JP5599605B2 - Noise removal apparatus, noise removal method, and noise removal program - Google Patents

Description

  The present invention relates to a noise removal device, a noise removal method, and a noise removal program, and more particularly, to a noise removal device, a noise removal method, and a noise removal program for removing noise from a fingerprint image.

  In a system that performs fingerprint authentication, either an area (surface) sensor or a line sensor is used to acquire a fingerprint image.

  The line sensor is a bar-shaped sensor, and acquires a fingerprint image by sliding a finger on the sensor. On the other hand, the area sensor obtains an image of a finger placed at a certain timing by placing the finger on a sensor portion on a surface larger than the finger. There are optical types and electric field types. The optical type irradiates light from the sensor portion of the surface, and obtains a fingerprint image using reflection of the irregularities of the fingerprint.

  Fingerprint authentication device that performs authentication by frequency analysis method, fingerprint image data acquired by imaging with an area sensor, for fingerprint image data, a rectangular area that is a unit of frequency conversion and a frequency data conversion target that is a conversion range A region is determined, frequency conversion is performed, and frequency data of each rectangular region is created. The frequency data is created for the number of rectangular areas in the frequency data conversion target area. In the authentication, the frequency data registered in the apparatus is compared with the frequency component data newly created from the inputted fingerprint image data, and it is determined whether the fingers are the same by the similarity.

Japanese Patent Application No. 2009-77149

  However, when an optical area sensor is used, as shown in FIG. 7, the area sensor captures a fingerprint image on a surface larger than the finger. Therefore, there is a problem that noise that is an image other than the fingerprint enters the acquired fingerprint image. FIG. 8 shows how noise is generated in the fingerprint image. Noise is a gradation part generated in a part where a finger is not placed. This noise gradually becomes white as it approaches the finger, and the change is characterized by being gentle compared to the fingerprint portion.

  This noise is generated regardless of the shape of the fingerprint because the finger is round when viewed from the front, and the reflection of light emitted from the sensor gradually increases as it approaches the ground plane.

  When this fingerprint image containing noise is used for fingerprint authentication processing by frequency analysis, not only the fingerprint but also noise gradation is converted into frequency data. Since noise is generated uniformly regardless of fingerprints, even if different fingers are used for authentication, it is determined that the same fingerprint is obtained by matching the frequency data of the noise part, and authentication succeeds by mistake. There was a problem. Further, as described in Patent Document 1, when noise is removed in units of pixels corresponding to detected noise, processing is performed for each pixel, which requires time. there were.

  In view of the above problems, an object of the present invention is to provide a noise removal device, a noise removal method, and a noise removal program that can efficiently remove noise from a fingerprint image.

In order to achieve the above object, the invention of claim 1 is characterized in that an outline of the fingerprint is obtained from an image pickup means for picking up a fingerprint and fingerprint image data representing a fingerprint image including the fingerprint imaged by the image pickup means by pixel values. Detecting means for detecting the position of each pixel indicating, a dividing means for dividing the fingerprint image into a plurality of areas each including a plurality of pixels, and a plurality of areas divided by the dividing means Dividing by the dividing means using deriving means for deriving frequency components for each region using pixel values of a plurality of pixels and the position of each pixel indicating the outline of the fingerprint detected by the detecting means among a plurality of areas which are, specifying means for specifying each area existing outside the contour of the fingerprint as a noise region, among the frequency components derived by the deriving means, the noise by the specifying means The frequency component corresponding to the regions identified as frequency, having, and disabling means for disabling all of said frequency components of said noise region by changing to a value indicating that the the noise area .
The invention according to claim 2 is the invention according to claim 1, wherein the specifying means obtains an average value of coordinates of the outline of the fingerprint for each of the divided areas, and the division outside the obtained average value. The identified area is specified as the noise area.

According to the first aspect of the present invention, the image of the pixel indicating the outline of the fingerprint is obtained from the fingerprint image data in which the fingerprint is captured by the imaging unit and the fingerprint image including the fingerprint imaged by the imaging unit is expressed by the pixel value. A position is detected, the fingerprint image is divided into a plurality of regions each including a plurality of pixels by a dividing unit, and a plurality of pixel values of pixels included in each of the plurality of regions divided by the dividing unit by a deriving unit Using the position of each pixel indicating the outline of the fingerprint detected by the detecting means by the specifying means , among the plurality of areas divided by the dividing means, identifies each region existing outside the contour of the fingerprint as a noise region, the invalidation means, among the frequency components derived by the deriving means, each of which is identified as a noise domain by specifying means The frequency component corresponding to the frequency, so invalidates all frequency components of the noise region by changing the value indicating a noise region, to efficiently remove noise in the image of the captured fingerprint Can do.
According to the invention of claim 2, the specifying means obtains an average value of the coordinates of the outline of the fingerprint for each divided area, and identifies the divided area outside the obtained average value as a noise area. Therefore, the noise region can be identified efficiently.

According to a third aspect of the present invention, in the first or second aspect of the present invention, the detection means may calculate a pixel value corresponding to a position of a pixel lined up on a straight line connecting one end portion to the other end portion of the fingerprint image. It is obtained from fingerprint image data, and the difference in pixel value between adjacent pixels is derived in order from the pixel value of the pixel corresponding to the one end, and whether or not the absolute value of the difference exceeds a predetermined value , The position of the pixel indicating the outline of the fingerprint is detected.

According to invention of Claim 3 , the outline of a fingerprint can be detected efficiently.

The invention according to claim 4, in the invention of any one of claims 1 to 3, wherein the deriving means further derives the directional component of the frequency for each respective region, are disabled by the disabling means It further has an authentication means for authenticating a fingerprint using the frequency component that has not been present and the direction component.

According to the fourth aspect of the present invention, since frequency component data derived from noise generated around the finger of the fingerprint image is not used for authentication, erroneous recognition that occurs when authentication is performed including a noise region is prevented. Can do.

According to a fifth aspect of the invention, in the invention according to any one of the first to fourth aspects of the invention, the specifying unit determines that the region including a pixel position indicating a contour detected by the detecting unit is a noise region. As further specified.

According to the invention of claim 5 , by specifying and invalidating the area including the contour as the noise area, it is possible to eliminate the possibility of validating the area including the noise located outside the contour. it can. Moreover, because it is not necessarily require all of the fingerprint data in the fingerprint authentication, and can be consequently reduce the amount of data according to claim 5.

To achieve the above object, the invention of claim 6, fingerprint from the fingerprint image data representing the fingerprint image including a fingerprint captured by the pixel value by the image pickup means for imaging a of each pixel indicating the contour of the fingerprint A detecting step for detecting a position, a dividing step for dividing the fingerprint image into a plurality of regions each including a plurality of pixels, and pixels of a plurality of pixels included in each of the plurality of regions divided by the dividing step A plurality of regions divided by the dividing step using a derivation step for deriving a frequency component for each region using a value and a position of each pixel indicating the outline of the fingerprint detected by the detection step of these, it identifies each region existing outside the outline of the fingerprint and the specific step of identifying a noise region, among the frequency components derived by said deriving step, as a noise region by the specifying stage The frequency component corresponding to each area, having a disabling step of disabling all of said frequency components of said noise region by changing to a value indicating that a said noise region.
The invention according to claim 7 is the invention according to claim 6, wherein the specifying step obtains an average value of coordinates of the outline of the fingerprint for each of the divided areas, and the division outside the obtained average value. The identified area is specified as the noise area.

Since the invention of claim 6 operates in the same manner as the invention of claim 1, the same effect as that of the invention of claim 1 can be obtained.
Further, since the invention of claim 7 operates in the same manner as the invention of claim 2, the same effect as that of the invention of claim 2 can be obtained.

According to an eighth aspect of the present invention, in the sixth or seventh aspect of the present invention, in the detection stage, the pixel value corresponding to the position of the pixel lined up on a straight line connecting from one end to the other end of the fingerprint image is set as the pixel value. It is obtained from fingerprint image data, and the difference in pixel value between adjacent pixels is derived in order from the pixel value of the pixel corresponding to the one end, and whether or not the absolute value of the difference exceeds a predetermined value , The position of the pixel indicating the outline of the fingerprint is detected.

The invention of claim 8, because they act in the same manner as the invention of claim 3, the same effects as the invention of claim 3 is obtained.

According to a ninth aspect of the present invention, in the invention according to any one of the sixth to eighth aspects, in the derivation step, a frequency direction component is further derived for each region, and is invalidated by the invalidation step. The method further includes an authentication step of performing fingerprint authentication using the missing frequency component and the direction component.

The invention of claim 9, because they act in the same manner as the invention of claim 4, the same effect as the invention of claim 4 is obtained.

According to a tenth aspect of the present invention, in the invention according to any one of the sixth to ninth aspects, the specifying unit determines that the region including a pixel position indicating a contour detected by the detecting unit is a noise region. As further specified.

The invention of claim 10, because they act in the same manner as the invention of claim 5, the same effects as the invention of claim 5 is obtained.

In order to achieve the above object, the invention of claim 11 shows the outline of the fingerprint from fingerprint image data in which a computer includes a fingerprint image including a fingerprint imaged by an imaging means for imaging a fingerprint. Detecting means for detecting the position of each pixel; dividing means for dividing the fingerprint image into a plurality of areas each including a plurality of pixels; and a plurality of areas included in each of the plurality of areas divided by the dividing means. Using the pixel value of the pixel, the dividing means uses the deriving means for deriving the frequency component for each region and the position of each pixel indicating the outline of the fingerprint detected by the detecting means . among the plurality of areas, specifying means for specifying each area existing outside the contour of the fingerprint as a noise region, among the frequency components derived by the deriving means by the specifying unit Noi The frequency component corresponding to each area specified as the region, and disabling means for disabling all of said frequency components of said noise region by changing to a value indicating that the a noise region, to It is a noise removal program for functioning.

Since the invention of claim 11 operates in the same manner as the invention of claim 1, the same effect as that of the invention of claim 1 can be obtained.

  According to the present invention, it is possible to provide a noise removal device, a noise removal method, and a noise removal program that can efficiently remove noise from a fingerprint image.

It is a figure which shows the structural example of the noise removal apparatus which concerns on embodiment. It is a schematic diagram which shows an example of the process outline | summary of a noise removal apparatus. It is a figure which shows an example of the characteristic of noise. It is a figure which shows the division area in a fingerprint image, the object area | region of frequency data conversion, and the pixel in a division area. It is a flowchart which shows the flow of an outline detection process. It is a flowchart which shows the flow of a noise removal process. It is a figure which shows the positional relationship of a finger | toe and an area sensor. It is a figure which shows an example of noise.

  The best mode for carrying out the present invention will be described below in detail with reference to the drawings.

  First, the configuration of the noise removal device according to the present embodiment will be described with reference to FIG. As shown in the figure, the noise removal apparatus 10 includes a CPU (Central Processing Unit) 12, a ROM (Read Only Memory) 14, a RAM (Random Access Memory) 16, an HDD (Hard Disk Drive) 18, an acquisition unit 20, And an area sensor 22. Among these, CPU12 controls the noise removal apparatus 10 whole, The flowchart mentioned later has shown the flow of the process by CPU12.

  The ROM 14 is a non-volatile storage device that stores a boot program or the like that is executed when the noise removal device 10 is started. The RAM 16 is a volatile storage device for temporarily storing information, for example, by developing various programs such as a noise removal program described later. The HDD 18 is a nonvolatile and rewritable storage device for storing a program for executing an operating system and contour detection processing and noise removal processing described later. The acquisition unit 20 acquires fingerprint image data in which a fingerprint image including a fingerprint imaged by the area sensor 22 is expressed by a pixel value. The area sensor 22 images a fingerprint, and is configured using a solid-state image sensor such as a CCD (Charge Coupled Device) image sensor.

  Next, an outline of processing of the noise removal apparatus 10 will be described with reference to FIG. First, when a fingerprint is captured by the above-described area sensor 22 and fingerprint image data representing a fingerprint image including the fingerprint captured by the acquisition unit 20 is acquired with pixel values, the fingerprint image data is temporarily stored in the RAM 16. Remembered.

  The fingerprint image indicated by the fingerprint image data is divided into a plurality of regions each including a plurality of pixels, and then discrete Fourier transform is performed using pixel values of the plurality of pixels included in each of the divided plurality of regions. Thus, frequency data deriving processing for deriving frequency data (frequency component, direction component) for each region is performed. In the following description, an area may be expressed as a divided area. (Frequency component, direction component) is derived for each divided region, but may be processed as a variable member by using, for example, a C language structure. In this case, for example, fre indicating the frequency and dir indicating the direction component are prepared as members of the two-dimensional array cell [] []. When actually referring to the frequency component, cell [] []. Fre is referred to, and when referring to the direction component, cell [] []. Dir is referred to.

  On the other hand, a fingerprint contour detection process for detecting the position of a pixel indicating the fingerprint contour from the fingerprint image data is performed.

  Then, using the pixel position detected by the fingerprint contour detection process, a noise area that is not included in the fingerprint is identified, and the frequency component derived by the frequency data deriving process is identified as the identified noise area. A noise removal process for invalidating the frequency component is performed by changing the corresponding frequency component to a value indicating the noise region.

  The frequency data from which noise has been removed in this way is recorded in the HDD 18. When the recorded frequency data is obtained from a plurality of fingerprint image data acquired by imaging the fingerprints of a plurality of people, there are a plurality of frequency data. The frequency data may be stored together with an ID for uniquely identifying the frequency data.

  The frequency data stored in this way is used as data for authentication by the frequency analysis method. Specifically, the authentication process acquires fingerprint image data for authentication, and includes the frequency data that has been subjected to the above-described fingerprint contour detection process, frequency data derivation process, and invalidation process, and the frequency data stored in the HDD 18. This is processing for determining that the fingerprints are the same when the similarity exceeds a predetermined threshold.

  Next, details of the fingerprint contour detection process will be described. In the fingerprint contour detection process, the coordinates (positions) of pixels indicating the contour are detected by using the noise feature and the fingerprint contour feature. The characteristics of noise will be described with reference to FIG. FIG. 3 is a diagram illustrating an example of pixel values (0 to 15) of pixels in a horizontal row of a fingerprint image for illustrating an example of noise characteristics. Since the noise gradually becomes white as it approaches the fingerprint, when the acquired fingerprint image is viewed with pixels in one horizontal row, adjacent pixel values change gradually as shown in FIG.

  On the other hand, the boundary between the noise and the fingerprint has a white background near the fingerprint, but the outline of the fingerprint is black, so that adjacent pixel values change rapidly. Therefore, it is determined that the part where the adjacent pixel value changes rapidly is the outline of the fingerprint, and the coordinates are used as the coordinates indicating the outline.

  In this embodiment, the fingerprint image is divided into vertical and horizontal 10 × 10 divided areas as shown in FIG. 4A, and the divided areas are further divided into vertical and horizontal 10 × 10 pixels as shown in FIG. 4B. Shall be composed. Further, in the fingerprint image, as shown in FIG. 4A, the middle 4 × 10 divided areas are set as frequency data conversion target areas. Therefore, since the frequency data structure cell [] [] described above has a 4 × 10 two-dimensional array, a total of 40 frequency data can be obtained from one fingerprint image data.

  Based on this, the outline detection process flow will be described with reference to the flowchart of FIG. In this flowchart, as shown in FIG. 4, the number of pixels in the horizontal direction of the fingerprint image is generalized as xsize, and the number of pixels in the vertical direction is generalized as ysize. Further, the variable i shown in the flowchart represents the vertical coordinate, the variable j represents the horizontal coordinate, and the coordinate of the upper left pixel of the frequency data conversion target region is (i, j) = (0, 0) ( (See FIG. 4). Furthermore, the pixel value of the pixel located at the coordinates (i, j) is d (i, j). In addition, a pixel located at coordinates (i, j) may be expressed as a target pixel.

  First, in step 101, the variable i is initialized with zero. In the next step 102, the pixel value of the pixel located in the i-th row is read for one row. Also, edge_l (i) is initialized with xsize / 2-1, and edge_r (i) is initialized with xsize / 2. In the next step 103, the variable j is initialized with zero.

  In step 104, the absolute value of the difference d (i, j) -d (i, j + 1) between adjacent pixels is substituted into the variable sb. In step 105, it is determined whether or not the variable sb exceeds a predetermined value TH. The value TH is an experimentally set value, for example, 3 or the like.

  If a negative determination is made in step 105, the amount of change is small, so the target pixel is determined to be noise, and in step 107, it is determined whether j> xsize / 2. This determination is a determination as to whether or not the target pixel has reached the center in the horizontal direction (xsize / 2 = 50 in the case of FIG. 4 and thus the 50th pixel counted in the horizontal direction from the left end). If an affirmative determination is made in this step 107, no contour exists from the 0th to j−1th (0th to 49th in the case of FIG. 4) of this row, so the process proceeds to step 109.

  On the other hand, if a negative determination is made in step 107, j is increased by 1 to return the pixel located at the adjacent coordinates (i, j + 1) to the target pixel in step 108, and the process returns to step 104.

  If the determination in step 105 is affirmative, it is determined that the pixel of interest is the outline of a fingerprint, and in step 106, j is substituted for variable edge_l (i) indicating the outline from 0 to j−1. Proceed to step 109.

  The above processing from Step 103 to Step 106 is processing for detecting an outline in which one end is the left end of the fingerprint image and the other end is the center in the horizontal direction of the fingerprint image. The next processing from Step 109 to Step 112 is processing for detecting the contour in one end portion as the right end of the fingerprint image and the other end portion as the horizontal center of the fingerprint image.

  Therefore, first, xsize is substituted for j in step 109. In the next step 110, the absolute value of the difference d (i, j) -d (i, j-1) between adjacent pixels is substituted into the variable sb. In step 111, it is determined whether or not the variable sb exceeds the above TH.

  If a negative determination is made in step 111, the amount of change is small, so that the target pixel is determined to be noise, and in step 113, it is determined whether j ≦ xsize / 2. This determination is a determination as to whether or not the target pixel has reached the center in the horizontal direction (xsize / 2 = 50 in the case of FIG. 4, so that it is the 51st pixel counted in the horizontal direction from the right end). . If an affirmative determination is made in step 113, there is no contour up to jth to xsizeth (50th to 100th in the case of FIG. 4) of this row, so the routine proceeds to step 115.

  On the other hand, if a negative determination is made in step 113, j is decremented by 1 to return the pixel located at the adjacent coordinates (i, j-1) to the target pixel in step 114, and the processing returns to step 110. .

  If the determination in step 111 is affirmative, it is determined that the pixel of interest is the outline of a fingerprint, and in step 112, j is substituted into variable edge_r (i) indicating the contours from j to xsize, and step 115 is performed. Proceed to

  In the next step 115, it is determined whether i = ysize. This determination is a determination as to whether or not the currently processed line is the lower end of the fingerprint image. If a negative determination is made in step 115, that is, if it is not the lower end, in step 116 the variable i is incremented by 1 in order to perform processing on the next lower row, and the processing returns to step 102. On the other hand, if the determination in step 115 is affirmative, that is, if the currently processed line is the lower end, the process ends.

  As described above, in the contour detection process, the pixel value corresponding to the position of the pixel arranged on the straight line connecting from one end to the other end of the fingerprint image is obtained from the fingerprint image data, and the pixel value of the pixel corresponding to the one end is obtained. In order to detect the position of the pixel indicating the outline of the fingerprint by deriving a difference in pixel value between adjacent pixels in order and determining whether or not the absolute value of the difference exceeds a predetermined value It has become.

  Next, the flow of noise removal processing will be described using the flowchart of FIG. In the flowchart shown in FIG. 6, the number in the vertical direction of the divided areas in the frequency data conversion target area shown in FIG. 4 is YSIZE (4 in FIG. 4). Further, the coordinate of the upper left pixel of the frequency data conversion target region is set to (0, 0) as in FIG. Furthermore, the variable y counts the divided areas in the vertical direction, and a variable line indicating the vertical coordinates of the upper left pixel of the divided area is obtained using this y.

  First, in step 201, 0 is substituted for y, and in step 202, 10y is substituted for variable line. This is due to the fact that the number of vertical pixels included in one divided area is 10, and by multiplying y by 10, the vertical coordinates of the upper left pixel of the divided area can be obtained.

  In the next step 203, the sum S of edge_l obtained in the flowchart of FIG. 5 is obtained. Σ shown in step 203 represents the total sum from k = 0 to 9. In step 204, the sum S is divided by 10 to obtain the average value of edge_l, that is, the average value of the horizontal coordinate of the contour, and substitute this into A. Since A is an integer variable, the value assigned to A is a value obtained by truncating the average value after the decimal point.

  In the next step 205, 0 is assigned to cell [x] [y] .fre in the range of 0 ≦ x ≦ A. Therefore, a noise area that is an area not including a fingerprint is identified in step 204, and a frequency component corresponding to the identified noise area is determined in step 205 as a value indicating a noise area (in this embodiment, 0). The frequency component is invalidated by changing to ().

  Steps 202 to 205 are processes related to edge_l, and steps 206 to 208 described below are processes related to edge_r.

  In step 206, the sum S of edge_r obtained in the flowchart of FIG. 5 is obtained. Σ shown in step 206 indicates the total sum from k = 0 to 9. In step 207, the sum S is divided by 10 to obtain the average value of edge_r, that is, the average value of the horizontal coordinate of the contour, and substitute this into A.

  In the next step 208, 0 is assigned to cell [x] [y] .fre in the range of A ≦ x ≦ 9. Therefore, a noise region that does not include a fingerprint is specified in step 207, and in step 208, the frequency component corresponding to the specified noise region is a value indicating a noise region (in this embodiment, 0). The frequency component is invalidated by changing to ().

  In the next step 209, it is determined whether y = YSIZE. This determination is to determine whether the currently processed divided region is at the lower end. If a negative determination is made in step 209, that is, if it is not the lower end, y is incremented by 1 in step 210 in order to perform processing on the next lower divided region, and the processing returns to step 202. On the other hand, if an affirmative determination is made in step 209, that is, if the currently processed divided region is the lower end, the processing ends.

  In the flowchart described above, the value indicating the noise region is set to 0, but it is not limited to this.

  By using the frequency data from which noise has been removed in this way, the authentication process determines that the data is invalid when the frequency component of the frequency data is zero. Invalid data is deleted from the comparison target population, and only valid frequency component data is compared.

  That is, since frequency component data derived from noise generated around the finger of the fingerprint image obtained by the area sensor 22 is not used for authentication, erroneous recognition that occurs when authentication is performed including a noise region is prevented. The effect of being able to be obtained.

  Furthermore, since processing can be performed at a higher speed than when noise is removed in pixel units, processing time can be shortened and power consumption can be reduced.

  Further, in addition to the processing of the flowchart shown in FIG. 6 described above, a region including the position of the pixel indicating the detected contour may be further specified as a noise region. Thus, by specifying and invalidating the area including the contour as the noise area, the possibility of validating the area including even the noise located outside the contour can be eliminated. In addition, since fingerprint authentication does not necessarily require all of fingerprint data, the amount of data can be reduced as a result.

  The processing flow of the flowcharts described in FIGS. 5 and 6 in the embodiment described above is an example, and the processing order is changed within the range not departing from the gist of the present invention, a new step is added, or unnecessary. It goes without saying that it is possible to delete various steps.

  Furthermore, although the area sensor is used in this embodiment, the present invention can also be applied when fingerprint image data is acquired by a line sensor. Further, according to the above-described embodiment, noise can be removed if the contour of the fingerprint can be detected. Therefore, the contour detection process is not limited to the process of the flowchart shown in FIG.

10 Noise removal device 12 CPU
14 ROM
16 RAM
18 HDD
20 Acquisition unit 22 Area sensor

Claims (11)

An imaging means for imaging a fingerprint;
Detecting means for detecting a position of each pixel indicating an outline of the fingerprint from fingerprint image data representing a fingerprint image including a fingerprint imaged by the imaging means by a pixel value;
Dividing means for dividing the fingerprint image into a plurality of regions each including a plurality of pixels;
Derivation means for deriving a frequency component for each area using pixel values of a plurality of pixels included in each of the plurality of areas divided by the dividing means;
Using the position of each pixel indicating the outline of the fingerprint detected by the detection means, each area existing outside the outline of the fingerprint is identified as a noise area among the plurality of areas divided by the division means. Specific means to
Of the frequency components derived by the deriving unit, the frequency components corresponding to the respective regions identified as noise regions by the identifying unit are changed to values indicating the noise region, thereby changing the frequency components within the noise region. Invalidating means for invalidating all the frequency components of
A noise removing apparatus having The said specific | specification part calculates | requires the average value of the coordinate of the outline of the fingerprint for every said divided | segmented area | region, and specifies the said divided | segmented area | region outside the calculated | required average value as said noise area | region. Noise removal device. The detection means acquires a pixel value corresponding to a position of a pixel arranged on a straight line connecting from one end to the other end of the fingerprint image from the fingerprint image data, and from the pixel value of the pixel corresponding to the one end The position of the pixel indicating the outline of the fingerprint is detected by deriving a difference in pixel value between adjacent pixels in order and determining whether or not the absolute value of the difference exceeds a predetermined value. The noise removal apparatus according to claim 1 or 2 . The deriving means further derives a frequency direction component for each region,
It said frequency component has not been disabled by the disabling means, and using the direction components, noise removal device according to any one of claims 1 to 3, further comprising an authentication means for performing authentication of a fingerprint. The specifying means, a noise removal apparatus according to any one of claims 1 to 4, further specifying the region including the position of the pixel indicating the detected contour by the detecting means as a noise region. A detection step of detecting a position of each pixel indicating the outline of the fingerprint from fingerprint image data representing a fingerprint image including a fingerprint imaged by an imaging means for imaging a fingerprint by a pixel value;
A division step of dividing the fingerprint image into a plurality of regions each including a plurality of pixels;
A derivation step of deriving a frequency component for each region using pixel values of a plurality of pixels included in each of the plurality of regions divided by the division step;
Using the position of each pixel indicating the outline of the fingerprint detected in the detection stage , among the plurality of areas divided by the division stage, each area existing outside the outline of the fingerprint is identified as a noise area. Specific stages to do,
Of the frequency components derived in the deriving step, the frequency component corresponding to each region identified as the noise region in the identifying step is changed to a value indicating the noise region, thereby changing the frequency component in the noise region. An invalidation step of invalidating all the frequency components of
A noise removing method. The said specific step calculates | requires the average value of the coordinate of the outline of the fingerprint for every said divided | segmented area | region, and specifies the said divided | segmented area | region outside the calculated | required average value as said noise area | region. Noise removal method. In the detection step, a pixel value corresponding to a position of a pixel arranged on a straight line connecting from one end to the other end of the fingerprint image is obtained from the fingerprint image data, and from the pixel value of the pixel corresponding to the one end The position of the pixel indicating the outline of the fingerprint is detected by deriving a difference in pixel value between adjacent pixels in order and determining whether or not the absolute value of the difference exceeds a predetermined value. The noise removal method according to claim 6 or 7 . In the derivation step, a frequency direction component is further derived for each region,
The noise removal method according to any one of claims 6 to 8, further comprising an authentication step of performing fingerprint authentication using the frequency component that has not been invalidated by the invalidation step and the direction component. The noise removal method according to any one of claims 6 to 9 , wherein in the specifying step, the region including the position of a pixel indicating the contour detected in the detection step is further specified as a noise region. Computer
Detection means for detecting the position of each pixel indicating the outline of the fingerprint from fingerprint image data representing a fingerprint image including a fingerprint imaged by an imaging means for imaging a fingerprint by pixel values;
Dividing means for dividing the fingerprint image into a plurality of regions each including a plurality of pixels;
Derivation means for deriving a frequency component for each area using pixel values of a plurality of pixels included in each of the plurality of areas divided by the dividing means;
Using the position of each pixel indicating the outline of the fingerprint detected by the detection means, each area existing outside the outline of the fingerprint is identified as a noise area among the plurality of areas divided by the division means. Specific means to
Of the frequency components derived by the deriving unit, the frequency components corresponding to the respective regions identified as noise regions by the identifying unit are changed to values indicating the noise region, thereby changing the frequency components within the noise region. Invalidating means for invalidating all the frequency components of
Noise removal program to make it function.

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