JP2007206873A - Fingerprint image generation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a fingerprint image generation device for suppressing an error taking a sweat gland hole area for a valley line area in generating a binary fingerprint image where the image is binarized into a ridge line pixel value showing a ridge line area and a valley line pixel value showing a valley line area. <P>SOLUTION: A fingerprint image generation device 1 comprises a local area setting means 14 setting a local area at a part of a multivalued fingerprint image in which unevenness of the fingerprint is indicated by multiple values; a ridge line candidate area determining means 15 determining whether or not the local area is a ridge candidate area on the basis of a comparison result between a local representative value representing a value of pixels included in the local area and a prescribed reference value; a threshold setting means 16 setting a first binarizing threshold for the local area determined as the ridge line candidate area to a value easy to determine as the ridge line area in comparison to a second binarizing threshold for the local area not determined as the ridge candidate area; and a binarizing means 17 binarizing the pixels included in the local area by means of the first or the second binarizing threshold. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a fingerprint image generation apparatus, and more particularly to a fingerprint image generation apparatus that generates a binary fingerprint image that is binarized into a ridge area and a valley line area from a multi-value fingerprint image.

  In recent years, there has been a growing interest in security in entry / exit management, access to information equipment, withdrawal of deposits and savings, and the like. Under such circumstances, biometrics authentication (biometric authentication) has attracted particular attention as a personal authentication method that can provide higher security with more certainty than conventional key and password authentication.

  In fingerprint authentication, which is one of such biometrics authentication, feature information is extracted from a fingerprint image representing the uneven pattern of a fingerprint obtained from a fingerprint sensor in shades, and the feature information and pre-registered feature information A method is known in which the degree of coincidence is obtained by comparing the above and personal authentication is performed based on the degree of coincidence.

  As one method for extracting feature information from a fingerprint image, the multivalued image obtained from the fingerprint sensor is subjected to processing such as noise removal and unevenness correction to correct the image quality, and then the fingerprint image is extracted from the binarized image. There is a method for obtaining branch points and end points of lines as feature points.

  As an example, when a fingerprint image is acquired by an optical fingerprint sensor with a finger internal property detection type, a ridge portion that is a convex portion has a relatively low pixel value, and a valley line portion that is a concave portion has a relatively high pixel value. A multi-value fingerprint image is obtained. Therefore, in the above binarization process, the ridge portion is binarized as a low pixel value and the valley portion as a high pixel value. Here, when extracting the feature information, it is desirable that the ridge portion and the valley portion can be clearly distinguished. However, there are sweat pores that are concave portions on the ridges of the fingerprint. The sweat pore has a relatively high pixel value on the multi-value fingerprint image, like the valley line portion. Therefore, in the binarization process, for example, when the average pixel value of the entire multi-value fingerprint image is used as a threshold value, the sweat pore region also has a high pixel value like the valley region and cannot be distinguished from the valley region. As a result, pseudo feature points are extracted in the feature information extraction, which is a factor of reducing the accuracy of fingerprint authentication.

  This will be described with reference to FIG. FIG. 1A schematically shows a part of a binary fingerprint image obtained by binarizing the fingerprint image, and FIG. 1B shows a thinning process for the binary fingerprint image shown in FIG. 1 schematically shows a thinned binary fingerprint image that has been subjected to. In FIG. 1A, a linear region 100 represented by black is a ridge region, and a linear region 110 represented by white is a valley region. Also, the white region 120 that exists so as to overlap the linear region 100 is a sweat pore region that is erroneously set to a high pixel value in the same manner as the valley region. As shown in FIG. 1A, when the sweat pore region 120 exists on the ridge region 100, when this binary fingerprint image is thinned, as shown in FIG. Instead of a single line, a portion corresponding to the sweat pore region 120 remains as a loop 150. The branch point generated by the loop 150 is extracted as a pseudo feature point, thereby reducing the accuracy of fingerprint authentication.

  For such problems, fingerprints that detect sweat glands one by one by performing a judgment process based on the knowledge that “a sweat gland hole is small and surrounded by a ridge region” Authentication devices have been developed. For example, the personal verification device described in Patent Document 1 performs the above determination processing to determine whether or not a branch point extracted from a binarized and thinned fingerprint image is caused by the presence of sweat pores. By using and discriminating and removing branch points caused by sweat gland holes, erroneous extraction of feature points caused by sweat gland holes is suppressed. Moreover, in the sweat gland hole part discrimination device described in Patent Document 2, a region indicating a sweat gland hole is detected based on a region surrounded by a ridge region in a binarized fingerprint image, and the detected region is detected as a ridge. The fingerprint image with the sweat glands removed is generated by replacing it with the same value.

  On the other hand, when the finger press at the time of reading a fingerprint by the fingerprint sensor is high, a fingerprint image is generated in which a ridge is crushed and widened. As described above, since the sweat gland hole exists in the ridge area, when the ridge is crushed and thickened, the sweat gland hole area also expands, and the sweat gland hole area is compared with the case where the fingerprint is normally read. Becomes a fingerprint image that is enlarged and appears distorted. When such a fingerprint image is binarized, the outline of the ridge may be scraped off by the sweat gland hole (see 130 in FIG. 1A). In such a case, the stripped portion appears as a ridge branch 160 in the fingerprint image after thinning, and a branch point and an end point that should not originally exist are generated.

  However, since the fingerprint authentication device as described above is based on the premise that the sweat glands are surrounded by the ridges, it is possible to identify whether or not the region where the outline of the ridges has been removed is caused by the sweat glands. Can not. Therefore, a fingerprint image representing an accurate ridge pattern cannot be obtained, and fingerprint matching / mismatching is erroneously determined, and authentication accuracy may be reduced.

JP-A-2-17579 JP 2001-243467 A

  In view of the above problems, an object of the present invention is to provide a binary fingerprint image generating apparatus capable of reducing errors in which a sweat pore region is a valley region.

  In view of the above problems, the unevenness of the fingerprint according to the present invention is represented by a binary value of a ridge pixel value representing a ridge region and a valley pixel value representing a valley line region from a multi-value fingerprint image in which the multi-value fingerprint image is represented. A fingerprint image generation device that generates a binary fingerprint image is a local region setting unit that sets a local region within the entire region of a multi-value fingerprint image, and a local representative value that represents a value of a pixel included in the local region. A ridge candidate area determining unit that determines whether or not a local area is a ridge candidate area based on a comparison result with a predetermined reference value, and a first second for a local area determined as a ridge candidate area Threshold setting means for setting the threshold value to a value that is easier to determine as a ridge region than the second binarization threshold value for the local region that is determined not to be a ridge candidate region, and the pixels included in the local region are Binarizing means for binarizing at the first or second binarization threshold value. To.

  According to the above configuration, in a multi-value fingerprint image, a pixel included in an area that is highly likely to be a ridge area is easier to determine as a ridge area than other areas. Therefore, it is possible to create a binary fingerprint image in which the sweat pore region on the ridge region is the ridge region without erroneously setting the valley region as the ridge region. In the above, the multi-value fingerprint image means an image that expresses the density of the fingerprint with three or more levels of gradation. Typically, the gradation of the fingerprint is expressed with 64 levels, 256 levels, or 1024 levels. It is an image to express.

  In the fingerprint image generating apparatus according to the present invention, it is preferable that the local region setting unit sets a predetermined range including the target pixel of the multi-value fingerprint image as a local region for the target pixel. Since an optimal local region can be set for each pixel of the multi-value fingerprint image, binarization can be more accurately performed into a ridge region or a valley region.

  In the fingerprint image generating apparatus according to the present invention, the local representative value is preferably an average pixel value or a median value calculated based on values of some or all pixels included in the local region. By using the average pixel value or the median value, it is possible to accurately know whether the pixel value of the entire local region is high or low, and thus the ridge candidate region can be set accurately.

  In the fingerprint image generating apparatus according to the present invention, the reference value is preferably an average pixel value of the entire area of the multi-value fingerprint image. By setting the reference value to the average pixel value of the entire fingerprint area, it is possible to make it less susceptible to fluctuations in conditions during fingerprint image acquisition.

  In the fingerprint image generating apparatus according to the present invention, it is preferable that the threshold setting unit sets the first binarization threshold to a value that is easier to determine as a ridge region than the reference value. With such a configuration, it is easy to determine only a pixel included in a region that is highly likely to be a ridge region as a ridge region, so that a pixel in the valley region can be prevented from being erroneously set as a ridge pixel value.

  In the fingerprint image generating apparatus according to the present invention, the multi-value fingerprint image represents a concave portion of the fingerprint with a high pixel value and a convex portion with a low pixel value, and the threshold setting means has a first binarization threshold value. Is preferably set to a pixel value higher than the reference value.

  Furthermore, in the above case, it is preferable that the threshold setting means sets the first binarization threshold to a higher pixel value as the local representative value of the local area determined as the ridge candidate area is lower than the reference value. Alternatively, the threshold setting means preferably obtains a difference value between the reference value and the local representative value, and sets a value obtained by adding the difference value and the reference value as the first binarization threshold. With such a configuration, an area that is more likely to be a ridge area is easier to be determined as a ridge area, so that an appropriate binarization threshold can be set, and thus binarization can be performed accurately.

  ADVANTAGE OF THE INVENTION According to this invention, it became possible to provide the production | generation apparatus of the binary fingerprint image which can reduce the error which makes a sweat gland hole area | region a valley area.

Hereinafter, a fingerprint image generation apparatus according to a first embodiment to which the present invention is applied will be described with reference to the drawings.
FIG. 2 is a functional block diagram of the fingerprint image generating apparatus 1 according to the first embodiment to which the present invention is applied. As shown in FIG. 2, the fingerprint image generation apparatus 1 includes an image input / output unit 11 and an image generation unit 12. The fingerprint image generating apparatus 1 receives a multi-value fingerprint image from the image input / output unit 11. The received fingerprint image is sent to the image generation unit 12. The image generation unit 12 generates a binary fingerprint image based on the received multi-value fingerprint image. The generated binary fingerprint image is output to an external device or a recording medium such as a magnetic disk through the image input / output unit 11.

First, an outline of the binarization process in the fingerprint image generation apparatus 1 will be described.
In the present embodiment, as an example, the multi-value fingerprint image has a size of horizontal 200 pixels × vertical 240 pixels. Each pixel of the multi-value fingerprint image is expressed by 256 gradations of 0-255, the fingerprint ridge region has a low pixel value (or black), and the fingerprint valley region has a high pixel value (or white). Shall be. The pixel value here corresponds to a luminance value in each pixel on the image, and a pixel with a low pixel value is expressed with low luminance and a pixel with a high pixel value is expressed with high luminance.

FIG. 3A shows an outline of a partial region of the multi-value fingerprint image, and FIG. 3B shows pixel values in the direction crossing the ridges and valleys in AA ′ of FIG. An outline of the profile is shown. In FIG. 3B, the horizontal axis represents the position on the fingerprint image, and the vertical axis represents the pixel value (luminance value).
As shown in FIG. 3B, the valley region 300 has a relatively high pixel value. On the other hand, the ridge region 310 has a relatively low pixel value. The sweat pore region 320 is included in the ridge region 310 and has a higher pixel value than the other portions of the ridge region 310. Therefore, for example, if the binarization threshold is the average pixel value of the entire fingerprint image, the sweat pore region 320 may have a pixel value higher than the binarization threshold. Therefore, if binarization is performed on the valley region and the ridge region using this binarization threshold, the sweat pore region 320 may be erroneously set as the valley region. On the other hand, if the binarization threshold value is carelessly set to a high pixel value, there arises a problem that a part of the valley region 300 becomes a ridge region this time.

  Therefore, the fingerprint image generating apparatus 1 first obtains a reference value for discriminating a low pixel value portion corresponding to the ridge region 310 from the entire fingerprint region of the multi-value fingerprint image. Next, a local region is set at an arbitrary position of the multi-value fingerprint image. Then, the average pixel value of the local area is obtained. If the average pixel value is relatively low, it is considered that there is a high possibility that the local region represents a ridge region. Therefore, the fingerprint image generating apparatus 1 sets the local region as a ridge candidate region. The fingerprint image generation apparatus 1 sets a binarization threshold value with a pixel value higher than the reference value for the ridge candidate region. The fingerprint image generating apparatus 1 performs binarization processing using the binarization threshold. That is, with respect to the pixel of interest in the ridge candidate region, the pixel value is compared with the binarization threshold value, and if the pixel value is smaller than the binarization threshold value, it is determined as the ridge region. In the ridge candidate area, since the binarization threshold is set to a higher pixel value, the sweat pore area having a higher pixel value than the other parts of the ridge area is also classified into the ridge area. It can be binarized as follows.

Hereinafter, each part of the fingerprint image generating apparatus 1 will be described in detail.
The image input / output unit 11 is an interface for connecting the fingerprint image generation apparatus 1 and an external device, and includes a communication port, an electronic circuit, and driver software compliant with standards such as Ethernet (registered trademark), USB, SCSI, and RS-232C. Etc. The image input / output unit 11 acquires the multi-value fingerprint image M sent from the external device as digital data and passes it to the image generation unit 12. Further, the binary fingerprint image B generated by the image generation unit 12 is output to an external device or a magnetic recording medium.

The image generation unit 12 generates a binary fingerprint image B based on the acquired multi-value fingerprint image M, and includes a central processing unit (CPU), a numerical operation processor, a semiconductor memory such as a ROM or a RAM, and the like. Is done. The image generation unit 12 performs a predetermined operation according to a program read from a semiconductor memory included in the image generation unit 12 or a magnetic recording medium (not shown). The image generation unit 12 includes a reference value setting unit 13, a local region setting unit 14, a ridge candidate region determination unit 15, a threshold setting unit 16, and a binarization unit 17.
Each means will be described below.

Reference value setting means 13 receives the multivalued fingerprint image M, and calculates the average pixel value I _A of the whole fingerprint region as a reference value. In some cases, the multi-value fingerprint image M includes a portion other than the fingerprint (for example, when the fingerprint image is read by the fingerprint sensor and the reading area of the fingerprint sensor is larger than the finger). In such a case, an area corresponding to a portion other than the fingerprint is not necessary in the following processing. Therefore, the following processing is performed using the remaining region from which such region is removed as the entire fingerprint region. Since the area corresponding to the part other than the fingerprint is almost the maximum pixel value and is higher than the pixel value of the valley line area, the removal of such an area uses a threshold T1 slightly lower than the maximum pixel value, This is performed by removing pixels having a pixel value higher than the threshold value T1.

The local region setting unit 14 sets an arbitrary pixel in the multi-value fingerprint image M as the target pixel C. The local region setting unit 14 sets the local region L _A around the target pixel C. FIG. 4A shows this state. Incidentally, the local region L _A is either substantially equal to the width of one ridge is preferably set slightly widens such width than that. As described later, the local area from the pixels included in L _A, to determine whether the local region L _A corresponds to one ridge region 310 and the valley line area 300, the pixel values representing the local region L _A This is because information (local representative value) is acquired. Too expand the local region L _A, also how to set the local region L _A, the local region L _A ratio of ridge regions and valley regions contained in is almost equal, the local region L _A is Takashi It is unclear whether it corresponds to the line region 310 or the valley region 300. If set too narrow local regions L _A Conversely preferable because if the pixels included in the local region L _A is almost equivalent to the sweat gland pore region occurs, it is impossible to accurately obtain the pixel value information of ridge region Absent. In the present embodiment, for a multi-value fingerprint image M in which the width of a ridge area in a standard fingerprint is about 3 to 5 pixels and the width of a ridge area in a fingerprint with a thick ridge is 6 to 8 pixels, the region L _a was 7 × 7 pixel region.

Ridge line candidate area determination unit 15, based on a result of comparison between the local representative value calculated based on the pixel values of the multi-level fingerprint image M with a predetermined reference value contained in the local region L _A, the local region L _A It is determined whether or not is a ridge candidate region that has a high possibility of being a ridge region.
With reference to FIGS. 4 and 5, the local region L _A to explain the principles of the determination of whether or not to correspond to the ridge line candidate area. 4 (a) shows a state of setting local regions L _A pixel sweat gland pore region of the multi-level fingerprint image M as the target pixel C. The Figure 4 (b) shows a schematic of a pixel value profile of the local region _{L A} in B-B 'in FIG. 4 (a). The horizontal axis of the graph in FIG. 4B represents position coordinates, and the vertical axis represents pixel values. Similarly, FIG. 5 (a) shows a state of setting local regions L _A where the pixels of the valley area of the multi-level fingerprint image M was focused pixel C. The Figure 5 (b) shows a schematic of a pixel value profile of the local region _{L A} in C-C 'of FIG. 5 (a). The horizontal axis of the graph in FIG. 5B represents position coordinates, and the vertical axis represents pixel values.

First, ridge candidate area determination unit 15 calculates the average pixel value I _L in the local region L _A as a representative value of the local region L _A (local typical). The ridge line candidate area determination unit 15, the difference value _I D of the average pixel value _{I A} of the entire region and the average pixel value _{I L} of the local region _{L A} of the fingerprint image is a reference value ₍₌ I A _-I L) Is calculated. In the case where the difference value I _D is positive, i.e. towards the average pixel value I _L in the local region L _A is lower than the average pixel value I _A of the entire fingerprint area, as shown in FIG. 4 (a), local region L _a is likely to be set in the ridge region 310. Therefore, ridge line candidate area determination unit 15 determines that the ridge line candidate region local regions L _A.
On the other hand, when the difference value I _D is negative, i.e. towards the average pixel value I _L in the local region L _A is higher than the average pixel value I _A of the entire fingerprint area, as shown in FIG. 5 (b), local region L _a is considered to be likely to be set in the valley region 300. Therefore, ridge line candidate area determination means 15 determines that not the ridge line candidate region local regions L _A (valley candidate region).

Threshold value setting means 16, a binarization threshold Thd _BIN for pixels of the multi-level fingerprint image M contained in the local region L _A where it is determined that ridge line candidate region, the local region is determined not to be a ridge line candidate region L _A Is set to a value that is easier to determine as a ridge region than the binarization threshold value for the pixels of the multi-value fingerprint image M included in. Here, the value that can be easily determined as the ridge region means a value that separates the pixel into the ridge region even if the value of the pixel to be binarized is a pixel value from the valley region.

Specifically, the binarization threshold Thd _BIN is set as follows.
First, if the ridge line candidate area determination unit 15 determines that the local region L _A is a ridge line candidate region, the threshold setting unit 16 sets a binarization threshold value Thd _BIN as follows.
_{Thd BIN = I A + I D} (1)
(1) from the equation, if the difference value I _D is positive, i.e., if the direction of the average pixel value I _L of the local region L _A is lower than the average pixel value I _A fingerprint whole region, and FIG. 4 (b) As shown in FIG. 5, the binarization threshold Thd _BIN is set higher than the average pixel value I _{A of the} entire fingerprint area so as to be higher than the pixel value of the pixels included in the sweat pore area 320. In particular, the more _{I D} is large, that is, the lower the average pixel value _{I L} of the local region _{L A,} since the local region _{L A} is likely to correspond to the ridge region 310, the binarization threshold value _{Thd BIN} nearest Set to a high value.

On the other hand, if the ridge line candidate area determination means 15 local region L _A is determined not to be the ridge line candidate region, the threshold setting means 16, so that the value lower than the pixel values of pixels included in the valley area setting a binarization threshold _Thd BIN = _{I a.} Thus, local regions L _A is by setting a binarization threshold Thd _BIN between otherwise as in ridge line candidate region to another value, divides correctly sweat gland pore region 320 in the ridge region 310 It is possible to prevent the valley region 300 from being mistakenly divided into the ridge region 310.
Further, the threshold value setting means 16 (1) even when the difference value _ID is negative, that is, when the average pixel value I _L of the local area L _A is higher than the average pixel value I _A of the entire fingerprint area. ) The binarization threshold Thd _BIN may be set according to the equation. In this case because it is _I D <0, the binarization threshold value _{Thd BIN} is set lower than the average pixel value _{I A.} Therefore, even if the value of the pixel of interest C has a lower pixel value than the ideal value due to noise or the like, the valley region 300 can be correctly classified.

Binarizing means 17 compares the binarized threshold _{Thd BIN,} a pixel value _{I C} of the target pixel C. The binarizing means 17 calculates as follows the pixel value B _C of the pixel of the binary fingerprint image B corresponding to the target pixel C of the multi-level fingerprint image M.
If I _C ≧ Thd _BIN , B _C = 255
If I _C <Thd _BIN , B _C = 0
Here, in the binary fingerprint image B, the pixel value “255” is a valley pixel value, and “0” is a ridge pixel value. That is, a pixel value _BC of “255” indicates that the pixel is a valley pixel. On the other hand, a pixel value _BC of “0” indicates that the pixel is a ridge pixel.
The pixel value B _C is' 0 'and is not limited to the combination of' 255 ', any two different values that can be handled in a data format that represents a binary fingerprint image B, for example,' 0 'and' 1 'can be used.

  The image generation unit 12 performs the processes of the local region setting unit 14, the ridge candidate region determination unit 15, the threshold setting unit 16 and the binarization unit 17 on all the pixels included in the multi-value fingerprint image M in order. As a result, the binary fingerprint image B is generated.

In the above description, it is preferable that the fingerprint image generation apparatus 1 generates the binary fingerprint image B based on the multi-value fingerprint image M that has been subjected to unevenness correction in advance.
Further, ridge candidate area determination unit 15, the procedure determines the local region L _A is whether ridge line candidate region is not limited to the above. For example, the ridge candidate area determination unit 15 uses the ratio I _R (= I _A / I _L ) of the average pixel value I _A of the entire area of the fingerprint image and the average pixel value I _L of the local area L _A as a determination criterion. May be. In this case, ridge line candidate area determination unit 15 determines the local region L _A and ridge line candidate region if I _R> 1. On the other hand, if I _R ≦ 1, ridge line candidate area determination means 15 determines that not the ridge line candidate region local regions L _A.
Further, the binarization threshold Thd _BIN is not limited to the above. For example, the following formula can be used instead of the formula (1).
Thd _BIN = I _A + I _D · C _r (2)
Here _{C r} is a constant, it is possible to use the value, eg 0.8 to 1.5. Thus, by multiplying the difference value _ID by a certain constant, the risk of erroneously setting the pixels included in the valley region 300 as the ridge region 310 is reduced (in the case of C _r <1), or vice versa. In addition, it is possible to reduce the risk of erroneously setting the sweat pore region as the valley region 300 (when C _r > 1). The value of C _r may be empirically varied by the relative position in the fingerprint image area. Furthermore, as for the binarization threshold Thd _BIN , when _ID is positive, a predetermined value C _A (where C _A > 0) may be added to I _A.

Further, in the above, the two values of the threshold Thd _BIN as a criterion for determining a local region L was used the average pixel value I _L of _A as a local representative value, the binarization threshold value Thd using other criteria _BIN may be determined. For example, the median I _M of the pixel values in the local region L _A, can be used as a local representative value instead of the average pixel value I _L. Alternatively, it is possible to use a weighted average pixel value I _W to the pixel value is calculated by weighted average of the local region L _A instead of the average pixel value I _L. Such weighting can be determined empirically, but for example, a Gaussian filter type weighting can be used. Furthermore, in order to reduce the adverse effect of noise may be calculated an average pixel value I _L using the remaining pixel values excluding the maximum pixel value and minimum pixel value in the local region L _A.

Similarly, the fingerprint image generating apparatus 1, instead of using the average pixel value I _A of the entire fingerprint image as a reference value, also possible to use an average value I _{A 'of} the remaining region excluding the peripheral portion of the fingerprint image it can. Further, a predetermined pixel value can be used as a reference value. However, when a predetermined pixel value is used as a reference value, the multi-value fingerprint image M may be normalized so as to have a pixel average value and variance within a certain allowable range. preferable.
In the above, sets the local region L _A for each pixel of interest C, and was calculated binarization threshold Thd _BIN on the determination whether ridge line candidate area, once set the local region L _A , the two calculating the value of the threshold _{Thd BIN,} may be used the binarization threshold _{Thd BIN} to a plurality of pixels in a local region _{L a.} By doing so, in order to reduce the number of calculations of the average pixel value I _L, it is possible to shorten the time required to generate the binarized fingerprint image B.

Next, the operation of the fingerprint image generating apparatus 1 according to the first embodiment to which the present invention is applied will be described with reference to FIGS.
6 and 7 are operation flowcharts for the generation of the binary fingerprint image B of the fingerprint image generating apparatus 1. The fingerprint image generating apparatus 1 executes the operation described below when the CPU in the image generating unit 12 is controlled according to the program.

As shown in FIG. 6, the image generation unit 12 of the fingerprint image generation apparatus 1 first acquires a multi-value fingerprint image M from the image input / output unit 11 (step S101). Then, the acquired multi-value fingerprint image M is temporarily stored in a RAM that is a component of the image generation unit 12. Further, a memory area corresponding to the size of the generated binary fingerprint image B is secured and initialized. Then, the reference value setting means 13 of the image generation unit 12 calculates the average pixel value I _A of the entire fingerprint area of the multi-level fingerprint image M, and temporarily stored in the RAM of the image generating section 12 (step S102) . Next, the local region setting unit 14 of the image generation unit 12 sets the pixel of interest C from the multi-value fingerprint image M (step S103). And it sets the local region _{L A} about its target pixel C (step S104). Thereafter, ridge candidate area determination unit 15 of the image generation unit 12, out of the multi-level fingerprint image M stored in the RAM, in the local region L _A with reference to the value of the pixel corresponding to the local region L _A An average pixel value _IL is calculated (step S105). Then, the ridge candidate area determination unit 15 refers to the average pixel value I _A of the entire fingerprint image stored in the RAM of the image generation unit 12, and the difference value I from the average pixel value I _L in the local area L _{A. D (=} I a -I _L) is calculated (step S106).

Next, as shown in FIG. 7, a ridge line candidate area determination unit 15, whether the local region L _A corresponds to the main ridge area, in order to determine whether the corresponding to the valley line area, the difference value I _It is checked whether _D is positive (step S107). The ridge line candidate area determination unit 15 determines that the case where the difference value I _D is positive, the local region L _A because of the high possibility of being set on the ridge region, a ridge line candidate area. The threshold value setting means 16 of the image generation unit 12 according to the above (1) to determine a high value become so binarization threshold _{Thd BIN (= I A + I} D) than the average pixel value _{I A} (Step S108). On the other hand, in step S107, ridge line candidate area determination unit 15, when the difference value I _D is less than or equal to zero, the local region L _A is not the very ridge candidate region. Then, the threshold setting unit 16 of the image generation unit 12 determines the average pixel value I _A as the binarization threshold Thd _BIN (= I _A ) (Step S109). As described above, the binarization threshold Thd _BIN may be determined so as to be a value lower than the average pixel value I _A according to the above equation (1) as in the ridge candidate region.

When the binarization threshold Thd _BIN is determined, the binarization unit 17 of the image generation unit 12 compares the pixel value IC of the pixel of interest _C with the binarization threshold Thd _BIN (step S110). Then, if the pixel value I _C is less than the binarization threshold value Thd _BIN of the target pixel C, for setting the binary fingerprint image B, and the value B _C of a pixel corresponding to the target pixel C in ridge pixel value, The value “0” is written in the corresponding memory address of the memory area of the binary fingerprint image B secured in the RAM of the image generation unit 12 (step S111). On the other hand, in step S110, if the pixel value _{I C} of the target pixel C is binarized threshold _{Thd BIN} above, in the binary fingerprint image B, and the value _{B C} of a pixel corresponding to the target pixel C in the valley pixel value In order to set, the value “255” is written to the corresponding memory address of the RAM (step S112).

  Thereafter, the image generation unit 12 determines whether or not the processing in steps S103 to S112 has been completed for all the pixels in the multi-value fingerprint image M (step S113). If the process has not been completed for any of the pixels, the control is returned to step S103. Then, any pixel that has not yet been processed is newly set as the target pixel C.

  On the other hand, if all the pixels have been processed in step S113, the image generation unit 12 ends the generation of the binary fingerprint image B. Then, the binary fingerprint image B is output through the image input / output unit 11 (step S114). Then, the fingerprint image generating apparatus 1 ends the process.

As described above, the fingerprint image generating apparatus 1 according to the first embodiment to which the present invention is applied includes the reference value obtained from the pixel values of the entire fingerprint area of the multi-value fingerprint image M, the ridge area, or the valley. based on the comparison results of the local representative value obtained from the local region L _a containing a portion of the line region, the local region L _a determines whether there is a high possibility that corresponding to ridge area. When the local region L _A is likely to correspond to the ridge region (a ridge line candidate area) sets the binarization threshold Thd _BIN to a value higher than the reference value, whereas local area L _{When A} is not considered to correspond to the ridge region, the binarization threshold Thd _BIN is set to the reference value (or the reference value or less), so that the sweat pore region is correctly divided into the ridge region, and the valley A binary fingerprint image that does not mistakenly divide a line area into a ridge area can be generated.

Next, a fingerprint authentication device according to a second embodiment to which the present invention is applied will be described with reference to the drawings.
FIG. 8 is a functional block diagram of the fingerprint authentication device 2 according to the second embodiment to which the present invention is applied. As illustrated in FIG. 8, the fingerprint authentication device 2 includes an operation / display unit 21, a fingerprint input unit 22, a storage unit 23, a processing unit 24, and an output unit 32. The fingerprint authentication apparatus 2 displays operation guidance and information for an operator who performs fingerprint authentication in the operation / display unit 21, and reads an input fingerprint from a finger placed on the fingerprint input unit 22 according to the guidance. A value fingerprint image M is generated. The generated multi-value fingerprint image M is sent to the processing unit 24. The processing unit 24 performs unevenness correction on the received multilevel fingerprint image M, generates a binary fingerprint image based on the unevenness corrected multilevel fingerprint image M, and further thins the binary fingerprint image. Do. In addition, about the production | generation of a binary fingerprint image, the process similar to the image generation part 12 of said fingerprint image generation apparatus 1 is performed.

Thereafter, processing unit 24, thinned feature point list by performing feature extraction on the basis of the binary fingerprint image (hereinafter, input referred feature point list) to create a F _i. Further, the processing unit 24 uses the input feature point list F _i created from the fingerprint image as a feature point list (hereinafter referred to as a registered feature point list) associated with the operator identification number IN registered in the storage unit 23 in advance. And match with F _r . The verification result is output via the output unit 32. For example, an electric lock locking / unlocking control device is connected to the output unit 32. Then, if the collation result received from the output unit 32 is a signal indicating that the authentication is successful, the electric lock locking / unlocking control device unlocks the electric lock and conversely indicates a signal indicating that the authentication has failed. If so, keep the electric lock locked.

Fingerprint authentication device 2 is a second embodiment according to the present invention, a binarization threshold Thd _BIN based on the difference between the average pixel value I _L in the average pixel value I _A and the local region L _A of the entire fingerprint image By adjusting, it is possible to generate a binary fingerprint image that is correctly classified into the ridge region without erroneously distinguishing the sweat pore region from the valley region. Therefore, the fingerprint authentication device 2 can reduce the risk of extracting pseudo feature points. As a result, the fingerprint authentication device 2 can achieve high authentication accuracy.

Hereinafter, each part of the fingerprint authentication device 2 will be described in detail.
The operation / display unit 21 is used by an operator of the fingerprint authentication device 2, that is, a person to be verified, to input an identification number IN and select an operation to be performed (for example, entering a specific room). . The operation / display unit 21 displays operation guidance or provides voice instructions, and includes a touch panel display and a speaker. Further, instead of the touch panel, an input device such as a keyboard or a mouse and a simple display device such as a liquid crystal display may be used. Note that the speaker may be omitted when the guidance voice instruction is not performed.
Data such as the identification number IN input by the operation / display unit 21 is used for specifying registered fingerprint data to be called by the processing unit 24.

  The fingerprint input unit 22 generates a fingerprint image to be used for collation processing. The fingerprint sensor is a module of a CCD camera that images a placed finger and converts it into a digital signal. A placement sensor for detecting the placement; an LED for illuminating the finger during imaging; and a finger guide member for causing the operator to correctly recognize the placement position of the finger. In the present embodiment, as an example, an optical sensor with a finger internal property detection type is used as a fingerprint sensor. However, the fingerprint sensor that can be used in the present invention is not limited to this. For example, as a fingerprint sensor, an optical sensor such as a total reflection optical type, an optical path separation optical type, a surface projection irregular reflection type, a non-optical sensor such as a capacitance type, an electric field type, a pressure sensitive type, an ultrasonic type, etc. May be used.

  In the fingerprint input unit 22, when the placement sensor detects that a finger is placed on the fingerprint sensor, the fingerprint sensor images the finger and generates a multi-value fingerprint image M of the input fingerprint. In this embodiment, as in the case of the first embodiment, each pixel of the fingerprint image is represented by 256 gradations from 0 to 255, and the ridge region of the fingerprint has a low pixel value (or black), and the fingerprint The valley line region is expressed with a high pixel value (or white).

The storage unit 23 stores registered fingerprint data in which an operator identification number IN registered in advance and a registered feature point list _Fr are associated with each other. The storage unit 23 is a nonvolatile memory such as a flash memory, a magnetic recording medium such as a hard disk, An optical recording medium such as a CD-ROM or DVD-R / W is included.

The registered feature point list F _r is a feature point list created from a fingerprint image of a user registered in advance, and is a list of the positions of feature points such as ridge branch points and end points. The registered feature point list F _r is created by the same method as the creation of the input feature point list F _i by the processing unit 24 described later. Details of the feature point list and the creation method will be described later.

  The storage unit 23 also stores programs used by the processing unit 24, various setting files, parameters, and the like. These are read by the processing unit 24 as necessary at a predetermined timing, such as when the fingerprint authentication device 2 is activated.

  The processing unit 24 performs fingerprint collation based on the fingerprint image acquired from the fingerprint input unit 22 and the registered feature point list acquired from the storage unit 23, and includes a central processing unit (CPU), a numerical operation processor, a ROM, A semiconductor memory such as a RAM is used. Further, the processing unit 24 executes a predetermined operation according to the program read from the storage unit 23. Further, the processing unit 24 is connected to the operation / display unit 21, the fingerprint input unit 22, the storage unit 23, and the output unit 32, and performs control by outputting predetermined control signals to these units. The processing unit 24 includes a control unit 25, an image correction unit 26, a feature extraction unit 30, and a collation unit 31 in order to perform fingerprint collation and control of each unit.

When the operator identification number IN is input from the operation / display unit 21, the processing unit 24 searches whether or not the identification number IN (hereinafter referred to as an input identification number) is registered in the storage unit 23. Then, the processing unit 24, if a matching identification number IN is registered, reads the identification number IN with the registered feature point list F _r associated from the storage unit 23. Further, the image correction unit 26 of the processing unit 24 performs unevenness correction on the multi-value fingerprint image M acquired from the fingerprint input unit 22, and then, based on the unevenness-corrected multi-value fingerprint image M, the binary fingerprint image. B is generated. Further, the image correcting unit 26 performs thinning against binary fingerprint image B, and generates a thinned binary fingerprint image B _T. Then, in the feature extraction unit 30, the processing unit 24 creates an input feature point list F _i of the input fingerprint from the thinned binary fingerprint image B _T. When the input feature point list F _i is created, the matching unit 31 of the processing unit 24 performs matching based on the input feature point list F _i and the registered feature point list F _r . Finally, the processing unit 24 causes the operation / display unit 21 to display a collation result regarding whether or not the authentication is successful, and outputs a signal indicating the collation result to an external device such as an electric lock control device via the output unit 32. To do.

Hereinafter, each part of the processing unit 24 will be described in detail.
The control unit 25 controls each part of the operation / display unit 21, the fingerprint input unit 22, the storage unit 23, and the output unit 32 according to the program read from the storage unit 23 and the input signal from the operation / display unit 21, and the fingerprint. Deliver various data such as images. In addition, the control unit 25 controls processing by the image correction unit 26, the feature extraction unit 30, and the collation unit 31 in the processing unit 24.

The image correction unit 26 performs preprocessing on the multi-value fingerprint image M in order to create an input feature point list F _i used for collation from the fingerprint image of the operator. For this purpose, the image correction unit 26 is a non-uniformity correction unit 27 for correcting density unevenness of the multi-value fingerprint image M, and a binary fingerprint image B that is binarized into a ridge area and a valley line area. having a thinning unit 29 for generating a thinned binary fingerprint image B _T the ridge area of the binarization unit 28 and the binary fingerprint image B by thinning. The fingerprint binarization unit 28 has the same configuration as the image generation unit 12 of the fingerprint image generation apparatus 1 described above, and generates a binary fingerprint image B by performing the same processing.

The unevenness correction unit 27 corrects density unevenness of the multi-value fingerprint image M caused by the difference between the pressing force at the center of the finger and the pressing force at the periphery when the finger is placed on the fingerprint input unit 22, and the fingerprint Make the density of the entire image uniform.
For example, the unevenness correction unit 27 divides the multi-value fingerprint image M into partial areas of about 32 × 32 pixels, and calculates the average pixel value and the variance of the pixel values for each partial area. Then, the unevenness correction unit 27 converts the pixel values so that the average pixel value and the variance of the pixel values are substantially equal to the average pixel value and the variance of the pixel values of the entire multi-value fingerprint image M (image analysis handbook p .478, Mikio Takagi, University of Tokyo Press).

The fingerprint binarization unit 28 generates a binary fingerprint image B binarized into a ridge region and a valley region based on the multi-value fingerprint image M on which the unevenness correction unit 27 has performed unevenness correction. As described above, the fingerprint binarization unit 28 has the same configuration as the image generation unit 12 of the fingerprint image generation apparatus 1 and performs the same processing to generate the binary fingerprint image B. The details will be omitted.
In the following, for convenience of explanation, represented by the value of the ridge pixels in the binary fingerprint image B and the thinned binary fingerprint image B _T '0', the value of the valley pixel '255'.

The thinning unit 29 performs a thinning process on the binary fingerprint image B generated by the fingerprint binarizing unit 28 so that the ridge region is thinned. The ridge region to generate a thinned binary fingerprint image B _T to be a continuous line with a width of 1 pixel. For example, the thinning unit 29 prepares a plurality of predetermined mask patterns in which a plurality of adjacent pixels have a value of “0”, sequentially applies the mask patterns, and applies to any one of the mask patterns. A method of converting the pixel value of “0” to “255” can be used. However, the thinning unit 29 is not limited to the above thinning method, and various known methods can be used.

The feature extraction unit 30 extracts feature points based on the thinned binary fingerprint image B _T generated by the image correction unit 26 and creates an input feature point list F _i . Here, the feature points are end points, branch points, and the like of the ridge region.

FIG. 9 shows an outline of feature points in a thinned fingerprint image.
In FIG. 9, the portion shown in black represents a thinned ridge region. Further, in FIG. 9, portions 900 and 910 that are the end of the ridge region are end points of the ridge, and a portion 920 where the ridge is branched from one to two is a branch point. Since the position and number of these feature points are different for each individual, it is very useful information for collating fingerprints.

The information representing the feature point includes each element of the type of feature point (end point or branch point), position, direction, and reliability. Here endpoints P _e, for example, among the ridge pixel (i.e., pixel value '0'), and among the eight neighboring pixels adjacent, can only 1 pixel is a pixel which is ridge pixel. The branch point P _b are among the ridge pixels, of the adjacent eight neighboring pixels, 3 pixels is ridge pixel, and the ridge pixels to their 8 in neighboring pixels to the pixel not adjacent be able to. The position of the feature point is represented by coordinate values (X _t , Y _t ) on the thinned binary fingerprint image B _T of the end point _Pe or the branch point P _b . However X _t is the horizontal coordinate values on thinning the binary fingerprint image B _T, Y _t is the vertical coordinate value of the thinned binary fingerprint image B _T.

The direction θ _t of the feature point represents the direction of the ridge at the feature point. The direction theta _t of feature points, as shown in FIG. 9, represented by the angle formed, for example, in the horizontal axis and the clockwise direction of thinning the binary fingerprint image B _T. If the feature point is the end point P _e, the direction theta _t of feature points, a straight line connecting the ridge pixels adjacent to its end point P _e, as the angle between the horizontal axis of the thinning binary fingerprint image B _T Can be sought. In the case the feature point is a branch point P _b, and the branch point P _b, the line segment determined (such segment with the ridge pixels at a distance of two pixels from the branch point P _b should there three ). Next, the angle formed by each line segment is obtained. Then, a bisector of two line segments having the narrowest angle is obtained. The angle between the horizontal axis of the bisector and thinning the binary fingerprint image B _T can be a direction theta _t of the feature point. The method of obtaining the feature point direction θ _t is not limited to the above, and other known methods may be used.

The feature point reliability T is a value indicating the reliability of the existence, position, and direction of the feature point. For example, in a fingerprint image, a plurality of ridges are arranged substantially in parallel when viewed locally. Therefore, when considering a local region including a plurality of feature points, the direction θ _t of each feature point is substantially the same in that region. Therefore, the reliability T can be set as follows. First, a local region (for example, about 32 × 32 pixels) around the feature point of interest is set. Next, an average value θ _av of the direction θ _t of each feature point included in the local region is calculated. Then, the absolute value of the difference between the average value θ _av and the direction of the feature point of interest θ _t is defined as the reliability.
Note that the feature quantity to be extracted is not limited to the above. For example, the positional relationship between each feature point, the number of ridges or valleys existing between adjacent feature points, and the like may be extracted as the feature amount.
Feature extractor 30 for each of the feature points extracted from one thinned binary fingerprint image B _T as described above, to create a list in accordance with a predetermined format information of the feature point.

The collation unit 31 collates the input feature point list F _i extracted by the feature extraction unit 30 from the fingerprint image of the operator and the registered feature point list F _r read from the storage unit 23. As a specific method of collation, a known method for calculating the similarity between feature quantities, for example, a minutia matching method can be used. Specifically, first, for some feature points included in the input feature point list F _i , the average position difference with any one of the feature points included in the registered feature point list F _r is the smallest. Thus, the coordinate value parallel movement amount Δd and rotation correction angle Δθ are obtained. Then, for each feature point included in the input feature point list F _i, by adding the translation amount Δd and the rotational correction angle [Delta] [theta], to align the input feature point list F _i and the registered feature point list F _r .

Next, for each feature point included in the input feature point list F _i , among the feature points included in the registered feature point list F _r , the distance and direction from the feature point of the same type and the closest position The score is calculated based on the absolute value and the reliability of the difference. This score is set to be higher as the distance is shorter. Similarly, the score is set higher as the absolute value of the direction difference is smaller and the reliability is higher.

When the score for each feature point is calculated, the matching unit 31 calculates the total of the scores as the similarity S _i . Then, the collation unit 31 determines that the authentication is successful if the calculated similarity S _i exceeds a predetermined collation threshold Thd _V (that is, the operator's fingerprint (input fingerprint) is registered). On the contrary, if the calculated similarity S _i is equal to or smaller than a predetermined collation threshold Thd _V , it is determined that the authentication has failed (that is, the operator's fingerprint is registered). Does not match the fingerprint).

The output unit 32 is an interface for connecting the fingerprint authentication device 2 to an external device (for example, an electric lock locking / unlocking control device) and inputting / outputting signals, and includes Ethernet (registered trademark), USB, SCSI, RS- It is composed of communication ports, electronic circuits, driver software, and the like that comply with standards such as 232C.
The output unit 32 outputs a signal indicating the verification result of the processing unit 24 to an external device.

Next, the operation of the fingerprint authentication device 2 according to the second embodiment to which the present invention is applied will be described.
FIG. 10 is a flowchart showing the fingerprint authentication operation in the fingerprint authentication apparatus 2. The operation of the fingerprint authentication device 2 described below is executed by the control unit 25 of the processing unit 24 in accordance with a program read into the processing unit 24.

  First, when the fingerprint authentication apparatus 2 detects the approach of the operator by using, for example, a near infrared sensor (not shown), a predetermined guidance is displayed on the operation / display unit 21, and the fingerprint authentication work is performed on the operator according to the guidance. At the same time, the fingerprint authentication operation is started.

  First, the fingerprint authentication device 2 acquires the operator's input identification number IN from the operation / display unit 21 (step S201). If the placement sensor of the fingerprint input unit 22 detects a finger before the operator inputs the identification number IN, the fingerprint authentication device 2 prompts the operator to input the identification number through the operation / display unit 21. Is displayed. Next, the fingerprint authentication device 2 searches whether or not the acquired input identification number IN is registered in the storage unit 23 (step S202). When the input identification number IN is registered in the storage unit 23, the fingerprint authentication apparatus 2 operates the operation / display unit 21 so that the finger is placed at a predetermined position of the fingerprint input unit 22 in order to obtain a fingerprint image of the operator. Guidance to the operator is performed through (step S203). On the other hand, if the acquired input identification number IN is not registered in step S202, the fingerprint authentication device 2 displays an error message in which the identification number is not registered through the operation / display unit 21 (step S204). Then, control is returned to the beginning.

After step S203, when the placement sensor of the fingerprint input unit 22 detects a finger, the fingerprint input unit 22 acquires a multi-value fingerprint image M of the operator (step S205). When the multi-value fingerprint image M is acquired, the image correction unit 26 of the processing unit 24 performs unevenness correction on the multi-value fingerprint image M (step S206). Thereafter, the image correction unit 26 of the processing unit 24 generates a binary fingerprint image B (step S207). The procedure for generating the binary fingerprint image B is the same as the operation flow of the fingerprint image generating apparatus 1 shown in FIGS. At that time, as described above, the fingerprint binarization unit 28 included in the image correction unit 26 binarizes so as to distinguish the ridge region and the valley region in the multi-value fingerprint image M. It binarizes so that it may become a ridge area | region. When the binary fingerprint image B is generated, thinning unit 29 of the image correcting unit 26, the ridge region to generate thinned thinned binary fingerprint image B _T (step S208).

In step S208, the thinning the binary fingerprint image B _T is generated, the feature extraction unit 30 of the processing unit 24, the end point P _e of ridge region from the thinning binary fingerprint image B _T, the branch point P _b Are extracted to create an input feature point list F _i (step S209). When the input feature point list F _i is created, the processing unit 24 reads the registered feature point list F _r associated with the input identification number IN from the storage unit 23, and the matching unit 31 inputs the input feature point list F _i. And the similarity S _i of the registered feature point list F _r is calculated (step S210).

Then, the collation unit 31 compares the similarity S _i calculated in step S210 with a predetermined collation threshold Thd _V (step S211). In step S211, when the similarity S _i is equal to or smaller than the matching threshold Thd _V , the processing unit 24 determines that the authentication has failed. Then, the fingerprint authentication device 2 displays a message indicating that the authentication has failed on the operation / display unit 21 (step S212). Then, in order to acquire the operator's fingerprint image again, the control is returned to before step S203. Thereafter, if the placement of the finger cannot be detected after a while, the process is terminated and the process returns to the standby state.

On the other hand, when the similarity S _i is greater than the verification threshold Thd _V , the verification unit 31 of the processing unit 24 determines that the authentication is successful. Then, the fingerprint authentication device 2 displays a message indicating that the authentication has been successful on the operation / display unit 21 and transmits a signal indicating the verification result to the external device through the output unit 32 as necessary (step S213). Then, the fingerprint authentication device 2 ends the operation.

  If the authentication fails continuously for a predetermined number of times, the fingerprint authentication device 2 may display a warning message to the operator through the operation / display unit 21. Further, in such a case, the fingerprint authentication device 2 may notify the monitoring center (not shown) or the like of the identification information and warning information of the fingerprint authentication device 2 itself through the output unit 32.

As described above, the fingerprint authentication device 2 according to the second embodiment to which the present invention is applied is binarized so that the sweat pore region existing on the ridge region is accurately included in the ridge region. generate values fingerprint image B, since the extraction of feature points based on the binary fingerprint image B in thinning the thinned binary fingerprint image B _T, reducing the number of pseudo feature points are erroneously extracted Can do. In addition, since the number of pseudo feature points is small, high authentication accuracy can be achieved.

  In the above-described embodiment, the ridge region of the fingerprint has a low pixel value (or black) and the valley region of the fingerprint has a high (or white) pixel value. The present invention is applied even when the fingerprint ridge region has a high pixel value (or white) and the fingerprint valley region has a low pixel value (or black), as in the case of using the optical method. be able to. In this case, in the above description, the pixel values can be handled in the same manner by reversing the magnitude of the pixel values (for example, the expression “˜” is replaced with “˜”).

Further, in the above embodiment, the fingerprint collation is determined based on the feature point-based similarity, but the fingerprint image generation apparatus of the present invention can also be applied when performing fingerprint collation by another method. it can. For example, the fingerprint image generation apparatus of the present invention can be applied even when fingerprint matching is performed based on pattern matching between fingerprint images. In this case, by applying the fingerprint image generation device of the present invention from the input fingerprint image, a binary image separated into ridges and valleys is generated, and the generated binary image is used as a binary fingerprint image of a person registered in advance. If the degree of coincidence exceeds a predetermined threshold, it can be determined that the authentication is successful.
As described above, various modifications can be made within the scope of the present invention according to the embodiment to be implemented.

(A) is a figure which shows roughly a part of the binarized image which binarized the fingerprint image, (b) performed the thinning process with respect to the binarized image of (a) It is a figure which shows a thin line image roughly. 1 is a functional block diagram of a fingerprint image generation apparatus according to a first embodiment to which the present invention is applied. (A) is a figure which shows the outline of the one part area | region of a multi-value fingerprint image, (b) shows the outline of the pixel value profile of the direction which crosses a ridge and a valley line in AA 'of (a). FIG. (A) is a figure which shows the mode at the time of setting a local area | region so that it may correspond with the ridge area | region of a multi-value fingerprint image, (b) is a pixel of the local area | region in BB 'of (a). It is a figure which shows the outline of a value profile. (A) is a figure which shows a mode when a local area | region is set so that it may correspond with the valley line area | region of a multi-value fingerprint image, (b) is a pixel of the local area | region in CC 'of (a). It is a figure which shows the outline of a value profile. It is a flowchart which shows the operation | movement of thinned binary image generation in the fingerprint image generation apparatus which is 1st Embodiment to which this invention is applied. It is a flowchart which shows the operation | movement of thinned binary image generation in the fingerprint image generation apparatus which is 1st Embodiment to which this invention is applied. It is a functional block diagram of the fingerprint authentication device which is the second embodiment to which the present invention is applied. It is a figure which shows the outline of the feature point in the thinned fingerprint image. It is a flowchart which shows the operation | movement of fingerprint authentication in the fingerprint authentication apparatus which is 2nd Embodiment to which this invention is applied.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Fingerprint image generation apparatus 11 Image input / output part 12 Image generation part 13 Reference value setting means 14 Local area setting means 15 Ridge line candidate area determination means 16 Threshold setting means 17 Binarization means 2 Fingerprint authentication apparatus 21 Operation / display part 22 Fingerprint input unit 23 Storage unit 24 Processing unit 25 Control unit 26 Image correction unit 27 Unevenness correction unit 28 Fingerprint binarization unit 29 Thinning unit 30 Feature extraction unit 31 Verification unit 32 Output unit 100 Ridge region 110 Valley region 120, 130 Sweat Gland Hole Area 140 Ridge Area 150, 160 Pseudo Feature Point 300 Valley Line Area 310 Ridge Line Area 320 Sweat Gland Hole Area 900, 910 Feature Point (End Point)
920 feature point (branch point)

Claims (8)

A fingerprint that generates a binary fingerprint image represented by binary values of a ridge pixel value that represents a ridge region and a valley pixel value that represents a valley region from a multi-value fingerprint image in which the unevenness of the fingerprint is represented in multiple values An image generation device,
Local area setting means for setting a local area within the entire area of the multi-value fingerprint image;
Ridge candidate area determination means for determining whether or not the local area is a ridge candidate area based on a comparison result between a local representative value representing a value of a pixel included in the local area and a predetermined reference value. When,
The first binarization threshold for the local area determined as the ridge candidate area is determined as the ridge area rather than the second binarization threshold for the local area determined as not the ridge candidate area. A threshold setting means for setting an easy value;
Binarization means for binarizing pixels included in the local region with the first or second binarization threshold;
A fingerprint image generating apparatus comprising:   The fingerprint image generation apparatus according to claim 1, wherein the local region setting unit sets a predetermined range including a target pixel of the multi-value fingerprint image as a local region for the target pixel.   The fingerprint image generation device according to claim 1, wherein the local representative value is an average pixel value or a median value calculated based on values of some or all pixels included in the local region.   The fingerprint image generation apparatus according to any one of claims 1 to 3, wherein the reference value is an average pixel value of an entire region of the multi-value fingerprint image.   The fingerprint image generation according to any one of claims 1 to 4, wherein the threshold setting unit sets the first binarization threshold to a value that is easier to determine as the ridge region than the reference value. apparatus.   The multi-value fingerprint image expresses a concave portion of a fingerprint with a high pixel value and a convex portion with a low pixel value, and the threshold setting means sets the first binarization threshold to a pixel value higher than the reference value. The fingerprint image generating apparatus according to claim 5, wherein   The threshold setting means sets the first binarization threshold to a higher pixel value as the local representative value of the local region determined as the ridge candidate region is lower than the reference value. Fingerprint image generator.   The threshold value setting unit obtains a difference value between the reference value and the local representative value, and sets a value obtained by adding the difference value and the reference value as the first binarization threshold value. The fingerprint image generating apparatus described.

Images