KR20170041593A - Method and apparatus for speed improvement of fingerprint registration and authentification - Google Patents

Abstract

According to an embodiment of the present invention, there is provided a method of generating a fingerprint image, For each pixel of the fingerprint image, an average of pixel density values for a specific area around the pixel is calculated, and the sum of the pixel density values averages included in the extended area is calculated Generating a second processing image for the fingerprint image using a second processing performance value for each pixel after performing a second processing to calculate the second processing performance; And selecting a feature point among pixels in the window region by forming a window including a certain region on the second processing image and moving the window.

Description

[0001] METHOD AND APPARATUS FOR SPEED IMPROVEMENT OF FINGERPRINT REGISTRATION AND AUTHENTIFICATION [0002]

The present invention relates to a method and apparatus for improving fingerprint registration and authentication speed, and more particularly, to a method and apparatus for setting a predetermined area including each pixel constituting a fingerprint image at the center, And a method for selecting feature points on a deformed image.

Since fingerprints vary from person to person, they are widely used in the field of personal identification. In particular, fingerprints are widely used in various fields such as finance, crime investigation and security as personal authentication means.

A fingerprint sensor has been developed to recognize these fingerprints and identify them. Background Art [0002] A fingerprint sensor is a device that contacts a finger of a person and recognizes a fingerprint, and is used as a means of determining whether or not the user is a legitimate user.

Various recognition methods such as an optical method, a thermal sensing method, and a capacitive sensing method are known as a method of implementing a fingerprint recognition sensor. Among them, the capacitive type fingerprint recognition sensor acquires the shape of the fingerprint (fingerprint pattern) by detecting the change of capacitance according to the shape of the fingerprint and the floor when the finger surface of the person touches the conductive detection pattern.

In recent years, various additional functions utilizing personal information such as finance and security have been provided not only in communication functions such as telephone and text message transmission service through portable devices, but also in the necessity of locking devices of portable devices. . In order to improve the locking effect of such a portable device, a terminal equipped with a locking device through fingerprint recognition is being developed in earnest.

1 shows an example in which a fingerprint sensor is mounted on a portable device, for example, a smart phone.

First, referring to FIG. 1 (a), the smartphone 10 has a display unit 11 that simultaneously performs a function of an input unit in a touch screen manner, and the fingerprint sensor 12 is mounted in a lower area thereof. The fingerprint sensor 12 is formed at the lower end of the main body of the smartphone 10 and is implemented with a home key for moving the screen of the display unit 11 to a home.

Next, the smartphone 20 shown in FIG. 1 (b) also has a fingerprint sensor 22 attached to the lower region of the display unit 21 together with a home key. The area occupied by the fingerprint sensor shown in Fig. 1 (b) is formed smaller than the area occupied by the fingerprint sensor shown in Fig. 1 (a).

The fingerprint detection method is roughly classified into a touch method (or an area method) and a swipe method. A touch method is applied to the fingerprint sensor 12 as shown in FIG. 1 (a) A swipe method is applied to the fingerprint sensor 22 as shown in Fig. 1 (b).

In the touch method, when a finger is placed on the fingerprint sensor 12 for a predetermined period of time, the fingerprint image is acquired from the fingerprint sensing area. Meanwhile, in the swipe method, when a finger is moved to the fingerprint sensor 22 in a sliding manner, the fingerprint sensor 22 senses the fingerprint of the finger moving on the fingerprint sensor 22, reads the fingerprint images, And a fingerprint image is acquired.

After the fingerprint image is acquired in this manner, the acquired fingerprint image is compared with the previously registered fingerprint image to determine whether the fingerprint image matches or not, and fingerprint authentication is performed according to the result. It is necessary to extract characteristic points on each fingerprint image and to perform comparison of these points.

Accordingly, various algorithms for extracting characteristic points on the fingerprint image have been developed and improved, and the accuracy of the fingerprint authentication is increasing accordingly.

However, in order to increase the accuracy of the fingerprint authentication, there is a disadvantage that the time required for the fingerprint authentication increases because the amount of calculation to be performed in order to extract the characteristic points on the fingerprint image increases.

Recently, there is an increasing need for an algorithm for performing fingerprint authentication within a limited time using not only accuracy of fingerprint authentication but also limited resources of hardware.

SUMMARY OF THE INVENTION The present invention has been made to solve the above problems of the prior art.

SUMMARY OF THE INVENTION An object of the present invention is to improve the speed of extracting minutiae points on a fingerprint image and to smoothly perform fingerprint registration and authentication even if only a limited hardware resource is used.

Another object of the present invention is to prevent the feature points from being concentrated on a specific region in the fingerprint image and to select the feature points relatively evenly over the entire region of the fingerprint image, thereby improving the accuracy of the fingerprint authentication.

According to an aspect of the present invention, there is provided an image processing method comprising the steps of: obtaining a fingerprint image; For each pixel of the fingerprint image, an average of pixel density values for a specific area around the pixel is calculated, and the sum of the pixel density values averages included in the extended area is calculated Generating a second processing image for the fingerprint image using a second processing performance value for each pixel after performing a second processing to calculate the second processing performance; And forming a window including a certain area on the second processing image and moving the window to select minutiae among the pixels in the window area.

The fingerprint information processing method of the fingerprint detection apparatus may further include performing fingerprint authentication by comparing the selected minutiae points with minutiae points of a previously registered fingerprint.

The step of expanding the specific area stepwise may further include determining whether to extend the specific area through a variation amount of the pixel density value average that changes as the specific area is expanded.

The step of expanding the specific area stepwise may further include stopping the expansion of the specific area when the specific area reaches the predetermined maximum expansion size.

Wherein the fingerprint information processing method of the fingerprint detection device includes generating a summation array through first processing on the fingerprint image after the fingerprint image acquisition step and the step of generating the second processing image comprises: The sum array may be used in calculating the average of the pixel density values included in the specific area.

The feature point selection step may further include limiting a minimum number or a maximum number of feature points selected from the pixels included in the window region.

The feature point selecting step may further include moving the window region so that an overlapping region does not exist on the second processing image.

According to another aspect of the present invention, there is provided a fingerprint sensor comprising: a fingerprint sensor for scanning a fingerprint of a finger to obtain a fingerprint image; And calculating, for each pixel of the fingerprint image, an average of pixel density values for a specific area centered on the pixel, and calculating a sum of pixel density values averages included in the expanded area, After generating a second processing image for the fingerprint image using a second processing execution value for each pixel and then generating a second processing image for the second processing image using a window including a certain region on the second processing image And an information processing device for selecting the feature points among the pixels in the window area while moving the window.

The information processing apparatus can determine whether to extend the specific region through a variation amount of the pixel density value average that changes as the specific region is expanded.

The information processing apparatus may use the sum array in a process of generating a sum array through first processing on the fingerprint image and calculating an average of pixel density values included in the specific region.

In order to accomplish the above object, another embodiment of the present invention is a method of acquiring a fingerprint image, comprising: obtaining a fingerprint image; For each pixel of the fingerprint image, an average of pixel density values for a specific area around the pixel is calculated, and the sum of the pixel density values averages included in the extended area is calculated Generating a second processing image for the fingerprint image using a second processing performance value for each pixel after performing a second processing to calculate the second processing performance; And forming a window including a certain area on the second processing image, moving the window, and selecting feature points among the pixels in the window area.

According to an embodiment of the present invention, a distortion using an average of pixel density values constituting a fingerprint image is applied to a fingerprint image. In this process, by using a pre-generated image-related sum array, The fingerprint authentication algorithm can operate smoothly even in the case where the hardware resources are small.

According to an embodiment of the present invention, when selecting a feature point on a fingerprint image to which distortion is applied, by limiting the maximum number of feature points that can be selected in a window including a certain region, So that the accuracy of fingerprint authentication can be increased.

It should be understood that the effects of the present invention are not limited to the above effects and include all effects that can be deduced from the detailed description of the present invention or the configuration of the invention described in the claims.

1 shows an example in which a fingerprint sensor is mounted on a portable device, for example, a smart phone.
2 is a diagram for explaining a process of acquiring a fingerprint image according to an embodiment of the present invention.
3 is a diagram showing an example of a fingerprint image acquired by an electronic device.
4 is a block diagram briefly dividing a system environment in which an electronic device operates.
FIG. 5 is a diagram for explaining a method for finding feature points in a fingerprint image in which a typical negative is not present according to an embodiment of the present invention.
6 is a diagram for explaining a method of performing first processing on a fingerprint image in a minutiae point extraction process according to an embodiment of the present invention.
7 is a diagram illustrating a method of performing second processing on a fingerprint image in a feature point extraction process according to an embodiment of the present invention.
8 is a diagram for explaining a method of selecting feature points for performing fingerprint recognition on the second processing image according to an embodiment.
9 is a flowchart illustrating a fingerprint authentication process according to an embodiment of the present invention.
10 is a flowchart illustrating a fingerprint authentication method according to an embodiment of the present invention.
11 is a diagram showing the configuration of an electronic device including a fingerprint detection device according to an embodiment of the present invention.
12 is a view showing feature points extracted according to embodiments of the present invention on a fingerprint image.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "indirectly connected" . Also, when an element is referred to as "comprising ", it means that it can include other elements, not excluding other elements unless specifically stated otherwise.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

As described above, the touch-type fingerprint detection method is performed in such a manner that when the user places a finger on the fingerprint sensor, the fingerprint sensor acquires the fingerprint image of the corresponding region.

On the other hand, in the swipe method, a fingerprint image is obtained on the principle as shown in FIG.

2, when the user moves his / her finger on the fingerprint sensor 110 provided in the lower end region of the electronic device 100 in a sliding manner, partial partial images P1 to P4 are successively obtained .

That is, the fingerprint image of the user is obtained sequentially in a sequential manner although they are fragmentary. The short fingerprint images P1 to P4 read from the fingerprint sensor 110 are matched to one fingerprint image to obtain a complete fingerprint image.

FIG. 3 is a diagram showing an example of a fingerprint image obtained by the electronic device 100. FIG.

Referring to FIG. 3, the fingerprint image is composed of ridges and valleys of the fingerprint. In order to recognize the fingerprint image, a fingerprint image is displayed on the fingerprint image using a singularity such as bifurcation, ridge end, Various pattern matching methods including a minutiae method for comparing a previously registered fingerprint image with a new fingerprint image are used. In addition, a method of searching for a feature point in a modified image by generating a deformation, such as filtering, on the acquired fingerprint image is also used. Various known algorithms for performing object recognition on an image are used to search feature points in a fingerprint image. Since a large amount of calculation is required in an operation process for implementing such an algorithm, A large portion of the performance of the hardware resources of the electronic device 100 must be utilized and if the performance of the electronic device 100 does not reach a certain level or more, .

4 is a block diagram schematically showing a system environment in which the electronic device 100 operates.

The operating environment of a system implemented in various hardware included in the electronic device 100 can be divided into a REE (Rich Execution Environment) area and a TEE (Trusted Execution Environment) area.

The REE area is an environment in which all the functions of the hardware included in the electronic device 100 can be used and is an environment in which a program such as an operating system (OS) operating in the electronic device and various applications operating in the electronic device operates Lt; / RTI >

The TEE area is an environment in which only a limited part of the hardware resources included in the electronic device 100 can be used, and according to one embodiment, only trusted operating systems and other programs can operate in the TEE area. More specifically, in the TEE area, only a limited portion of the entire resources is used. Programs operating in the TEE area can use only a part of the CPU functions of the electronic device 100, thereby performing calculation over a certain load The computation time can be greatly increased. In addition, programs operating in the TEE area can use only a limited amount of memory, and programs requiring data of a certain size or more can not operate in the TEE area.

The fingerprint recognition algorithm to be disclosed in the present invention can be designed to operate in the TEE domain and thus can only use limited hardware resources, so that it is required to be designed so as to reduce the calculation required for operation as much as possible.

FIG. 5 is a diagram for explaining a method for finding feature points in a fingerprint image in which a typical negative is not present according to an embodiment of the present invention.

Referring to FIG. 5, a ridge is simply arranged in a specific direction in a fingerprint image, so that a Minutiae such as a branch point or an end point may not exist. However, if we enlarge it, we can confirm that the ridges have a characteristic shape, not a uniform thickness. For example, a specific point of a ridge may protrude outward, a specific point of a ridge may be recessed inward, and a hole may be formed in the middle of the ridge by a pore.

Such features on the fingerprint image can be detected by detecting the difference between the change value of the tint in the fingerprint image and the average change value. That is, in FIG. 4, it can be confirmed whether or not the characteristic point exists based on how rapidly the density change value in the region in which the features appear or in the vicinity thereof is changed compared to the average change value.

For example, the value of the change in density in the image of ridges arranged in a uniform thickness does not deviate from a certain range based on the average change value. However, when there is a protruding point or depression at the ridge, or when the pore is formed, The change value of the density change becomes large.

In the present invention, the feature point of the fingerprint refers to a point detected based on the change value of the tint in the region where the features appear or in the vicinity thereof.

6 is a diagram for explaining a method of performing first processing on a fingerprint image in a minutiae point extraction process according to an embodiment of the present invention.

Specifically, referring to FIG. 6, the density value of each pixel constituting the fingerprint image may be temporarily stored in the memory in the form of a matrix of NxM type. The fingerprint image of the original form temporarily stored in this way can be deleted from the memory after the feature point extraction is completed. According to one embodiment, the sensor array of the fingerprint detection device included in the electronic device 100 may be composed of fingerprint sensor elements arranged in the form of an NxM matrix, and each fingerprint sensor element Can be determined.

According to an embodiment, the density value position of each pixel temporarily stored in a matrix form on the memory can be configured to be the same as the position of each pixel constituting the fingerprint image.

In FIG. 6, a matrix in which a density value of an 8x8 size fingerprint image is stored is shown as an example, but the scope of the present invention is not limited thereto.

The electronic device 100 of the present invention can perform the first processing in the feature point candidate detection process for fingerprint recognition. When the first processing for a specific fingerprint image is performed, a numeric array corresponding to the corresponding fingerprint image is generated.

Such a numerical arrangement may be formed by the same number as the number of pixels constituting the fingerprint image, and is generated according to a rule determined by summing the density values of the pixels included in a certain area constituting the fingerprint image. Hereinafter, for convenience of explanation, the numerical array generated by performing the first processing on the specific fingerprint image will be referred to as a " summation array ". The sum array for a fingerprint image in the NxM matrix format can be generated in the same NxM matrix format.

As an example of the process of generating the sum array configured in the matrix format, the value of 5 rows and 3 columns of the sum array can be calculated by adding all the values from 1 row 1 column to 5 column 3 column in FIG. According to this method, the sum of all the values in the matrix shown in FIG. 6 becomes a value of 8 rows and 8 columns on the sum array generated through this.

In the method in which the electronic device 100 generates a sum array corresponding to the fingerprint image through the data in the form of a matrix storing the density values of the fingerprint image pixels, the values of the previously calculated sum array can be used. For example, in the specific sum array generated through the matrix of FIG. 6, the values of the third row and the sixth column are obtained by subtracting the values of the second row and the fifth column from the values obtained by summing the values of the third row and fifth column and the values of the second row and sixth column, By summing the values in the third row and sixth column in Fig. According to this method, it is possible to calculate the values constituting the summation array without performing a large amount of addition operation as described above.

The electronic device 100 disclosed in the present invention can quickly process the summation of the density value for a specific region of the fingerprint image pixels shown in FIG. 6 in a short time using the summation array generated through the above method .

For example, assuming that the total sum of the density values included in the D area is calculated in the matrix format data of FIG. 6, the sum of the density values from the sum of the density values up to the point d to the point b, If the sum of the values is subtracted and the density value? D up to the point a is summed, the sum of the density values contained in the D area can be calculated. That is, if the value of 6 rows and 6 columns and the value of 2 rows and 6 columns are subtracted from the values of 6 rows and 6 columns of the sum array generated through FIG. 6 and the values of 2 rows and 2 columns are added to the values, It is possible to obtain the total sum of the included pixel density values. As described above, by using the sum array generated in advance for a specific image, the sum of the density values of a specific area on the fingerprint image can be calculated by only two subtractions and one addition. As described above, since the sum array is used for addition and subtraction without complicated calculation such as multiplication or division, the speed of the fingerprint recognition can be improved without waste of hardware resources.

7 is a diagram illustrating a method of performing second processing on a fingerprint image in a feature point extraction process according to an embodiment of the present invention.

As described above, the density value of each pixel constituting the fingerprint image acquired by the electronic device 100 can be temporarily stored in a matrix form of NxM type.

When the second processing is performed on the obtained fingerprint image, the values after the second processing is performed on each pixel may be calculated and stored as matrix format data of the same size as the fingerprint image.

A method of performing second processing with respect to a density value of each pixel is as follows. First, a density value average for a specific peripheral area of one pixel is calculated, a grayscale value average is calculated while gradually expanding a specific area, The second processing application value of the corresponding pixel can be calculated using the final value obtained by summing the same average values.

7, a specific pixel includes an A region 210 including a corresponding pixel, a B region 220 including an A region 210, a B region 220 including an A region 210, , And a B region 220, as shown in FIG. As the expansion from the A region 210 to the B region 220 and from the B region 220 to the C region 230 is performed, the number of pixels existing in each region increases.

According to one embodiment, the average value of the density values of the pixels included in the A area 210, the average value of the density values of the pixels included in the B area 220, and the average value of the C area 230 ) May be summed to calculate a second processing performance value for the pixel.

As described above, in calculating the average value of the density values of the pixels included in each region in the second processing, a pre-generated summation array through the first processing can be utilized. As described with reference to FIG. 6, when the sum array is used in the process of calculating the sum of the pixel density values existing in a specific area, a desired value can be calculated quickly using only a few hardware resources by merely adding and subtracting .

If the pixels constituting the fingerprint image are positioned at the edges of the fingerprint image, the pixels may not exist in a certain region within the region formed around the pixel. In this case, the average density value of each area can be calculated in such a manner that the average of the density values is calculated in consideration of only the number of pixels existing in the area.

A process for calculating the second processing performance value for a specific pixel on the fingerprint image according to another embodiment may be performed by applying different weights to the average value of each area shown in FIG. For example, a weighting factor of 5 times is applied to the A region 210, a weighting factor of 3 times is applied to the average value of the B region 220 which is extended one step higher than the weighting factor, and a small weight is applied to the average value of the C region 230 . The reason why a larger weight is added to the average value of the inner area is that the average value of the density value may be prevented from being excessively different from the pixel density value located at the center as the area for calculating the average value is enlarged . When the weights are added as described above, weights of the A, B, and C regions 210 to 230 may be divided by the weights of different weights. For example, when the A region 210 is weighted five times and the B region 220 is weighted three times, the average value of the density values of each region is multiplied by a weight, , And dividing the total area by 8, which is the sum of the weights applied to the entire area, to the second processing performance value of the specific pixel. For example, the method of calculating the second processing performance value for each area is shown in FIG. 7 as follows.

In Equation (1), PC represents a second processing performance value for a specific pixel, and Mean (A), Mean (B) and Mean (C) Represents an average. In addition, w1, w2, and w3 represent weights applied to the respective regions. According to the embodiment, as described above, the weights for the regions located inside are set to be high, so that a relationship such as w1> w2> . At this time, w1, w2, w3 may be an integer value of 0 or more. The second processing performance value calculated in this manner can be used for the minutiae point calculation by comparison with a predetermined threshold value in the future.

Since the fingerprint form of a person is composed of an elliptical shape having a small eccentricity, as in the embodiment of the present invention, the concentric circle formed around a specific pixel to be subjected to the second processing performance calculation is expanded, The average density value of existing pixels can be calculated and used. As a result, the second processing performance value calculated for each pixel may appear in a more appropriate form for fingerprint analysis.

In addition, in the present invention, the shape of the area arbitrarily determined in the second processing performance value calculation process for the density value of each pixel is not limited to the above-described concentric shape but can be implemented in various forms such as a round shape, have.

According to an embodiment of the present invention, in calculating a second processing calculation value for a density value of a specific pixel, an average value of a predetermined peripheral region of the pixel is calculated, and in the step of expanding a peripheral predetermined region, Can be determined based on the average value calculation result of the previous areas.

7, an average of the pixel density values included in the A and B regions 210 and 220 is preferentially calculated, and the calculation of the second processing performance value Region 230 may be compared to determine if the average value of the C region 230 should be calculated.

According to an embodiment, when the average value of the specific region is equal to or greater than the average value of the previous region, or when the decrease value when compared with the average value of the previous region is equal to or less than the predetermined reference value, the average value for the next region can be calculated by further expanding the region. Alternatively, when the average value of the specific region is compared with the average value of the previous region, if the decrease value is not less than the predetermined reference value, the average value is not calculated by expanding the region, Value of the second processing execution value.

7, the average value of the density values of the pixels included in the A area 210 and the average value of the density values of the pixels included in the B area 220 are compared with each other, If the average value of the density values of the pixels included in the B area 220 is smaller or smaller than the predetermined reference value, the average value of the density values of the pixels included in the C area 230 extended from the B area 220 And the calculated value can be used for calculating the second processing performance value. Thereafter, the average value of the density values of the pixels included in the B area 220 is compared with the average value of the density values of the pixels included in the C area 230, and the D area (not shown) It is possible to determine whether to calculate the grayscale value average of the pixels.

In the above-described manner, in a process in which a region centered at a specific pixel to be subjected to the second processing performance calculation step is expanded stepwise and an average of pixel density values included in each region is calculated, It can be determined whether or not the area centered at a specific pixel is further expanded according to the comparison result of the average of the pixel density values and the pixel density value average of the immediately preceding area.

As described above, whether or not the specific region is further expanded is determined through the variation amount of the pixel density value average included in the specific region expanded stepwise on the fingerprint image. According to one embodiment, the maximum size of the specific region is determined, When the specific region reaches the maximum expansion size, expansion of the specific region can be stopped irrespective of the variation of the pixel density value average.

Looking at the image of the average person's fingerprints, ridges and ridges appear in an elliptical shape. According to an embodiment, if the area in which the average of the pixel density values is calculated is expanded and a plurality of pixels corresponding to the goal are included, the average value of the corresponding area is reduced compared to the immediately preceding area, The second processing performance value is calculated through an average of the pixel density values calculated to the completed area or the area immediately before the second processing performance value. In this way, a second processing performance value suitable for analysis of the human fingerprint type can be calculated .

In the present invention, the above-described methods are applied to the respective pixels constituting the fingerprint image to calculate the second processing performance value, and thereby, a matrix having the same size as the matrix format data storing each pixel density value of the fingerprint image The format data can be calculated and stored on the memory of the electronic device 100. [ Hereinafter, a distorted image formed through the set of second processing performance values calculated for one fingerprint image will be referred to as a " second processing image ".

8 is a diagram for explaining a method of selecting feature points for performing fingerprint recognition on the second processing image according to an embodiment.

Referring to FIG. 8, a window of a predetermined size including a plurality of pixels is set on the second processing image, and a density value of pixels existing in the window area is analyzed, and feature points in the corresponding area can be selected.

According to an exemplary embodiment of the present invention, when the density value of each pixel in the second processing image is compared with the density value of neighboring pixels and a difference of more than a certain value is obtained, Can be selected. In this process, the number of feature points selected in an area within one window can be limited. When a larger number of pixels than the limited number of pixels meet the feature point selection criterion, only a limited number of pixels collide with each other, . That is, in a region within one window, only a limited number of pixels or less can be selected as the minutiae. Such a contention process can be performed by scoring each pixel located in the window region, and the selection is performed in the order of higher scores, so that only a limited number of pixels can be selected as the minutiae.

According to another embodiment, the minimum number of pixels to be selected as feature points, along with the maximum number of pixels that can be selected as feature points within a window region, can also be determined. At this time, the maximum number and the minimum number of pixels may be variously set according to the security level of the fingerprint recognition or the characteristic of the fingerprint recognition device. As described above, when only a fewer number of pixels than the minimum number of pixels to be selected as the minutiae in one window region satisfy the minutia point selection criterion, the minutiae can be selected among the pixels that do not meet the minutiae point selection criteria. Such a process can be performed by scoring each pixel in the window region as described above, and selecting a pixel having a small number of pixels as feature points in descending order of the score.

According to one embodiment, the positional relationship of each feature point can be limited even within one window area. For example, when the positions of the pixels selected as the minutiae are concentrated among the pixels existing in one window region, it may be difficult to perform the fingerprint authentication later. Therefore, the pixels selected as the minutiae in one window region The minimum separation distance between the two can be set.

Hereinafter, a method of moving a window that is a reference region of minutia point setting within one second processing image will be described. Hereinafter, the movement of the window on the second processing image will be referred to as " shift ".

According to one embodiment, the manner in which the window is shifted may be shifted so that there is no overlapping area with the previous window area, as in Figure 8 (a). In this way, in order to select feature points for the entire area of the second processing image, the entire area of the image is divided into a number of non-overlapping window areas, and feature points can be selected for each window.

According to another embodiment, the window can be shifted with a predetermined rule in the first predetermined window area, and the window can be shifted until the entire area of the second processing image is included in the window area shifted at least once. Referring to (b) of FIG. 8, when a shift of a window region is performed, there may exist overlapping regions in each window. 8B, when the selection of the minutiae among the pixels existing in one window area is completed, the window is shifted and the minutiae among the pixels existing in the shifted window area are selected. In this case, There may be selected minutiae in the window region before the shift is performed in the performed window region. In this case, the shifted window region can be divided into an area where the selection of the minutiae is completed and an area where the selection of the minutiae is incomplete. In the area where the minutiae are selected, the minutiae are not additionally selected, Feature points can be additionally selected only in the area. In this case as well, the maximum number of feature points that can be selected in one window can be limited as described above.

The electronic device 100 disclosed in the present invention stores feature points selected through the above process in a fingerprint image acquired through a fingerprint detection device and compares the selected feature points with newly selected feature points in a fingerprint image It is possible to judge whether the fingerprint images match or not.

According to one embodiment, after the feature points are selected on the fingerprint image and information about the feature points is stored in the memory, the original fingerprint image temporarily stored on the memory, the sum array generated through the first processing, Can be deleted.

The feature points selected through the above-described process in the second processing image can be further selected through an additional algorithm. In this case, the feature points selected through the above-described process can be used as the feature point candidates.

9 is a flowchart illustrating a fingerprint authentication process according to an embodiment of the present invention.

First, when a finger touches the fingerprint sensor for fingerprint authentication, the fingerprint sensor senses the touch of the finger and starts acquiring the fingerprint image (S910). A fingerprint image can be obtained in the same manner as described with reference to FIG.

When the fingerprint image is acquired, the first processing is performed on the fingerprint image to generate a sum array for the fingerprint image (S920). The sum of each pixel value of the corresponding fingerprint image is calculated in the same manner as described with reference to FIG. 6 to generate a sum array having the same size as the data size of the matrix format in which the pixels constituting the fingerprint image are stored.

Thereafter, a second processing is performed on the fingerprint image to generate a second processing image for the obtained new fingerprint image (S930). 7, a second processing execution value for each pixel is obtained by summing up the average density values of the calculated areas by enlarging a predetermined area around each pixel step by step And the second processing image can be generated by performing the same process for all the pixels constituting the fingerprint image. In this process, the sum array generated in step S920 can be used, and the calculation speed can be significantly improved as compared with the case where the sum array is not used.

In operation S940, a feature point for the entire second processing image is selected in such a manner that the second processing image for the acquired new image is generated, and then the window is shifted on the second processing image and the feature points are selected in each window. That is, in the same manner as described with reference to FIG. 8, the feature points for the second processing image are selected, and the maximum number of feature points to be selected in each window region shifted in this process can be limited. After the entire pixels in the second processing image are included in the window shifted at least once, the feature point selection process can be terminated.

The degree of similarity between the previously registered fingerprint and the newly recognized fingerprint is calculated through mutual comparison of the minutiae points of the previously registered fingerprint image and the minutiae selected through the process in the newly obtained fingerprint image, And the fingerprint authentication is performed (S950). An operation that the user intends to perform through the fingerprint authentication can be performed in the electronic device 100 according to the fingerprint authentication.

According to the embodiment of the present invention, an algorithm that requires a somewhat higher performance of hardware is not used, and in the second processing image acquired through the second processing performed using only the average value of the density values of the pixels included in the specific region In addition, in the process of applying the second processing to the acquired fingerprint image, since the summing array generated by the first processing is utilized, the calculation process is minimized. Therefore, in the TEE area of the electronic device 100, Even in this operation, the fingerprint authentication process can be performed quickly. In addition, the selected feature point information is stored and utilized in the TEE area. Since the fingerprint image acquired through the fingerprint sensor for the first time and temporarily stored is deleted from the memory after the completion of the first processing or the second processing, Authentication can be performed quickly.

10 is a flowchart illustrating a fingerprint authentication method according to an embodiment of the present invention.

First, when a finger touches the fingerprint sensor for fingerprint authentication, the fingerprint sensor senses the touch of the finger and starts acquiring the fingerprint image (S1010). A fingerprint image can be obtained in the same manner as described with reference to FIG.

When the fingerprint image is obtained, the first and second processing for the fingerprint image are performed, and the feature points are extracted based on the grayscale values of the second processing image generated through the second processing (S1020).

The information of one or more minutiae points may be stored as position information between the temples. The positional information between the feature points, that is, the geographical position is compared with the geographical position of the feature points of the previously registered fingerprints to determine which part of the fingerprint image the feature points correspond to (S1030). That is, a process of searching for a portion to be compared between minutiae points of a newly obtained fingerprint and minutiae points of a previously registered fingerprint is performed. In the fingerprint sensing, the fingerprint image may be flexibly reduced or increased depending on the case, so that it is possible to perform the operation of reducing or increasing the distance to other feature points radially around one feature point.

As a result of the execution of step S1030, if the comparison target position between the minutiae points of the newly obtained fingerprint and the minutiae of the previously registered fingerprint is selected, the degree of similarity between the second processing performance values of each minutiae is calculated (S1040). The degree of similarity means the degree of similarity between the second processing performance value of the first feature point and the second processing performance value of the second feature point of the corresponding new recognition fingerprint among the minutiae of the previously registered fingerprint, Can be defined as the absolute value of the value difference.

When the degree of similarity of the second processing performance values between the minutiae points is calculated, it is determined whether the similarity degree is equal to or greater than a predetermined threshold value (S1050). If the similarity is equal to or greater than the threshold value, it is determined that the fingerprint authentication is successful (S1060). Otherwise, it is determined that the fingerprint authentication has failed (S1070).

11 is a diagram showing a configuration of an electronic device 100 including a fingerprint detection device according to an embodiment of the present invention.

Referring to Fig. 11, an electronic device 100 according to an embodiment includes a fingerprint sensor 110 and an information processing device 120. Fig.

The electronic device 100 according to an exemplary embodiment is a digital device that performs a predetermined operation according to a user input and includes memory means such as a personal computer, a workstation, a PDA, a web pad, a mobile phone, a navigation device, Any number of digital devices equipped with computing ability can be adopted as the electronic device 100 according to the present invention.

The fingerprint sensor 110 is formed in a part of the area of the electronic device 100. For example, the fingerprint sensor 110 may be located on the front surface of the electronic device 100, but may be formed on the side surface, the back surface, or the surface of the display portion 125 of the electronic device 100 according to various embodiments to be.

The fingerprint sensor 110 according to one embodiment includes a fingerprint sensing unit 111, a signal processing unit 112, and a signal transmission / reception unit 113.

The fingerprint sensing unit 111 is a part that acquires a fingerprint image by sensing the touch of the finger and scanning the fingerprint of the finger being touched. The fingerprint sensing unit 111 may scan the fingerprint using various known methods such as a capacitive method, an optical method, a pressure method, a heat sensing method, and the like. According to the embodiment, the fingerprint sensing unit 111 may perform fingerprint sensing by using a combination of a swipe method and a touch method. For example, at the time of fingerprint registration, the fingerprint image is acquired through the swipe method, and the feature points of the fingerprint are extracted. In fingerprint authentication, the fingerprint image can be acquired through the touch method, , Or vice versa.

The signal processing unit 112 processes the fingerprint image frame scanned at a predetermined period (speed) by the fingerprint sensing unit 111. For example, an analog circuit for converting a fingerprint image into an electrical signal, a noise elimination circuit, a signal sensitivity amplification circuit, an analog-digital signal conversion circuit, a digital circuit, and the like. The signal processing unit 112 may be implemented separately or integrally with the fingerprint sensing unit 111 in the form of an ASIC.

The signal transmitting and receiving unit 113 transmits an electrical signal to the fingerprint image, that is, an output signal from the signal processing unit 112, to the information processing apparatus 120 and transmits a signal (for example, Signals, control signals, data signals for registered fingerprints, etc.). The signal transmitting / receiving unit 113 can use an I2C system or an SPI system interface.

The information processing apparatus 120 includes a control unit 121, a memory unit 122, a fingerprint registration unit 123, a fingerprint authentication unit 124, and a display unit 125. [ The components of the electronic device 100 other than the fingerprint sensor 110 are shown as the information processing device 120. However, the configuration of the information processing device 120 is not limited to the illustrated components, Various components such as audio and touch detection units may be added according to the examples.

The control unit 121 controls the overall operation of the fingerprint sensor 110 and the information processing apparatus 120.

The memory unit 122 temporarily or permanently stores feature point information of the fingerprint in the form of a template. Also, the memory unit 122 stores data, firmware information, and the like of the electronic device 100. The memory unit 122 may be implemented as a volatile memory such as an S-RAM or a D-RAM, or a non-volatile memory such as a ROM or a flash memory. The memory unit 122 may temporarily store a fingerprint image in the original form, a summation array generated through the first processing, and a second processing image generated through the second processing.

The fingerprint registering unit 123 extracts the minutiae points in the fingerprint image of the finger through the process described with reference to FIGS. 6 to 8, and registers it as fingerprint information. The registered fingerprint information is stored in the memory unit 122.

The fingerprint authentication unit 124 performs fingerprint authentication by comparing the fingerprint information stored in the memory unit 122 with the feature information of the currently obtained fingerprint image.

Although the fingerprint registration unit 123 and the fingerprint authentication unit 124 are shown as separate components in FIG. 10, they may be integrated as a single module.

The fingerprint registration unit 123 and the fingerprint authentication unit 124 may be stored in a form of a program module in an algorithm form in a specific area of the control unit 121 or the memory unit 122. [ They are preferably encrypted so that they can be encrypted with a high security level so that access to another route, modification or export of fingerprint information can not be allowed.

The display unit 125 may display the operating status of the electronic device 100 or other information. The display unit 125 according to one embodiment may display an operation state of the fingerprint registration unit 123 and the fingerprint authentication unit 124 (e.g., information on registration success, authentication success or the like).

12 is a view showing feature points extracted according to embodiments of the present invention on a fingerprint image.

As shown in Fig. 12, a pixel point having a specific difference in magenta difference value with surrounding pixels in a fingerprint image in which a general minuscha (for example, a ridge end or a branch point) does not exist, i.e., It can be extracted in a sufficient number to be authenticated. In Fig. 12, these minutiae are represented by circles.

According to the embodiment, it is possible to extract the characteristic features of the fingerprint by performing the second processing for each image even if the fingerprint image has no miniseries which is a general fingerprint feature. Therefore, even when a sufficient number of miniseries do not exist on the obtained fingerprint image area, it is possible to extract unique information of the fingerprint and to authenticate the fingerprint through the extracted fingerprint image.

As described above, according to the embodiments of the present invention, the electronic device 100 performs a fingerprint registration and authentication by using a simpler algorithm method than that used in known fingerprint authentication algorithms, The fingerprint registration and authentication can be completed within the time limit requested by the electronic device 100 even if the fingerprint recognition algorithm operates in the TEE area where only limited hardware resources can be used.

The embodiments of the present invention described above can be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination. The program instructions recorded on the computer-readable recording medium may be those specially designed and constructed for the present invention or may be those known and used by those skilled in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules for performing the processing according to the present invention, and vice versa.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included within the scope of the present invention.

100: Electronic device
110: fingerprint sensor
111: fingerprint sensing unit
112: Signal processor
113: Signal transmission /
120: Information processing device
121:
122:
123: Fingerprint register
124: Fingerprint authentication unit
125:
210: area A
220: area B
230: C region

Claims (11)

Obtaining a fingerprint image;
For each pixel of the fingerprint image, an average of pixel density values for a specific area around the pixel is calculated, and the sum of the pixel density values averages included in the extended area is calculated Generating a second processing image for the fingerprint image using a second processing performance value for each pixel after performing a second processing to calculate the second processing performance; And
Selecting a feature point among pixels in the window area by forming a window including a certain area on the second processing image and moving the window.
The method according to claim 1,
And performing fingerprint authentication by comparing the selected minutiae points with minutiae points of previously registered fingerprints.
The method according to claim 1,
The step of expanding the specific region stepwise comprises:
Further comprising the step of determining whether to extend the specific area through a variation of a pixel density value average that changes as the specific area is expanded.
The method according to claim 1,
The step of expanding the specific region stepwise comprises:
Further comprising stopping the expansion of the specific area when the specific area reaches a predetermined maximum expansion size.
The method according to claim 1,
After the fingerprint image acquisition step,
Generating a sum array through a first processing on the fingerprint image,
Wherein the generating the second processing image comprises:
Wherein the sum array is used in calculating an average of pixel density values included in the specific area.
The method according to claim 1,
The feature point selection step may include:
Further comprising the step of limiting a minimum number or a maximum number of feature points selected from the pixels included in the window region.
The method according to claim 1,
The feature point selection step may include:
Further comprising moving the window region such that no overlapping region exists on the second processing image. ≪ Desc / Clms Page number 19 >
A fingerprint sensor that scans a fingerprint of the finger to acquire a fingerprint image; And
For each pixel of the fingerprint image, an average of pixel density values for a specific area around the pixel is calculated, and the sum of the pixel density values averages included in the extended area is calculated After generating a second processing image for the fingerprint image using a second processing performance value for each pixel,
And an information processing device for forming a window including a certain area on the second processing image and moving the window and selecting the minutiae among the pixels in the window area.
9. The method of claim 8,
The information processing apparatus includes:
And determines whether to further expand the specific region through a variation of a pixel density value average that changes as the specific region is expanded.
9. The method of claim 8,
The information processing apparatus includes:
Generating a summation array through first processing on the fingerprint image and using the summation array in calculating an average of pixel density values included in the specific region.
Obtaining a fingerprint image;
For each pixel of the fingerprint image, an average of pixel density values for a specific area around the pixel is calculated, and the sum of the pixel density values averages included in the extended area is calculated Generating a second processing image for the fingerprint image using a second processing performance value for each pixel after performing a second processing to calculate the second processing performance; And
A step of forming a window including a certain area on the second processing image and moving the window to select minutiae among the pixels in the window area.

Images