JP2006209364A - Image acquisition method and device, and collating method and device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To acquire an image of a fingerprint or the like with a small memory capacity and low computational complexity. <P>SOLUTION: An imaging part acquires a subject image in a plurality of partial images. An arithmetic part calculates in each partial image Po a feature quantity Pf characterizing directions of lines in the partial image Po, and uses the feature quantity of each image Po to construct the subject image in a single image. A feature quantity is alternatively constructed in the subject image constructed in the single image. If the subject image has an overlap of partial images Po, the arithmetic part can connect the partial images Po such that corresponding portions of the feature quantities Pf of the partial images Po overlap one another. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an image acquisition method and apparatus that can be used when performing personal authentication using biometric information such as fingerprints and irises, and a collation method and apparatus.

  In recent years, fingerprint authentication systems have been installed in mobile devices such as mobile phones. Unlike desktop PCs and large-scale systems, portable devices are limited in memory and CPU performance, so an authentication method that can be implemented with a small amount of memory and an inexpensive CPU is required.

  Conventional fingerprint authentication methods are roughly classified into (a) minutia method, (b) pattern matching method, (c) chip matching method, and (d) frequency analysis method. (A) In the minutiae method, fingerprint points are identified by extracting characteristic points such as ridge ends and branches from the fingerprint image, that is, minutiae, and comparing the information of these points between the two fingerprint images. And determine whether or not the person is the person.

  (B) The pattern matching method performs fingerprint comparison by directly comparing image patterns between two fingerprint images, and determines whether or not the person is the person. (C) The chip matching method has an image of a small area around a feature point, that is, a chip image as registration data, and performs fingerprint collation using this chip image. (D) The frequency analysis method performs frequency analysis on a line obtained by slicing a fingerprint image, and compares fingerprint components by comparing the distribution of frequency components in the direction perpendicular to the line direction between two fingerprint images. I do.

Patent Document 1 extracts a feature vector and its quality index from a fingerprint image, etc., assigns reliability information obtained using an error distribution of the feature vector to a feature quantity, and performs fingerprint matching using these. Disclose technology.
Japanese Patent Laid-Open No. 10-177650

  However, (a) the minutiae method and (c) the chip matching method require preprocessing such as connecting cut portions in the captured image, and the amount of calculation increases. (B) Since the pattern matching method has to store the data of the entire image, the memory capacity becomes large especially when data of a large number of people is registered. (D) Since the frequency analysis method requires frequency conversion, the amount of calculation increases. The above-mentioned Patent Document 1 also requires a statistical analysis, which increases the amount of calculation.

  The present invention has been made in view of these problems, and an object thereof is to provide an image acquisition method and apparatus capable of acquiring an image with a small memory capacity and a small calculation amount.

  Another object is to provide a collation method and apparatus capable of collating images with a small memory capacity and a small amount of calculation.

  In order to solve the above problems, an image acquisition method according to an aspect of the present invention includes a step of acquiring a target image as a plurality of partial images, and a feature amount that characterizes the direction of a line in the partial image for each partial image, Alternatively, a feature amount that characterizes the partial image as a single physical quantity and a feature amount for each partial image are used to construct the target image into a single image, or the target image into a single image. And constructing a feature quantity when constructed.

  The “line” may be a ridge or valley line of a fingerprint. The “feature amount characterizing the direction of the line” may be a value calculated based on the gradient vector of each pixel. The “single physical quantity” may be a vector quantity or a scalar quantity, and may be an image density switching mode such as a fringe switching count. According to this aspect, an image can be acquired with a small memory capacity and a small calculation amount.

  Another aspect of the present invention is also an image acquisition method. In this method, a target image is acquired as a plurality of partial images, a step of dividing each partial image into a plurality of regions along a predetermined direction, and a direction of a line in the region is determined for each of the divided regions. The step of calculating the feature quantity to be characterized or the feature quantity that characterizes the region as a single physical quantity, generating a feature quantity group along a predetermined direction for each partial image, and correspondence between the feature quantity groups between the partial images Referring to the relationship, constructing the target image into one image, or constructing a feature amount when the target image is constructed into one image.

  The “feature group” may be a function of coordinate axes along each direction. According to this aspect, an image can be acquired with a small memory capacity and a small calculation amount.

  In the building step, when a part of the target image overlaps between the partial images, the partial images may be connected so that corresponding portions of the feature amount groups between the partial images overlap. According to this aspect, even when a part of the target image overlaps between partial images, such as when an image is captured while the relative positional relationship between the imaging target and the image sensor changes, the image is reduced with a small memory capacity and a small amount of calculation. Can be obtained.

  Yet another embodiment of the present invention is an image acquisition device. This apparatus includes an imaging unit that acquires a target image as a plurality of partial images, and a feature amount that characterizes the direction of a line in the partial image or a feature amount that characterizes the partial image as a single physical amount for each partial image. A calculation unit for calculating. The calculation unit uses the feature amount of each partial image to construct the target image into one image, or constructs the feature amount when the target image is constructed into one image.

  The “imaging unit” may include a line sensor, and may acquire a “partial image” by a relative position change with respect to the imaging target. According to this aspect, an image can be acquired with a small memory capacity and a small calculation amount.

  Yet another embodiment of the present invention is also an image acquisition device. This apparatus includes an imaging unit that acquires a target image as a plurality of partial images, and a feature amount that characterizes the direction of a line in the region for each region divided into a plurality of regions along a predetermined direction, or A calculation unit that calculates a feature value that characterizes the region as a single physical quantity, and generates a feature value group along a predetermined direction for each partial image. The calculation unit refers to the correspondence relationship between the feature amount groups between the partial images, and constructs the feature amount when the target image is constructed as one image or when the target image is constructed as one image. According to this aspect, an image can be acquired with a small memory capacity and a small calculation amount.

  When a part of the target image overlaps between the partial images, the calculation unit may connect the partial images so that the corresponding portions of the feature amount groups between the partial images overlap. According to this aspect, even when a part of the target image overlaps between partial images, such as when the image is captured while the relative positional relationship between the imaging target and the imaging unit is changed, the image is reduced with a small memory capacity and a small amount of calculation. Can be obtained.

  Yet another embodiment of the present invention is a verification method. This method includes a step of acquiring a target image to be collated as a plurality of partial images, and a feature amount that characterizes the direction of a line in the partial image for each partial image, or a feature amount that characterizes the partial image as a single physical quantity. And a step of collating the feature quantity of the partial image constituting the target image with the corresponding feature quantity of the partial image constituting the reference image. According to this aspect, it is possible to collate images with a small memory capacity and a small calculation amount.

  Yet another embodiment of the present invention is also a collation method. In this method, a target image to be collated is acquired as a plurality of partial images, a step of dividing each partial image into a plurality of regions along a predetermined direction, and a line in the region for each divided region. Calculating a feature quantity that characterizes a direction or a feature quantity that characterizes a region as a single physical quantity, generating a feature quantity group along a predetermined direction for each partial image; and Collating the feature quantity group with the corresponding feature quantity group of the partial image constituting the reference image. According to this aspect, it is possible to collate images with a small memory capacity and a small calculation amount.

  Yet another embodiment of the present invention is a collation device. This apparatus includes an imaging unit that acquires a target image to be collated as a plurality of partial images, and a feature amount that characterizes the direction of a line in the partial image for each partial image, or a feature that characterizes the partial image as a single physical amount. A calculation unit that calculates a quantity; and a matching unit that matches a feature quantity of a partial image constituting the target image with a corresponding feature quantity of the partial image constituting the reference image. According to this aspect, it is possible to collate images with a small memory capacity and a small calculation amount.

  Yet another embodiment of the present invention is also a verification device. This apparatus includes an imaging unit that acquires a target image to be collated as a plurality of partial images, and a feature amount that characterizes the direction of a line in each region divided into a plurality of regions along a predetermined direction for each partial image. Or calculating a feature quantity that characterizes a region as a single physical quantity, generating a feature quantity group along a predetermined direction for each partial image, and a feature quantity group of a partial image constituting the target image; A collation unit that collates the feature amount groups of the partial images constituting the reference image. According to this aspect, it is possible to collate images with a small memory capacity and a small calculation amount.

  It should be noted that any combination of the above-described constituent elements and a conversion of the expression of the present invention between a method, an apparatus, a system, a computer program, a recording medium, and the like are also effective as an aspect of the present invention.

  According to the present invention, it is possible to acquire an image with a small memory capacity and a small calculation amount.

(Embodiment 1)
The first embodiment obtains one vector characterizing the direction of a ridge or valley in a line-shaped area along a line perpendicular to the reference direction on the fingerprint image. The component is calculated. Then, the distribution of the reference direction of the component is obtained, and compared with the distribution of registered data obtained by the same method, the fingerprint images are collated and authenticated.

  FIG. 1 is a diagram illustrating functional blocks of the matching device 1 according to the first embodiment. Each block shown here can be realized in hardware by various elements such as a processor and RAM, and various devices such as sensors, and in software, it can be realized by a computer program. Depicts functional blocks realized by. Accordingly, those skilled in the art will understand that these functional blocks can be realized in various forms by a combination of hardware and software.

  The collation device 1 includes an imaging unit 100 and a processing unit 200. The imaging unit 100 uses a CCD (Charge Coupled Device) or the like, images a user's finger, and outputs the image to the processing unit 200 as image data. For example, when a line sensor such as a CCD is mounted on a portable device, a fingerprint image is collected by holding a finger over the line sensor and sliding the finger vertically with respect to the line sensor. May be.

  The processing unit 200 includes an image buffer 210, a calculation unit 220, a collation unit 230, and a recording unit 240. The image buffer 210 is a memory area that temporarily stores image data input from the imaging unit 100 and is also used as a work area of the calculation unit 220. The calculation unit 220 performs various calculations described later on the image data in the image buffer 210. The collation unit 230 compares the feature amount of the image data stored in the image buffer 210 to be an authentication target with the feature amount of the image data registered in the recording unit 240, and determines whether the fingerprints are the same person. Determine whether or not. The recording unit 240 registers the feature amount of a fingerprint image captured in advance. When used for a mobile phone or the like, data for one person is often registered, but when used for a gate in a room or the like, data for a plurality of persons is often registered.

  FIG. 2 is a flowchart for explaining processing for generating reference data of the collation device 1 according to the first embodiment. In this reference data, a fingerprint image of a person to be authenticated is registered in advance as a distribution of a predetermined direction component, here as a distribution of a feature amount characterizing a ridge or valley direction in a linear region. Is.

  First, the imaging unit 100 captures an image of a finger held by the user, converts the captured image into an electrical signal, and outputs the electrical signal to the processing unit 200. The processing unit 200 acquires the signal as image data and temporarily stores it in the image buffer 210 (S10). The arithmetic unit 220 converts the image data into binary data (S12). For example, a value brighter than a predetermined threshold is determined to be white, and a value darker than that is determined to be black. White is expressed as 1 or 0, and black is expressed as 0 or 1.

  Next, the calculation unit 220 divides the binarized image data for each linear region (S14). FIG. 3 is a diagram illustrating a captured fingerprint image in the first embodiment. In FIG. 3, the calculation unit 220 forms a linear region 12 with the X-axis direction as the longitudinal direction and the Y-axis direction as the short direction on the fingerprint image. In this linear region, the short direction is preferably set to one pixel or three pixels. A plurality of such linear regions are formed in the Y-axis direction, that is, the longitudinal direction of the finger, and the fingerprint image is divided into a plurality of regions.

  Next, the calculation unit 220 calculates the gradient of each pixel (S16). As a method for calculating the gradient, the method described in the document “Computer Image Processing, edited by Hideyuki Tamura, Ohm Co., Ltd. (p182-191)” can be used.

Briefly described below, in order to calculate a gradient for a digital image, it is necessary to calculate first-order partial differentials in the x and y directions.
Δ _x f (i, j) ≡ {f (i + 1, j) −f (i−1, j)} / 2 (Expression 1)
Δ _y f (i, j) ≡ {f (i, j + 1) −f (i, j−1)} / 2 (Expression 2)

In the difference operator for a digital image, the differential value of the pixel at (i, j) is a density value of a pixel in the vicinity of 3 × 3 centered at (i, j), that is, f (i ± 1, j ± 1). Defined as a linear combination. This means that the calculation for obtaining the derivative of the image can be realized by spatial filtering using a 3 × 3 weighting matrix, and various difference operators can be expressed by a 3 × 3 weighting matrix. In the following, a 3 × 3 neighborhood centered at (i, j) is represented by f (i−1, j−1) f (i, j−1) f (i + 1, j−1)
f (i-1, j) f (i, j) f (i + 1, j) (Formula 3)
f (i-1, j + 1) f (i, j + 1) f (i + 1, j + 1)
The difference operator can be described by a weighting matrix for this.

For example, the first-order partial differential operators in the x and y directions defined by Equation 1 and Equation 2 are
0 0 0 0 -1/2 0
−1/2 0 1/2 and 0 0 0 (Equation 4)
0 0 0 0 1/2 0
It is expressed. That is, in the 3 × 3 rectangular area of Equation 3 and Equation 4, the product of the value of the pixel at the corresponding position and the value of the element of the matrix are obtained, and the sum of those is calculated, and the right side of Equation 1 and Equation 2 Match.

The gradient magnitude and direction are spatially filtered using the weighting matrix of Equation 4, and after calculating partial differentials in the x and y directions defined by Equations 1 and 2,
| ∇f (i, j) | = √ {Δ x f (i, j) 2 + Δ y f (i, j) 2} ... ( Equation 5)
_{^{θ = tan -1 {Δ y f}} (i, j) / Δ x f (i, j)} ... ( Equation 6)
It can be asked.

  Note that the difference operator can be simply calculated using a Roberts operator, a Plewit operator, a Sobel operator, or the like, and noise countermeasures can be taken.

  Next, the computing unit 220 obtains a set of numerical values obtained by doubling the gradient direction obtained by Equation 6, that is, the angle of the gradient vector (S18). In this embodiment, the direction of the ridge or valley line of the fingerprint is calculated using the gradient vector, but the value of the gradient vector is not the same even if the ridge or valley line is in the same direction. . Therefore, by rotating the gradient vector so that the angle formed by the gradient vector and the coordinate axis is doubled to obtain a set of numerical values composed of the x component and the y component, a ridge or valley line in the same direction is obtained. It can be expressed by a set of unique numerical values having the same components. For example, 45 ° and 225 ° are just opposite directions, but if doubled, they become 90 ° and 450 °, indicating a unique direction. Here, a set of numerical values composed of an x component and a y component is obtained by rotating a vector according to a certain rule in a certain coordinate system. Hereinafter, this numerical value is also expressed as a vector in this specification.

  The direction of ridges or valleys in a certain area of a fingerprint image varies widely locally, so that an average is taken in a certain range as will be described later. In that case, if the angle of the gradient vector is doubled to make it a unique vector as described above, an approximate value of the direction of the ridge or valley is obtained by simply adding them and averaging them. be able to. If this is not done, adding a gradient vector pointing in the opposite direction will result in 0, and a simple addition cannot be used. In this case, a complicated calculation is required to compensate that 180 ° and 0 ° are equivalent.

  Next, the arithmetic unit 220 calculates an averaged vector by adding the vectors calculated for each pixel for each linear region. This vector becomes the feature amount of the region (S20). This feature amount is a value indicating the average of the direction of the ridge or valley of the region, and one feature amount is determined for each region.

  At this time, when a plurality of white points or black points continue, a state in which the gradient cannot be defined continues. Therefore, when the number exceeds a predetermined number, the portion may be excluded from the target of the averaging process. The predetermined number of optimum values may be obtained experimentally.

  Finally, the calculation unit 220 obtains the x component and y component of the vector obtained for each region and records them as reference data in the recording unit 240 (S22). Note that the calculation unit 220 may record the x component and y component distribution of the vector after performing a smoothing process as described later.

  FIG. 4 is a diagram showing a vector V (y0) representing the feature in the linear region 12 of FIG. The linear region 12 is a region cut out at y = y0 on the coordinate plane shown in FIG. FIG. 4 shows a vector V = (Vx, Vy) representing features in the ridge or valley direction within that region. Vx (y0) and Vy (y0) represent end point coordinates on orthogonal coordinates with the start point of the vector V (y0) as the origin. As the feature amount, an average value obtained by doubling the angle of the gradient vector of each pixel described above can be used.

  FIG. 5 is a diagram illustrating a distribution of feature amounts when the image of FIG. 3 is sliced into linear regions. FIG. 4 shows a distribution of feature amounts when scanning is performed in the y direction on the coordinate plane shown in FIG. 3, that is, in a direction perpendicular to the slice direction of a linear region. The horizontal axis in FIG. 5 indicates the y axis in FIG. 3, that is, the sub-scanning direction, and the vertical axis indicates the feature amount in each region. In FIG. 5, as shown in FIG. 4, a vector characterizing each region is expressed by an x component and a y component. The calculation unit 220 can obtain the distribution of the x component and the y component of the vector characterizing each region from the fingerprint image to be registered, and record it as reference data in the recording unit 240.

  FIG. 6 is a flowchart for explaining the authentication process of the verification device 1 according to the first embodiment. First, the imaging unit 100 captures an image of a finger held by a user requesting authentication, converts the captured image into an electrical signal, and outputs the electrical signal to the processing unit 200. The processing unit 200 performs the same processing as the processing shown in steps S12 to S20 in FIG. 2 on the acquired image, and calculates the feature amount distribution of the image data as authentication target data (S30).

  Next, the computing unit 220 applies a smoothing process to the feature amount distribution (S32). For example, several points of the previous and subsequent values are averaged. The degree of smoothing depends on the application and the optimum value may be obtained experimentally.

  Next, the collation unit 230 collates the feature quantity distribution of the reference data with the feature quantity distribution of the authentication target data (S34). This collation is performed by obtaining the most matching pattern by fixing one distribution and sliding the other distribution. You can simply perform pattern matching for all patterns, but in order to reduce the amount of calculation, detect the attention points of both distributions, and only perform matching of several patterns around the matching points. May be. For example, a point that becomes the maximum value of the x component, a point that is 0, a point that becomes 0 when differentiated, a point with the steepest slope, or the like can be used as a point of interest.

Pattern matching can be performed by detecting a difference between each reference sentence and authentication target data for each point on the y-axis. For example, the calculation is performed by calculating the matching energy E by the following equation (7).
E = Σ√ {ΔVx ² + ΔVy ² } Equation 7

  Taking the square root of the value obtained by squaring the error ΔVx of the x component and the error ΔVy of the y component of both distributions, the x component and the y component are originally vector components, so that the vector size error is obtained. be able to. A value obtained by adding them in the y-axis direction is defined as a matching energy E. Therefore, the larger the energy E, the closer the image becomes, and the smaller the energy E, the closer the image. Then, the pattern having the smallest matching energy E is set as the overlapping position (S36). The pattern matching method is not limited to this, and the absolute values of the x component error ΔVx and the y component error ΔVy of both distributions may be added together. A highly accurate collation method may be experimentally obtained and used.

  FIG. 7 is a diagram depicting the feature quantity distribution of the reference data and the feature quantity distribution of the authentication target data in the first embodiment. In FIG. 7, the feature amount distribution of the reference data is indicated by a solid line, and the feature amount distribution of the authentication target data is indicated by a broken line. In the example of FIG. 7, the maximum value of the x component of both distributions is first detected. Then, pattern matching is performed at a position where the maximum values p1 coincide with each other and a position where one of the distributions is shifted from that position by several points, and the position that best matches is set as the overlapping position.

  The collation unit 230 compares the calculated matching energy E with a threshold value for determining whether or not to succeed in the preset authentication. If the matching energy E is less than the threshold value, the reference data The user of the fingerprint image is authenticated (S38). Conversely, if the matching energy E is greater than or equal to the threshold value, authentication is not performed. The above processing is performed between each reference data and authentication target data when a plurality of reference data is registered.

  As described above, according to the first embodiment, an image of biometric information such as a fingerprint is divided into predetermined areas, and values that characterize each area are used for matching processing between the reference image and the authentication target image. As a result, authentication processing can be performed with a small memory capacity. Also, the amount of calculation can be reduced, and the authentication process can be speeded up. Therefore, if it is applied to authentication processing for a portable device such as a battery drive, the circuit area can be reduced and power can be saved. Furthermore, by obtaining a feature amount for each linear region, it is suitable for collation of fingerprint images captured by a line sensor or the like. Furthermore, the feature amount of each pixel is averaged for each region, and the distribution of the averaged feature amount is smoothed, so that it is also resistant to noise. In addition, since the averaging process is performed on the linear area, it is possible to collate whether or not the fingers are the same person even if the finger slides left and right between the reference data and the authentication target data. Further, unlike the minutiae method, even a noisy fingerprint image can function effectively if registration and authentication are performed in the same state.

(Embodiment 2)
In the first embodiment, an example of dividing in one direction has been described as a method of dividing an image for obtaining a linear region. In this regard, in the second embodiment, an example of dividing in a plurality of directions will be described. For example, an image can be sliced in two directions.

  First, the configuration and operation of the collation device 1 according to the second embodiment are the same as those of the collation device 1 according to the first embodiment shown in FIG. FIG. 8 is a diagram illustrating an example of slicing an image in the vertical direction according to the second embodiment. Here, as an image to be collated, in addition to the above-described fingerprint image, an iris image or other images expressing biological information may be used. In FIG. 8, it is expressed as a striped pattern image for convenience. FIG. 9 is a diagram illustrating the distribution of feature amounts in each linear region in FIG. The feature amounts A and B characterizing each linear region are obtained, and the distribution in the y direction, that is, the vertical direction is shown.

  FIG. 10 is a diagram illustrating an example of slicing an image in the oblique 45 ° direction according to the second embodiment. FIG. 10 slices the same image as in FIG. 8 in a 45 ° oblique direction. FIG. 11 is a diagram showing the distribution of feature amounts in each linear region in FIG. The feature amounts C and D characterizing each linear region are obtained, and the distribution in the z direction, that is, the oblique 45 ° direction is shown. In this manner, the image is sliced in two directions, and the feature of the image is expressed by the four types of feature amounts A, B, C, and D. The process of generating reference data and the process of authenticating in the second embodiment can be performed in the same manner as in FIGS. 2 and 6 described in the first embodiment, using these feature amounts. In this case, since there are a plurality of matching energies E calculated from each direction, for example, the collation unit 230 may determine the average value to determine whether to authenticate.

  Here, the feature amount is not limited to the x component and the y component of the vector that characterizes the ridge or valley line in the linear region described in the first embodiment. For example, it may be image density, brightness, color information, other local image information, or a scalar amount, a vector amount, or other numerical values obtained from these image information by differentiation or other calculations.

  Note that the image in the second embodiment may be captured by a one-dimensional sensor such as a line sensor, loaded into the image buffer 210, and sliced in two or more directions, or a two-dimensional sensor may be used. Examples of two or more directions are generally the vertical direction, the horizontal direction, the oblique 45 °, and the oblique 135 °, but are not limited thereto, and can be sliced in any direction. Furthermore, the combination of two or more directions is not limited to the vertical direction and the oblique 45 °, and can be arbitrarily set.

  As described above, according to the second embodiment, by using a plurality of different directions as slice directions of an image for obtaining a linear region, the accuracy of matching can be improved as compared with the first embodiment. . In the second embodiment, another image is not generated from a certain image as in the minutia method, and therefore, it is sufficient if the memory capacity is sufficient to store the original image. Therefore, highly accurate authentication can be performed with a small memory capacity and a small amount of calculation.

(Embodiment 3)
In the first and second embodiments, an example in which the sliced linear region has a linear shape has been described. This shape is not limited thereto, and may be a non-linear shape such as a curved shape, a closed curved shape, a circular shape, or a concentric circular shape. In the third embodiment, the matching of iris images will be described as a typical example of dividing into non-linear regions.

  First, since the configuration and operation of the collation device 1 in the third embodiment are also the same as those of the collation device 1 in the first embodiment shown in FIG. FIG. 12 is a diagram illustrating an example of dividing the iris portion of the eye into concentric regions in the third embodiment. In FIG. 12, each linear region is divided into concentric circles. FIG. 13 is a diagram showing the distribution of feature amounts in each concentric region in FIG. The distribution of the dynamic system direction r of values characterizing each region is obtained. The process of generating reference data and the process of authenticating in the third embodiment can be performed in the same manner as in FIGS. 2 and 6 described in the first embodiment, using this feature amount.

  In the case of an iris, the matching process described in FIG. 6 is simplified. The distribution of both feature amounts may be overlapped so that the boundary R0 between the pupil and the iris and the boundary R1 between the iris and the white eye portion match between the authentication target data and the reference data. However, since the size of the pupil changes depending on the brightness of the surroundings, it is necessary to expand and contract uniformly so that the distance between the boundary R0 and the boundary R1 of the authentication target data matches that of the reference data.

  As described above, according to the third embodiment, the iris image can be collated with a small memory capacity and a small calculation amount. In addition, since the feature amount is obtained by averaging the gradient vector of each pixel in each concentric region, even if the eye position is rotated with respect to the reference data, it is possible to accurately collate. This property corresponds to the property that the finger image is resistant to sliding in the horizontal direction in the fingerprint image of the first embodiment. In addition, even when the eyelid is blocked and all the portions of the iris cannot be imaged, a considerable degree of accuracy can be maintained even if collation is performed on a plurality of regions near the pupil.

  When half of the iris is used for collation, it may be divided not for concentric areas but for each semicircular area. Further, such a method of dividing in a non-linear region is not limited to an iris image but can be applied to a fingerprint image. For example, the center of the fingerprint vortex may be detected and concentrically divided from there.

(Embodiment 4)
In the above-described embodiment, the example in which the feature amount distributions as illustrated in FIG. 5 are compared and collated has been described. In the fourth embodiment, an example in which an entire image is generated from a plurality of partial images acquired when the imaging unit 100 is configured by a line sensor or the like will be described as a premise.

  FIG. 14 is a flowchart for explaining image acquisition processing according to the fourth embodiment. First, in the fourth embodiment, it is assumed that the imaging unit 100 does not include a surface sensor that can be acquired by one imaging with an imaging target such as a finger as an entire image. Therefore, the collation device 1 obtains a plurality of partial images of the imaging target by having the user move the imaging target and capturing images a plurality of times with a sensor having a small imaging area. For example, when acquiring a fingerprint image with a line sensor, a plurality of partial images of the fingerprint image are acquired by having the line sensor slide the finger from top to bottom or from bottom to top.

  In FIG. 14, as described above, the collation device 1 acquires a plurality of partial images to be imaged from the imaging unit 100 and temporarily stores them in the image buffer 210 (S60). The calculation unit 220 calculates a feature for each partial image (S62). Here, the feature may be obtained as a function of a coordinate axis indicating a reference direction for obtaining the feature, or may be a distribution of feature amount calculated based on the gradient vector as described above. . Then, the features of the adjacent partial images are connected to generate the features of the entire image (S64). Hereinafter, as described in the above-described embodiment, the features of the entire image can be compared and collated to perform personal authentication.

  FIG. 15 is a diagram illustrating a process of generating features of the entire image from the partial image according to the fourth embodiment. FIG. 15 shows a plurality of partial images Po acquired by having the user slide his / her finger from the top or bottom or the bottom to the top with respect to the line sensor mounted horizontally. The length of each partial image Po is set to about 6 to 8 pixels, and the width is set to about 100 to 200 pixels. This corresponds to the imaging area of the line sensor. When the verification device 1 is applied to a mobile terminal or the like, a space for attaching the imaging unit 100 is limited, and thus a sensor having an imaging region of that degree is often used. In addition, the imaging unit 100 captures images at a timing such that the upper half and the lower half of the adjacent partial images Po overlap with each other under the assumed finger sliding speed.

  The calculation unit 220 calculates a feature amount Pf for each partial image Po. Next, the features Pf of the continuous partial images Po are connected. Here, there is a possibility that the finger slide speed may be different every time depending on the difference of users, etc., so it is necessary to detect how much of the adjacent partial images Po overlap, that is, the connection condition. In some cases, there are no overlapping portions, and adjacent partial images Po can be directly connected. In FIG. 15, as a method for superimposing the feature Pf of each partial image Po, the x component and the y component of the gradient vector are superimposed between the adjacent partial images Po. Thereby, it is possible to detect how many pixels slide between the adjacent partial images Po, and by performing this between the adjacent partial images Po, it is possible to generate the characteristics of the entire image of the fingerprint. it can.

  Note that when the finger slide stops, the same partial image is acquired. In this case, the calculation unit 220 may delete any of the adjacent partial images. Alternatively, at least one partial image near the upper end or the lower end may be ignored, and a partial image mainly near the center may be generated as reference data or authentication target data. In addition, when the imaging unit 100 cannot capture a part of the entire image, such as when the user slides a finger very quickly, the feature amount of the entire image cannot be constructed. An image input may be requested.

  In the present embodiment, features of the entire image are generated from a plurality of partial images. In this regard, the connection conditions may be obtained by comparing and collating the features Pf of the adjacent partial images Po, and the whole image may be reconstructed from the plurality of partial images Po using this. Hereinafter, using this whole image, authentication is performed not only by the authentication method for comparing and collating the features described in the above-described embodiments, but also by the above-described minutiae method, pattern matching method, chip matching method, and frequency analysis method. be able to.

  As described above, according to the fourth embodiment, when the features of the entire image are obtained from the partial image to be imaged, the features of each partial image are extracted and the features of the whole image are obtained using those features. As a result, the memory capacity can be reduced. That is, a method of comparing and collating partial images to reconstruct an entire image is common, but in this embodiment, in order to compare and collate features of partial images, the partial images themselves are compared and collated. It does not require the necessary memory area, and does not cause a pressure on the system. Therefore, it can be easily applied to a system with relatively low specifications. This effect is generally applicable when the entire image itself is reconstructed from a plurality of partial images by the method of the present embodiment.

(Embodiment 5)
In the fourth embodiment, an example in which an entire image is generated from a plurality of partial images has been described. In this regard, a collation method that does not require construction of the entire image will be described.

  FIG. 16 is a diagram for explaining an example in which features are compared and collated for each partial image in the fifth embodiment. As described in the fourth embodiment, the calculation unit 220 extracts features for each partial image. After that, the partial images are not connected to construct the whole image, but are stored in the recording unit 240 as the reference data while maintaining the characteristics of the partial images. Further, at the time of authentication, the characteristics of a set of partial images constituting the entire image registered in the recording unit 240 in advance are compared and collated with the calculated characteristics of the partial images. At that time, the features of the corresponding partial images are compared and collated. In addition, there may be a partial image constituting the entire image that is not used for collation. For example, it is not necessary to compare and collate features of partial images that have a small contribution to collation.

  The collation unit 230 calculates the matching energy E for each partial image as described in the first embodiment. Then, the sum or average value of the matching energies E of the partial images constituting the entire image is compared with a threshold value for determining whether or not to succeed in the authentication, and whether or not to authenticate the person Determine whether.

  As described above, according to the fifth embodiment, it is necessary to construct the entire image from the partial images by comparing and collating each feature of the partial image to be imaged instead of comparing and collating the features of the entire image. Therefore, the images can be collated with a small memory capacity and a small calculation amount. For example, it is particularly suitable for simple authentication, such as when allowing the use of game machines and toys.

  The present invention has been described above based on the embodiment. It is to be understood by those skilled in the art that these embodiments are exemplifications, and various modifications can be made to the combination of each component, and such modifications are also within the scope of the present invention.

  In the above-described embodiment, for each linear region, a vector calculated from the gradient vector of each pixel is used as a single physical quantity that characterizes the region. In this regard, as the single physical quantity, the number of times of switching the image density may be used for each region. For example, the image may be binarized into a black and white image, and the number of times that the black and white are switched may be counted. The count value is the number of stripes such as fingerprints. Where the stripes are vertical, the density is high, and a lot of switching occurs within a certain distance, that is, per unit length. This may be done in the x and y directions. According to this, collation is possible with a simple calculation and a small memory capacity.

  In the above-described embodiment, binary data is used. However, multi-tone data such as 256 values may be used. Also in that case, the technique described in the above-mentioned document “Computer Image Processing, edited by Hideyuki Tamura, Ohm Co., Ltd., (p182-191)” can be used, and the gradient vector of each pixel can be calculated. According to this, although the amount of calculation increases compared with a black-and-white image, highly accurate collation can be performed.

  Furthermore, in the above-described collation process, a region that is not striped may be omitted from each linear region. For example, if a vector cannot be detected for a certain line length more than a set value, white continues for a set value or more, black is determined to be a black area that continues for more than a set value, black and white switching count When the value is less than the set value, the processing is performed by omitting the area. Thereby, the amount of calculation at the time of collation can be reduced.

  Further, the process of constructing the whole image or the whole image feature using the feature of the partial image described in the fourth embodiment described above is a simple divided part without overlapping parts or missing parts between the partial images. The present invention can be applied in general when an entire image is constructed from images.

It is a figure which shows the functional block of the collation apparatus in Embodiment 1. FIG. 6 is a flowchart for explaining a process of generating reference data for the collation device according to the first embodiment. It is a figure which shows the fingerprint image imaged in Embodiment 1. FIG. It is a figure which shows the vector V (y0) expressing the characteristic in the linear area | region of FIG. It is a figure which shows distribution of the feature-value at the time of slicing the image of FIG. 3 in each linear area | region. 5 is a flowchart for explaining an authentication process of the collation device according to the first embodiment. FIG. 3 is a diagram in which the feature quantity distribution of reference data and the feature quantity distribution of authentication target data are overlaid in the first embodiment. It is a figure which shows the example which slices the image in Embodiment 2 to the orthogonal | vertical direction. It is a figure which shows distribution of the feature-value in each linear area | region of FIG. It is a figure which shows the example which slices the image in Embodiment 2 in the diagonal 45 degree direction. It is a figure which shows distribution of the feature-value in each linear area | region of FIG. It is a figure which shows the example which divides | segments the iris part of the eye in Embodiment 3 into a concentric area | region. It is a figure which shows distribution of the feature-value in each concentric area | region of FIG. 14 is a flowchart for explaining image acquisition processing according to the fourth embodiment. FIG. 10 is a diagram illustrating a process of generating features of an entire image from a partial image according to a fourth embodiment. FIG. 10 is a diagram for explaining an example of comparing and collating features for each partial image in the fifth embodiment.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 collation apparatus, 100 imaging part, 200 process part, 210 image buffer, 220 calculating part, 230 collation part, 240 recording part.

Claims (10)

Obtaining a target image as a plurality of partial images;
For each partial image, calculating a feature amount that characterizes the direction of a line in the partial image, or a feature amount that characterizes the partial image as a single physical amount; and
Using the feature amount for each partial image, constructing the target image into a single image, or constructing a feature amount when the target image is constructed into a single image;
An image acquisition method comprising: Obtaining a target image as a plurality of partial images;
Dividing each partial image into a plurality of regions along a predetermined direction;
For each divided area, a feature quantity characterizing the direction of a line in the area or a feature quantity characterizing the area as a single physical quantity is calculated, and a feature quantity group along the predetermined direction is calculated for each partial image. Generating steps,
Referring to the correspondence between the feature quantity groups between the partial images, constructing the target image into a single image, or constructing a feature quantity when the target image is constructed into a single image; ,
An image acquisition method comprising:   The building step is characterized in that, when a part of the target image overlaps between the partial images, the partial images are connected so that corresponding portions of the feature amount groups between the partial images overlap. 3. The image acquisition method according to 2. An imaging unit for acquiring a target image as a plurality of partial images;
A calculation unit that calculates a feature quantity that characterizes the direction of a line in the partial image or a feature quantity that characterizes the partial image as a single physical quantity for each partial image;
The arithmetic unit uses the feature amount of each partial image to construct the target image into a single image, or constructs a feature amount when the target image is constructed into a single image. An image acquisition device. An imaging unit for acquiring a target image as a plurality of partial images;
For each of the partial images, a feature amount that characterizes the direction of a line in the region or a feature amount that characterizes the region as a single physical amount is calculated for each region divided into a plurality of regions along a predetermined direction. A calculation unit that generates a feature amount group along the direction of each partial image,
The calculation unit refers to the correspondence relationship between the feature quantity groups between the partial images, and constructs the target image into one image, or the feature quantity when the target image is constructed into one image. An image acquisition apparatus characterized by constructing   The said calculating part connects the said partial images so that the corresponding part of the feature-value groups between the said partial images may overlap, when the part of the said target image overlaps between the said partial images. The image acquisition device described in 1. Obtaining a target image for verification as a plurality of partial images;
For each partial image, calculating a feature amount that characterizes the direction of a line in the partial image, or a feature amount that characterizes the partial image as a single physical amount; and
Collating the feature quantity of the partial image constituting the target image with the corresponding feature quantity of the partial image constituting the reference image;
A collation method comprising: Obtaining a target image for verification as a plurality of partial images;
Dividing each partial image into a plurality of regions along a predetermined direction;
For each divided area, a feature quantity characterizing the direction of a line in the area or a feature quantity characterizing the area as a single physical quantity is calculated, and a feature quantity group along the predetermined direction is calculated for each partial image. Generating steps,
Collating the feature quantity group of the partial image constituting the target image with the corresponding feature quantity group of the partial image constituting the reference image;
A collation method comprising: An imaging unit that acquires a target image for verification as a plurality of partial images;
For each partial image, a calculation unit that calculates a feature amount that characterizes the direction of a line in the partial image, or a feature amount that characterizes the partial image as a single physical quantity;
A collation unit that collates the feature quantity of the partial image constituting the target image with the corresponding feature quantity of the partial image constituting the reference image;
A collation device comprising: An imaging unit that acquires a target image for verification as a plurality of partial images;
For each of the partial images, a feature amount that characterizes the direction of a line in the region or a feature amount that characterizes the region as a single physical amount is calculated for each region divided into a plurality of regions along a predetermined direction. A calculation unit for generating a feature amount group along the direction of each partial image;
A collation unit for collating the feature amount group of the partial image constituting the target image with the feature amount group of the partial image constituting the reference image;
A collation device comprising:

Images