is another person, corresponding to FIG.

[Explanation of symbols]

10 operation section, 20 control section, 10-1 numeric keypad, 10-2 display (LCD), 10-3 ...
Fingerprint sensor, 10-31: light source, 10-32, prism, 10-33: CCD camera, 20-1: control unit, 2
0-2 ROM, 20-3 RAM, 20-4 Hard disk (HD), 20-5 Frame memory (F
M), 20-6: external connection unit (I / F), 20-7: Fourier transform unit (FFT).

Claims (3)

[Claims] A registered Fourier pattern data generating means for performing N-dimensional discrete Fourier transform on N-dimensional pattern data of a registered pattern to generate registered Fourier N-dimensional pattern data; Pattern data creating means for creating a matching Fourier N-dimensional pattern data by performing a dynamic Fourier transform, and synthesizing the registered Fourier N-dimensional pattern data and the matching Fourier N-dimensional pattern data, thereby obtaining a combined Fourier N Pattern processing means for performing either one of N-dimensional discrete Fourier transform and N-dimensional discrete inverse Fourier transform on the two-dimensional pattern data, and appearing in the synthesized Fourier N-dimensional pattern data subjected to Fourier transform by the pattern processing means N-dimensional pattern of the correlated component area Among the correlation components of each data constituting the turn data, the one having the largest intensity is obtained as a correlation peak, and a weighting process according to the distance from the correlation peak constitutes the N-dimensional pattern data of the correlation component area. And a pattern matching unit that performs a comparison between the registered pattern and the matching pattern based on the intensity of the correlation component after applying the correlation component to each data. 2. A registered Fourier pattern data generating means for performing N-dimensional discrete Fourier transform on N-dimensional pattern data of a registered pattern to generate registered Fourier N-dimensional pattern data; Pattern data creating means for creating a matching Fourier N-dimensional pattern data by performing a dynamic Fourier transform, and synthesizing the registered Fourier N-dimensional pattern data and the matching Fourier N-dimensional pattern data, thereby obtaining a combined Fourier N Pattern processing means for performing one of N-dimensional discrete Fourier transform and N-dimensional discrete inverse Fourier transform after performing amplitude suppression processing on the dimensional pattern data; and synthesizing which is subjected to Fourier transform by the pattern processing means. Appears in Fourier N-dimensional pattern data Among the correlation components for each piece of data constituting the N-dimensional pattern data of the correlation component area, the one having the largest intensity is obtained as a correlation peak, and a weighting process according to the distance from the correlation peak is performed on the correlation component area. Pattern matching means for applying to the correlation components of each individual data constituting the N-dimensional pattern data, and matching the registered pattern with the matching pattern based on the intensity of these correlation components. Pattern matching device. 3. A registered Fourier pattern data creating means for creating registered Fourier N-dimensional pattern data by performing an N-dimensional discrete Fourier transform on the N-dimensional pattern data of the registered pattern and then performing an amplitude suppression process; Matching Fourier pattern data creating means for creating matching Fourier N-dimensional pattern data by performing N-dimensional discrete Fourier transform on the N-dimensional pattern data and then performing amplitude suppression processing; the registered Fourier N-dimensional pattern data and the matching Fourier Pattern processing means for synthesizing the N-dimensional pattern data and performing one of N-dimensional discrete Fourier transform and N-dimensional discrete inverse Fourier transform on the synthesized Fourier N-dimensional pattern data obtained therefrom; By means of the Fourier transform Among the correlation components for each data constituting the N-dimensional pattern data of the correlation component area appearing in the composed Fourier N-dimensional pattern data, the correlation component having the largest intensity is obtained as a correlation peak, and according to the distance from the correlation peak Pattern matching for applying the weighting process to the correlation components of each of the data constituting the N-dimensional pattern data of the correlation component area, and comparing the registered pattern with the matching pattern based on the intensity of these correlation components. And a pattern matching device.