WO2012138086A2 - Appareil et procédé pour vérifier de manière hiérarchique des empreintes digitales à l'aide d'une distribution de similarité - Google Patents

Appareil et procédé pour vérifier de manière hiérarchique des empreintes digitales à l'aide d'une distribution de similarité Download PDF

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Publication number
WO2012138086A2
WO2012138086A2 PCT/KR2012/002446 KR2012002446W WO2012138086A2 WO 2012138086 A2 WO2012138086 A2 WO 2012138086A2 KR 2012002446 W KR2012002446 W KR 2012002446W WO 2012138086 A2 WO2012138086 A2 WO 2012138086A2
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WIPO (PCT)
Prior art keywords
fingerprint
similarity
image
feature point
unit
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PCT/KR2012/002446
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English (en)
Korean (ko)
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WO2012138086A3 (fr
Inventor
채승훈
반성범
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Chosun University Industry Academic Cooperation Foundation
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Chosun University Industry Academic Cooperation Foundation
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification

Definitions

  • the present invention relates to a hierarchical fingerprint authentication device and method using similarity distribution. More specifically, by using the fingerprint feature point and the binary image information of the fingerprint, two times of fingerprint comparison process enables more accurate authentication, and also determines the similarity for the user fingerprint image and based on the binary image only based on the determination result.
  • the present invention relates to a fingerprint authentication device and a method for enabling real-time processing by performing authentication.
  • biometrics are integrated into computer systems.
  • the biggest feature of these biometrics is that there is no fear of loss, theft, forgetting, or duplication by external factors under any circumstances.
  • the advantage is that it can be built completely.
  • the user authentication technology using a fingerprint has been actively commercialized in recent years, and the user authentication using such a fingerprint has a merit that it is easy to access and carry by authenticating a user by using a unique characteristic of a person.
  • Various studies are underway and many developments are being made.
  • a user's fingerprint is typically registered in a security token such as a smart card or a USB token, and the user is authenticated by comparing the fingerprint with the fingerprint in the security token without leaking the registration fingerprint. If only the result is transmitted to the outside, it is more secure than a system that manages fingerprint data with a central database or a system that transmits registered fingerprint data to perform external fingerprint comparison from an external device. By blocking the can ensure the stability of the biological data that can cause a big problem in the outflow.
  • fingerprint authentication methods are classified into image-based fingerprint authentication methods and feature-based fingerprint authentication methods.
  • Feature-based fingerprint authentication is a universal method that consists of two processes: minutiae extraction and matching.
  • image processing techniques such as smoothing, separation of foreground and background areas, binarization, and thinning Apply the spatial features of the extracted feature points. That is, the feature-based fingerprint authentication method is a method of comparing an authentication fingerprint and a registration fingerprint by using a feature point, and a method of checking similarity between two feature points is mainly used.
  • the image-based fingerprint authentication method uses ridge information.
  • the overall directionality of the fingerprint image is applied by applying techniques such as Gaber filter, Fast Fourier Transform (FFT), slope, directional histogram, and projection. It is one of the classic ways of using information.
  • FFT Fast Fourier Transform
  • the entire fingerprint area is divided into small areas, the direction value of the ridges existing in the small areas is extracted to complete the directional map of the fingerprint, and the two directional maps are convolved to determine the degree of similarity. .
  • the fingerprint authentication method based on binary image is a method using a fingerprint image image expressed in two types of black and white.
  • image processing data can be classified into color, gray, and binary.
  • Color data uses color separation and contrast
  • gray data uses contrast without color separation
  • binary data uses gray image.
  • the data is handled in two ways, black and white, using a threshold.
  • the binary image-based fingerprint authentication method requires a lot of storage space for storing the image because the entire fingerprint image is used, and there is a problem that the performance of the fingerprint authentication process is slow.
  • the present invention was devised to solve the above problems, and by using two fingerprint comparison processes using fingerprint feature points and binary image information of a fingerprint, more accurate authentication is possible, and similarity with respect to a user fingerprint image is also provided. It is an object of the present invention to provide a fingerprint authentication device and a method for performing real-time processing by performing a binary image based authentication only according to the determination result.
  • a fingerprint authentication device comprising: a database unit for storing a registered fingerprint image; A fingerprint image input unit configured to receive a user fingerprint image from a user; A feature point based fingerprint matching unit extracting a feature point from the user fingerprint image inputted through the fingerprint image input unit and the registered fingerprint image stored in the database unit, and performing a feature point based fingerprint matching based on the extracted feature point; A similarity determination unit that determines whether or not the similarity between the user fingerprint image and the registered fingerprint image exists within a set range; Binary image based fingerprint matching unit performing binary image based fingerprint matching; And performing a binary image-based fingerprint authentication when the similarity is within the set range in the result of the similarity determining unit, and performing a feature-based fingerprint authentication when the similarity is out of the set range in the result of the similarity determining unit. It is characterized by.
  • the above-described fingerprint authentication device further comprises a feature point correcting unit for correcting a difference in at least one of a position, a direction, a distance, and an angle for each ridge information of a range of similarity of feature points extracted from the user fingerprint image and the registered fingerprint image. It may include.
  • the binary image-based fingerprint matching unit may perform fingerprint matching based on a binary image by using a fingerprint binary image generated when the feature point is extracted and a correction value of the feature point corrector.
  • a fingerprint authentication method comprising: receiving a user fingerprint image from a user; Extracting a feature point from the input user fingerprint image and a registered fingerprint image stored in a database, and performing feature-point based fingerprint matching based on the extracted feature point; Determining whether a similarity between the user fingerprint image and the registered fingerprint image is within a set range; If it is determined that the similarity is within the set range by the similarity determination step, performing the fingerprint matching based on the binary image using the fingerprint binary image generated when the feature point extraction and the correction value of the feature point correction unit; It features.
  • the fingerprint authentication method may further include performing feature-point based fingerprint authentication when the similarity is determined to be out of the set range by the similarity determining step.
  • the fingerprint authentication method may further include correcting a difference of at least one of a position, a direction, a distance, and an angle with respect to each ridge information of a range of similarity of feature points extracted from the user fingerprint image and the registered fingerprint image. can do.
  • Fingerprint authentication is performed by hierarchical fingerprint authentication.
  • FIG. 1 is a view schematically showing a fingerprint authentication device according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating an example of a fingerprint image, where (a) shows an image of a registered fingerprint and (b) shows an image of an authentication fingerprint.
  • 3 is a diagram showing an example by bin accumulation as an example of feature point correction.
  • FIG. 4 is a diagram illustrating a fingerprint authentication similarity distribution form based on general feature points.
  • FIG. 5 is a diagram illustrating an example of a binarized fingerprint image.
  • FIG. 6 is a flowchart illustrating a fingerprint authentication method using the fingerprint authentication device of FIG. 1.
  • the fingerprint authentication apparatus 100 includes a database unit 110, a fingerprint image input unit 120, a feature point based fingerprint matching unit 140, a similarity determination unit 150, and a binary image based fingerprint matching unit 160. And an authentication performing unit 150.
  • the fingerprint authentication device 100 may further include a feature point corrector 130.
  • the database unit 110 stores the registered fingerprint image. At this time, the database unit 110 may store the feature point information analyzed for the registered fingerprint image.
  • One fingerprint curve representing a fingerprint is called a ridge, which is a continuous ridge, an end that breaks in the middle, and a bifurcation where two or more ridges meet, which are called minutiae. do. Since the feature points vary from person to person, the database unit 110 obtains a registered fingerprint image from a registered subscriber, analyzes the location and number of feature points, and stores the database.
  • the fingerprint image input unit 120 receives a user fingerprint image from a user for authentication.
  • the feature point-based fingerprint matching unit 130 extracts feature points from a fingerprint image of a user for authentication input through the fingerprint image input unit 120 and a registered fingerprint image stored in the database unit 110, and based on the extracted feature points Based fingerprint matching.
  • a bifurcation point 220 is divided into an end point (210) through which the ridge passes in the fingerprint image and one ridge is surrounded.
  • One feature point extracted from the fingerprint image may have a coordinate of the feature point and type information of the feature point and may be represented by (x, y, ⁇ , type).
  • the coordinate value of the feature point is translated and the direction is rotated.
  • FIG. 2A is a fingerprint image input when registering and FIG. 2B is a fingerprint image input when authenticating.
  • the feature point corrector 135 may be configured to display the position, direction, distance, and angle of each feature point extracted from the registered fingerprint image stored in the database unit 110 and the fingerprint image of the user input through the fingerprint image input unit 120. Correct the difference for at least one.
  • the feature point-based fingerprint matching unit 140 extracts feature point information for authentication from the user fingerprint image input through the fingerprint image input unit 120, and then stores the feature point information on the registered fingerprint image pre-stored and registered in the database 110. Read and load the data into a memory to compare and analyze the registered feature point information with the feature point information of the user fingerprint image.
  • the feature point corrector 130 extracts a difference of at least one of a position, a direction, a distance, and an angle between the feature point information of the registered fingerprint loaded in the memory and the feature point information of the authentication fingerprint to correct the feature point.
  • Range_X1 is associated with a detectable positional movement in the X direction to the first area of the X axis
  • Range_X2 is set to the area around the maximum bin at the first stage as the second area of the X axis
  • Range_Y1 Is associated with the detectable positional movement in the Y direction to the first region of the Y axis
  • Range_Y2 is the second region of the Y axis, set as the region around the maximum bin at the first stage.
  • bx1 is the first unit of the X-axis
  • bx2 is the second unit of the X-axis, smaller than bx1
  • by1 is the first unit of the Y-axis
  • by2 is the second unit of the Y-axis, which is smaller than by1.
  • ⁇ X is the amount of change in the X-axis of the pair of feature points
  • ⁇ Y is the amount of change in the Y-axis of the pair of feature points
  • B means a bin having a maximum value.
  • a bin means an area of each feature point expressed in binary.
  • the direction difference between the feature points can be measured. In this case, by checking whether the direction difference between the two feature points is out of the allowable range, if the deviation is not out of the allowable range, the position difference and the distance difference between the two feature points may be measured.
  • the level of the specific bin of the set area corresponding to the obtained position, direction and distance difference is raised or accumulated, and all the feature points, ie, the direction.
  • the bin having the maximum level is obtained, and whether the unit for the position, direction, and distance of the bin is the minimum unit is determined. If the unit is the minimum unit, the difference in the position, direction, and distance corresponding to the maximum bin value, that is, the position, direction, and distance difference corresponding to B shown in FIG. 3B may be extracted.
  • the feature point corrector corrects the feature points of the authentication fingerprint and the enrolled fingerprint based on the detected position, direction, and distance difference, and then measures the similarity between the two corrected feature points, if the measured similarity is greater than the preset threshold. It is determined that this is the same, and the authentication process of the user is performed according to the determined result.
  • the similarity distribution of the feature-based fingerprint authentication generally has a form as shown in FIG.
  • a false match that is, a mistake of another person's fingerprint as a registered user's fingerprint and a false rejection error (False Non) Match
  • the user's fingerprint is misidentified as the fingerprint of another person.
  • point a is called Zero False Non-Match Rate (ZeroFNMR) and point b is called Zero False Match Rate (ZeroFMR).
  • ZeroFNMR Zero False Non-Match Rate
  • ZeroFMR Zero False Match Rate
  • the similarity is increased around the threshold, the other person's acceptance error is reduced, and if the similarity is low, the self rejection error is reduced, and the other person's acceptance error and the person's rejection error are most frequently generated around the threshold.
  • the registration fingerprint and the user fingerprint is matched based on the feature point, when the similarity is between a point and b point, an error of rejecting authentication or misidentifying another person as a person occurs by misidentifying the person as another person.
  • the fingerprint authentication apparatus 100 uses a similarity determination unit 150 and a binary image-based fingerprint matching unit 160 to a fingerprint image based on a feature point and a fingerprint image based on a binary image.
  • the authentication can be performed twice.
  • the similarity determination unit 150 may determine whether the similarity between the feature point of the user fingerprint image and the feature point of the registered fingerprint image is within a set range. If the similarity determined by the similarity determining unit 150 is not included in the set similarity range, the fingerprint authentication may be performed on the input user fingerprint image based on the fingerprint matching by the feature-based fingerprint matching unit 140. have.
  • the fingerprint matching based on the binary image is performed by using the fingerprint binary image generated when the feature point is extracted and the correction value by the feature point corrector 130. You can do that.
  • the binary image-based fingerprint matching unit 160 may perform binary image-based fingerprint matching using the fingerprint binary image generated when the feature point is extracted and the correction value of the feature point corrector 130.
  • the range between point a and point b for performing binary image-based fingerprint matching can be set to 10%, 20%, 30%, ... using a similarity distribution based on a threshold value.
  • FIG. 5 illustrates an example of a fingerprint image input as a gray image and a binary image of a fingerprint.
  • a general gray image has a contrast level of 256 levels.
  • a fingerprint image having a different intensity value is input every time the fingerprint is input.
  • the binarized fingerprint image is composed of consistent ridges and bones. Therefore, when comparing two fingerprints, a fingerprint of a binary image can be clearly and simply compared to a fingerprint of a gray image.
  • binary images have higher compression ratios than gray images because the distribution of contrast values is simple. Therefore, it has the advantage of effectively storing fingerprint images.
  • the fingerprint matching is performed using the binary image generated during the feature point extraction process.
  • the authentication performing unit 170 determines that the similarity between the user fingerprint image and the registered fingerprint image is out of the range set by the similarity determining unit 150 by the feature-based fingerprint matching unit 140. If smaller than the point or larger than the point b, authentication of the user's fingerprint is performed based on the feature point. In this case, as described above, no error occurs in performing authentication.
  • the binary image-based fingerprint matching unit 160 is the fingerprint binary image and the feature point correction unit generated when the feature point extraction when the similarity between the user fingerprint image and the registered fingerprint image is set by the feature-based fingerprint matching unit 140.
  • the binary image-based fingerprint matching 160 is performed using the correction value of 130.
  • FIG. 6 is a flowchart illustrating a fingerprint authentication method using the fingerprint authentication device of FIG. 1.
  • the fingerprint image input unit 120 receives a user fingerprint for authentication from the user (S602).
  • the feature point-based fingerprint matching unit 140 extracts a feature point from a user fingerprint image input through the fingerprint image input unit 120 and a registered fingerprint image stored in the database unit 110 (S604), and the feature point corrector 130 extracts the feature point. Based on the respective feature points, the two fingerprints are corrected (S606). In this case, the feature point corrector 130 corrects a difference of at least one of a position, a direction, a distance, and an angle for each feature point extracted from the user fingerprint image and the registered fingerprint image. The feature point-based fingerprint matching unit 140 performs fingerprint matching on the user fingerprint image and the registered fingerprint image based on the feature points corrected by the feature point corrector 130 (S608).
  • the similarity determination unit 150 determines whether the similarity between the feature point of the user fingerprint image and the feature point of the registered fingerprint image is within a set range (S610). If the similarity is not included in the similarity range set by the similarity determining unit 150, the authentication performing unit 170 is input through the fingerprint image input unit 120 based on the similarity extracted by the feature-based fingerprint matching 140. Fingerprint authentication is performed on the user fingerprint image (S612). In addition, when it is determined by the feature point-based fingerprint matching unit 140 that the similarity between the feature point of the user fingerprint image and the feature point of the registered fingerprint image is within the set range, the fingerprint binary image and the feature point corrector 130 generated when the feature point is extracted The binary image-based fingerprint matching 160 is performed using the correction value. The fingerprint authentication is performed on the user fingerprint image input through the fingerprint image input unit 120 based on the similarity of the binary image-based fingerprint matching (S614).
  • the feature-based similarity is determined with respect to the user fingerprint image and the registered fingerprint image for authentication, and if the feature-based similarity is out of the set range, the feature-based fingerprint authentication is performed, and the feature-based similarity is determined. If it is within the set range by performing the fingerprint authentication based on the binary image, it is possible to significantly reduce the occurrence of errors during fingerprint authentication.
  • the present invention performs binary image-based fingerprint authentication only when the similarity based on the feature point is within the set range, it is not necessary to compare binary images for all input user fingerprint images, thereby real-time processing of fingerprint authentication is required. It becomes possible.
  • all elements constituting the embodiments of the present invention are described as being combined or operating in combination, but the present invention is not necessarily limited to these embodiments.
  • all of the components may be selectively operated in combination with one or more.
  • each or some of the components of the program modules are selectively combined to perform some or all of the functions combined in one or a plurality of hardware It may be implemented as a computer program having a.
  • such a computer program may be stored in a computer readable medium such as a USB memory, a CD disk, a flash memory, and the like, and read and executed by a computer, thereby implementing embodiments of the present invention.
  • the storage medium of the computer program may include a magnetic recording medium, an optical recording medium, a carrier wave medium, and the like.

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Abstract

L'invention porte sur un appareil et un procédé pour vérifier de manière hiérarchique une empreinte digitale à l'aide d'une distribution de similarité. L'appareil de vérification d'empreinte digitale comprend : une unité de base de données pour stocker des image d'empreintes digitales enregistrées ; une unité d'entrée d'image d'empreinte digitale pour recevoir une image d'empreinte digitale d'un utilisateur à partir d'un utilisateur ; une unité de mise en correspondance d'empreintes digitales sur la base de caractéristiques distinctives pour extraire des caractéristiques distinctives de l'image d'empreinte digitale de l'utilisateur reçue par l'intermédiaire de l'unité d'entrée d'image d'empreinte digitale et des images d'empreintes digitales enregistrées stockées dans l'unité de base de données, de façon à mettre en correspondance des empreintes digitales sur la base des caractéristiques distinctives extraites ; une unité de détermination de similarité pour déterminer si la similarité entre l'image d'empreinte digitale de l'utilisateur et les images d'empreintes digitales enregistrées est ou non à l'intérieur d'une plage préréglée ; une unité de mise en correspondance d'empreintes digitales sur la base d'images binaires pour mettre en correspondance des empreintes digitales sur la base d'images binaires ; et une unité de vérification pour vérifier une empreinte digitale sur la base des images binaires si le résultat déterminé par l'unité de détermination de similarité montre que la similarité est à l'intérieur de la plage préréglée, et vérifier une empreinte digitale sur la base des caractéristiques distinctives si le résultat déterminé par l'unité de détermination de similarité est que la similarité n'est pas à l'intérieur de la plage préréglée.
PCT/KR2012/002446 2011-04-06 2012-04-02 Appareil et procédé pour vérifier de manière hiérarchique des empreintes digitales à l'aide d'une distribution de similarité Ceased WO2012138086A2 (fr)

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US20160217310A1 (en) * 2015-01-23 2016-07-28 Samsung Electronics Co., Ltd. System and method for partial fingerprint enrollment and matching using small size fingerprint sensors
CN110383283A (zh) * 2017-12-18 2019-10-25 指纹卡有限公司 用于分类指纹的方法和设备
CN112215049A (zh) * 2019-07-12 2021-01-12 比亚迪股份有限公司 指纹识别方法、装置、存储介质及电子设备
CN115497125A (zh) * 2022-11-17 2022-12-20 上海海栎创科技股份有限公司 指纹识别方法、系统、计算机设备及计算机可读存储介质

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KR102459852B1 (ko) 2017-02-08 2022-10-27 삼성전자주식회사 지문 인식을 위한 후보 지문 이미지를 선정하는 장치 및 방법

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KR100479332B1 (ko) * 2002-12-09 2005-03-31 한국전자통신연구원 계층적 지문 정합 방법
KR100884743B1 (ko) * 2006-12-07 2009-02-19 한국전자통신연구원 지문 특징점 및 지문 이진영상을 이용한 지문 정합 방법 및그 장치
KR100957073B1 (ko) * 2008-11-19 2010-05-13 고려대학교 산학협력단 지문영상의 품질에 기초한 선택적 영상압축장치 및 방법, 그리고 이를 이용한 인증장치

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US20160217310A1 (en) * 2015-01-23 2016-07-28 Samsung Electronics Co., Ltd. System and method for partial fingerprint enrollment and matching using small size fingerprint sensors
US9996728B2 (en) * 2015-01-23 2018-06-12 Samsung Electronics Co., Ltd. System and method for partial fingerprint enrollment and matching using small size fingerprint sensors
CN110383283A (zh) * 2017-12-18 2019-10-25 指纹卡有限公司 用于分类指纹的方法和设备
CN112215049A (zh) * 2019-07-12 2021-01-12 比亚迪股份有限公司 指纹识别方法、装置、存储介质及电子设备
CN115497125A (zh) * 2022-11-17 2022-12-20 上海海栎创科技股份有限公司 指纹识别方法、系统、计算机设备及计算机可读存储介质
CN115497125B (zh) * 2022-11-17 2023-03-10 上海海栎创科技股份有限公司 指纹识别方法、系统、计算机设备及计算机可读存储介质

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