Method, device, equipment and medium for deleting fingerprint feature points

Abstract

The present application discloses a method, device, equipment and medium for deleting fingerprint fracture feature points. The method comprises: acquiring a fingerprint image; processing the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include the breakpoints and / or bifurcation points of the fingerprint; traversing the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the fracture of the fingerprint; if so, deleting the feature points corresponding to the fracture of the fingerprint. The method solves the problem that when deleting a fingerprint fracture, the accuracy of the determined fingerprint fracture position is low, resulting in the mistaken deletion of normal fingerprint information.

Description

Technical Field

[0001] The present application generally relates to the field of fingerprint recognition technology, and specifically relates to a method, device, equipment and medium for deleting fingerprint fracture feature points. Background Art

[0002] Fingerprint recognition is widely used in social life where identity authentication is required. Human fingerprints usually contain broken lines. During the fingerprint recognition process, the feature points at the broken lines of different people's fingers show similar distribution patterns, which can easily lead to errors in fingerprint recognition results. Therefore, during the fingerprint recognition process, it is usually necessary to delete the feature points corresponding to the broken lines in the fingerprint.

[0003] In the related art, the grayscale difference method is usually used to mark the fracture area in the fingerprint, and then the feature points in the area are deleted. However, since the grayscale value difference between the finger fracture and the valley line is very small, in the actual processing process, the grayscale threshold for marking the fracture is required to be very accurate. It is often the case that the valley line is marked as a fracture, resulting in the normal fingerprint information in the fingerprint being deleted, and the fracture in the fingerprint cannot be accurately deleted. Summary of the invention

[0004] In view of the above defects or deficiencies in the prior art, it is desirable to provide a method, device, equipment and storage medium for deleting fingerprint fracture feature points that can accurately determine and delete feature points corresponding to the fingerprint fracture.

[0005] In a first aspect, the present application provides a method for deleting fingerprint feature points, comprising:

[0006] Acquire fingerprint image;

[0007] Processing the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include breakpoints and / or bifurcation points of the fingerprint;

[0008] Traversing the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the breaklines of the fingerprint;

[0009] If included, delete the feature points corresponding to the fingerprint fracture;

[0010] In a second aspect, the present application provides a device for deleting fingerprint feature points, comprising:

[0011] An acquisition module, configured to acquire a fingerprint image;

[0012] A processing module is configured to process the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include the breakpoints and / or bifurcation points of the fingerprint;

[0013] A determination module is configured to traverse the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the breaklines of the fingerprint;

[0014] a deletion module configured to delete, if included, feature points corresponding to the fracture pattern of the fingerprint;

[0015] In a third aspect, the present application provides a computer device, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor being configured to implement the method of the first aspect when executing the program;

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to implement the method of the first aspect.

[0017] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0018] The method, device, equipment and medium for deleting fingerprint fracture feature points provided in the embodiments of the present application can obtain a fingerprint image; process the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include the breakpoints and / or bifurcation points of the fingerprint; traverse the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the fracture of the fingerprint; if so, delete the feature points corresponding to the fracture of the fingerprint. The accuracy of fingerprint fracture recognition can be improved, and other valid feature information can be prevented from being deleted when the fingerprint fracture is deleted, which is conducive to improving the accuracy and reliability of fingerprint recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0020] Figure 1 A flow chart of a method for deleting fingerprint feature points provided in an embodiment of the present application;

[0021] Figure 2 A fingerprint image provided in an embodiment of the present application;

[0022] Figure 3 A binary image provided in an embodiment of the present application;

[0023] Figure 4 A feature point image provided in an embodiment of the present application;

[0024] Figure 5 A flowchart of another method for deleting fingerprint feature points provided in an embodiment of the present application;

[0025] Figure 6Another feature point image provided by an embodiment of the present application;

[0026] Figure 7 Another feature point image provided by an embodiment of the present application;

[0027] Figure 8 Another feature point image provided by an embodiment of the present application;

[0028] Fig. 9 Another feature point image provided by an embodiment of the present application;

[0029] Fig.10 A schematic diagram of the structure of a device for deleting fingerprint feature points provided in an embodiment of the present application;

[0030] Fig.11 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0032] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0033] The present application embodiment provides a method for deleting fingerprint feature points, which can be applied to a terminal device with a fingerprint recognition function, such as a smart phone, a fingerprint lock or a PC. Figure 1 As shown, the method includes:

[0034] Step 101: Obtain a fingerprint image.

[0035] In an embodiment of the present application, the terminal device generally includes a fingerprint acquisition module and a fingerprint recognition module. The fingerprint acquisition module is used to respond to the user's touch operation, obtain the user's fingerprint information, and generate a fingerprint image based on the fingerprint information. The fingerprint recognition module is used to perform fingerprint image processing based on the fingerprint image to achieve fingerprint recognition.

[0036] In this step, the fingerprint recognition module can obtain the fingerprint image generated by the fingerprint collection module, wherein the acquired fingerprint image can be a sample fingerprint image collected by the fingerprint collection module when the user uses a terminal device for the first time, and the sample fingerprint image is used for user identity authentication; or, the acquired fingerprint image can be a temporary fingerprint image collected by the fingerprint collection module when the user uses the terminal device for identity authentication. By comparing the temporary fingerprint image with the stored sample fingerprint image, the user identity can be determined.

[0037] Step 102: Process the fingerprint image to obtain a feature point image.

[0038] It should be noted that the lines of a person's fingerprint are usually not complete or continuous lines. The lines of a fingerprint contain breakpoints and / or bifurcation points, which are feature points in the fingerprint. The feature point image of the fingerprint image can be determined by processing the fingerprint image.

[0039] In this step, the process of the fingerprint recognition module processing the fingerprint image to obtain the feature point image may be: binarizing the fingerprint image to obtain the binary image; and pixelating the binary image to obtain the feature point image.

[0040] For example, Figure 2 As shown, Figure 2 A fingerprint image of a user collected by a terminal device. Figure 3 is the binary image obtained after the fingerprint image is binarized. Figure 4 The feature point image is obtained after pixelation processing is performed on the binary image. The feature point image includes multiple feature points, wherein the feature point A1 is a bifurcation point in the fingerprint, and the feature point A2 is a breakpoint in the fingerprint.

[0041] Step 103: traverse the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the fingerprint breaklines.

[0042] In the embodiment of the present application, the fracture in a person's fingerprint is usually a straight line, which usually intersects with the lines of the fingerprint. If there is a fracture in the user's fingerprint, in the feature point image corresponding to the fingerprint image of the user, multiple fingerprints will contain feature points in a certain area, and this area is determined as the fracture area. By judging whether the feature point image contains feature points belonging to the fracture area, it can be determined whether the feature point image contains feature points corresponding to the fracture of the fingerprint.

[0043] In this step, if Figure 5 As shown, the process of the fingerprint recognition module determining whether the feature point image contains feature points corresponding to the fingerprint fracture pattern may be:

[0044] Step 1301: Taking any breakpoint or bifurcation point in the feature point image as a reference feature point, an initial feature point set is selected.

[0045] In this step, any breakpoint or bifurcation point in the feature point image can be used as a reference feature point, and two feature points adjacent to the reference feature point can be selected to determine an initial feature point set.

[0046] It can be understood that in the embodiment of the present application, the feature points corresponding to the broken lines of the fingerprint are usually the breakpoints in the fingerprint. Therefore, the feature points in the feature point image can be classified and the feature points in the feature point image can be divided into breakpoint feature points and fingerprint feature points. In order to improve processing efficiency, in the process of selecting reference feature points, any breakpoint can be selected as a reference feature point from at least one breakpoint feature point.

[0047] Step 1302: Determine whether the three feature points in the initial feature point set are feature points corresponding to the suspected fault lines.

[0048] In this step, the process of judging whether the three feature points in the initial feature point set are feature points corresponding to the fracture of the fingerprint can be: determining the feature point distance between any two feature points among the three feature points to obtain the first feature point distance, the second feature point distance and the third feature point distance; determining the maximum feature point distance among the first feature point distance, the second feature point distance and the third feature point distance; determining the sum of the feature point distances of two non-maximum feature point distances among the first feature point distance, the second feature point distance and the third feature point distance; based on the relationship between the sum and the maximum feature point distance, determining whether the three feature points are feature points corresponding to the suspected fracture.

[0049] In the embodiment of the present application, based on the relationship between the sum value and the maximum feature point distance, the process of determining whether three feature points are feature points corresponding to suspected faults may include the following optional implementation methods:

[0050] In an optional implementation, the process of determining whether the three feature points are feature points corresponding to the suspected fault may include: judging whether the difference between the sum value and the maximum feature point distance is less than a difference threshold; if so, determining that the three feature points are feature points corresponding to the suspected fault, otherwise, determining that the three feature points are not feature points corresponding to the suspected fault. The difference threshold may be determined based on actual needs, and the embodiment of the present application does not limit this. For example, the difference threshold is 0.25 times the shortest feature point distance among the feature point distances between any two feature points among the three feature points.

[0051] It should be noted that in the embodiment of the present application, the three feature points in the initial feature point set may be on a straight line, or the three feature points in the initial feature point set are not on a straight line. In another optional implementation method, the process of determining whether the three feature points are feature points corresponding to the suspected faults can be: judging whether the sum value is equal to the distance from the maximum feature point; if they are equal, determining that the three feature points are feature points corresponding to the suspected faults, and determining that the three feature points are on a straight line; if they are not equal, judging whether the difference between the sum value and the distance from the maximum feature point is greater than 0 and less than the difference threshold; if so, determining that the three feature points are feature points corresponding to the suspected faults, and determining that the three feature points are not on a straight line; otherwise, determining that the three feature points are not feature points corresponding to the suspected faults.

[0052] In another optional implementation, the process of determining whether three feature points are feature points corresponding to suspected faults can be: judging whether the difference between the sum value and the distance to the maximum feature point is greater than 0 and less than the difference threshold; if so, determining that the three feature points are feature points corresponding to the suspected faults, and determining that the three feature points are not on a straight line; if not, judging whether the sum value and the distance to the maximum feature point are equal; if they are equal, determining that the three feature points are feature points corresponding to the suspected faults, and determining that the three feature points are on a straight line, otherwise, determining that the three feature points are not feature points corresponding to the suspected faults.

[0053] For example, Figure 6 As shown, the initial feature point set includes three feature points: feature point t1, feature point t2 and feature point t3. Assuming that the distance between feature point t1 and feature point t2 is the first feature point distance s1, and the distance between feature point t2 and feature point t3 is the second feature point distance s2, then the distance between feature point t1 and feature point t3 is the third feature point distance s3, and the third feature point distance s3 is determined to be the maximum feature point distance among the three feature point distances. The sum of the first feature point distance s1 and the second feature point distance s2 is determined. Since the sum of the first feature point distance s1 and the second feature point distance s2 is equal to the third feature point distance s3, it can be determined that feature point t1, feature point t2 and feature point t3 are feature points corresponding to suspected faults, and it can be determined that feature point t1, feature point t2 and feature point t3 are in the same straight line.

[0054] like Figure 7As shown, the initial feature point set includes three feature points: feature point t1, feature point t2 and feature point t3. Assuming that the distance between feature point t1 and feature point t2 is the first feature point distance s1, and the distance between feature point t2 and feature point t3 is the second feature point distance s2, then the distance between feature point t1 and feature point t3 is the third feature point distance s3, and the third feature point distance s3 is determined to be the maximum feature point distance among the three feature point distances. The sum of the first feature point distance s1 and the second feature point distance s2 is determined. Since the difference between the sum of the first feature point distance s1 and the second feature point distance s2 and the third feature point distance s3 is greater than 0 and less than the difference threshold, it can be determined that the three feature points t1, feature point t2 and feature point t3 are feature points corresponding to the suspected fault lines, and it can be determined that the feature points t1, feature point t2 and feature point t3 are not in the same straight line.

[0055] Step 1303: If yes, select a new feature point set, determine the new feature point set as the initial feature point set, repeat the above process of determining whether the three feature points in the initial feature point set are feature points corresponding to the suspected faults, until it is determined that the number of feature points corresponding to the suspected faults is greater than or equal to the number threshold, and determine that the feature point image contains feature points corresponding to the faults of the fingerprint.

[0056] In this step, the process of the fingerprint recognition module selecting a new feature point set can be: selecting a reference feature point in the initial feature point set as the first target feature point; selecting any feature point adjacent to the first target feature point in the initial feature point set as the second target feature point; selecting a feature point adjacent to the second target feature point in the feature point image as the third target feature point; and determining the first target feature point, the second target feature point and the third target feature point as the new feature point set.

[0057] In this step, when it is determined that the number of feature points corresponding to the suspected fault is greater than the number threshold, it can be determined that the area where the feature points corresponding to the suspected fault are located is the area where the fingerprint fault is located, and it is determined that the feature point image contains feature points corresponding to the fingerprint fault. The number threshold can be determined based on actual needs, and the embodiment of the present application does not limit this.

[0058] It can be understood that in the embodiment of the present application, if it is determined that the three feature points in the initial feature point set are not the feature points corresponding to the suspected fault lines, the initial feature point set can be reselected and the above steps can be continued until each feature point in the feature point image is traversed.

[0059] Step 104: If included, delete the feature points corresponding to the fingerprint fracture.

[0060] In this step, the fingerprint recognition module can delete the feature points corresponding to the fingerprint fractures. Figure 4After the above step 103, it can be determined that the feature image contains feature points corresponding to the cracks of the fingerprint, and the feature points corresponding to the cracks of the fingerprint are deleted. The feature point image after the crack deletion process is obtained as shown in FIG. Figure 8 As shown, the feature points corresponding to the crack w are deleted.

[0061] It is understandable that in the embodiment of the present application, if the user uses the terminal device for the first time, the fingerprint recognition module can delete the fractures in the sample fingerprint image based on the method of deleting the fingerprint fracture feature points in the above embodiment, and store the processed sample fingerprint image for subsequent user identity authentication; or, if the user does not use the terminal device for the first time, the fingerprint recognition module can delete the fractures in the temporary fingerprint image based on the method of deleting the fingerprint fracture feature points in the above embodiment, perform feature comparison with the stored sample fingerprint image, and perform user identity authentication. The reliability and security of user identity authentication can be improved.

[0062] For example, Fig. 9 As shown, in Fig. 9 The feature point image shown includes feature point A, feature point B, feature point C, feature point D and feature point E, wherein the coordinates of feature point A are (121, 111), the coordinates of feature point B are (124, 118), the coordinates of feature point C are (129, 126), the coordinates of feature point D are (132, 135), and the coordinates of feature point E are (138, 147).

[0063] Take feature point C as the reference feature point, select feature points B and D adjacent to feature point C to form the initial feature point set, and determine the feature point distance S between feature point B and feature point C. BC is 9.43, determine the feature point distance S between feature point C and feature point D CD is 9.49, and the feature point distance S between feature point B and feature point D is determined BD is 18.79, the feature point distance S BD is the maximum feature point distance among the three feature point distances, and determines the feature point distance S BC Distance S from feature point CD The sum of the values ​​is 18.92, and its distance from the maximum feature point is S BD The difference is 0.13. Assume that the difference threshold is the shortest feature point distance S BC 0.25 times of the maximum feature point, the difference threshold is 2.36. Since the sum of the distances of the two non-maximum feature points is equal to the maximum feature point distance S BD The difference is 0.13, which is greater than 0 and less than the difference threshold 2.36. It can be determined that the feature points B and D adjacent to the feature point C are the feature points corresponding to the suspected fault lines.

[0064] Next, feature point C is selected as the first target feature point, feature point B adjacent to the first target feature point C in the initial feature point set is selected as the second target feature point, feature point A adjacent to the second target feature point B in the feature point image is selected as the third target feature point, and feature point A, feature point B and feature point C are determined as a new feature point set.

[0065] Further, determine the feature point distance S between feature point A and feature point B AB is 7.62, and the feature point distance S between feature point B and feature point C is determined BC is 9.43, determine the feature point distance S between feature point A and feature point C AC is 17, determine the feature point distance S AC is the maximum feature point distance among the three feature point distances, and determines the feature point distance S BC Distance S from feature point AB The sum of the values ​​is 16.95, and its distance from the maximum feature point is S AC The difference is 0.05. Assume that the difference threshold is the shortest feature point distance S AB 0.25 times of the maximum feature point, the difference threshold is 1.91. Since the sum of the distances of the two non-maximum feature points is equal to the maximum feature point distance S AC The difference is 0.05, which is greater than 0 and less than the difference threshold 1.91. It can be determined that the feature points A and C adjacent to the feature point B are the feature points corresponding to the suspected fault lines.

[0066] Next, feature point C can be selected as the first target feature point, feature point D adjacent to the first target feature point C in the initial feature point set can be selected as the second target feature point, feature point E adjacent to the second target feature point D in the feature point image can be selected as the third target feature point, and feature point C, feature point B and feature point E can be determined as a new feature point set.

[0067] Further, determine the feature point distance S between feature point C and feature point D CD is 9.49, and the feature point distance S between feature point D and feature point E is determined DE is 13.42, determine the feature point distance S between feature point C and feature point E CE is 22.85, determine the feature point distance S CE is the maximum feature point distance among the three feature point distances, and determines the feature point distance S CD Distance S from feature point DE The sum of the values ​​is 22.91, and its distance from the feature point S AC The difference is 0.06. Assume that the difference threshold is the shortest feature point distance S AB0.25 times of the maximum feature point, the difference threshold is 2.37. Since the sum of the distances of the two non-maximum feature points is equal to the maximum feature point distance S CE The difference is 0.06, which is greater than 0 and less than the difference threshold 2.37. It can be determined that the feature points C and E adjacent to the feature point D are the feature points corresponding to the suspected fault lines.

[0068] Then feature points A, B, C, D and E are feature points corresponding to suspected faults. Assuming that the set quantity threshold is 5, since there are 5 feature points corresponding to suspected faults, the area where feature points A, B, C, D and E are located is determined to be the fault area, and the 5 feature points are determined to be the feature points corresponding to the faults of the fingerprint, and feature points A, B, C, D and E are deleted.

[0069] In summary, the method for deleting fingerprint fracture feature points provided in the embodiment of the present application can obtain a fingerprint image; process the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include the breakpoints and / or bifurcation points of the fingerprint; traverse the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the fracture of the fingerprint; if so, delete the feature points corresponding to the fracture of the fingerprint. The accuracy of fingerprint fracture recognition can be improved, and other valid feature information can be prevented from being deleted when the fingerprint fracture is deleted, which is conducive to improving the accuracy and reliability of fingerprint recognition.

[0070] The present application embodiment provides a device for deleting fingerprint feature points, such as Fig.10 As shown, including:

[0071] An acquisition module 201 is configured to acquire a fingerprint image;

[0072] The processing module 202 is configured to process the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include the breakpoints and / or bifurcation points of the fingerprint;

[0073] The determination module 203 is configured to traverse the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the breaklines of the fingerprint;

[0074] The deletion module 204 is configured to delete the feature points corresponding to the fingerprint fracture, if included.

[0075] Optionally, the determination module 203 is configured to:

[0076] Taking any breakpoint or bifurcation point in the feature point image as a reference feature point, an initial feature point set is selected, and the initial feature point set includes three adjacent feature points;

[0077] Determine whether the three feature points in the initial feature point set are feature points corresponding to the suspected faults;

[0078] If so, select a new feature point set and determine the new feature point set as the initial feature point set. Repeat the above process of determining whether the three feature points in the initial feature point set are feature points corresponding to the suspected faults, until it is determined that the number of feature points corresponding to the suspected faults is greater than or equal to the number threshold, and determine that the feature point image contains feature points corresponding to the faults of the fingerprint.

[0079] Optionally, the determination module 203 is configured to:

[0080] Determine the feature point distance between any two feature points among the three feature points to obtain a first feature point distance, a second feature point distance, and a third feature point distance;

[0081] Determine the maximum feature point distance among the first feature point distance, the second feature point distance, and the third feature point distance;

[0082] Determine a sum of feature point distances of two non-maximum feature point distances among the first feature point distance, the second feature point distance, and the third feature point distance;

[0083] Based on the relationship between the sum value and the maximum feature point distance, determine whether the three feature points are feature points corresponding to the suspected fault lines.

[0084] Optionally, the determination module 203 is configured to:

[0085] Determine whether the sum value is equal to the distance of the maximum feature point;

[0086] If they are equal, the three feature points are determined to be the feature points corresponding to the suspected fault lines.

[0087] Optionally, the determination module 203 is configured to:

[0088] Determine whether the difference between the sum value and the maximum feature point distance is greater than 0 and less than the difference threshold;

[0089] If so, the three feature points are determined to be feature points corresponding to the suspected fault lines.

[0090] Optionally, the determination module 203 is configured to:

[0091] Determine whether the difference between the sum and the maximum feature point distance is less than a difference threshold;

[0092] If so, the three feature points are determined to be feature points corresponding to the suspected fault lines.

[0093] Optionally, the determination module 203 is configured to:

[0094] Select a reference feature point in the initial feature point set as the first target feature point;

[0095] Select any feature point in the initial feature point set that is adjacent to the first target feature point as the second target feature point;

[0096] Selecting a feature point adjacent to the second target feature point in the feature point image as a third target feature point;

[0097] The first target feature point, the second target feature point and the third target feature point are determined as a new feature point set.

[0098] Optionally, the processing module 202 is configured to:

[0099] Binarizing the fingerprint image to obtain a binary image;

[0100] The binary image is pixelated to obtain a feature point image.

[0101] In summary, the device for deleting fingerprint fracture feature points provided in the embodiment of the present application can obtain a fingerprint image; process the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include the breakpoints and / or bifurcation points of the fingerprint; traverse the breakpoints and / or bifurcation points to determine whether the feature point image contains feature points corresponding to the fracture of the fingerprint; if so, delete the feature points corresponding to the fracture of the fingerprint. The accuracy of fingerprint fracture recognition can be improved, and other valid feature information can be prevented from being deleted when the fingerprint fracture is deleted, which is conducive to improving the accuracy and reliability of fingerprint recognition.

[0102] Fig.11 301 is a computer system according to an exemplary embodiment, which includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage part to a random access memory (RAM) 303. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0103] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

[0104] In particular, according to the embodiments of the present application, the above Figure 1 and Figure 5 The described process can be implemented as a computer software program. For example, various embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU) 301, the above-mentioned functions defined in the system of the present application are executed.

[0105] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0106] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the method, device and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0107] The units involved in the embodiments described in the present application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves. The units or modules described may also be set in a processor, for example, they may be described as: a processor includes an acquisition module, a processing module, a determination module, and a deletion module. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves, for example, the acquisition module may also be described as an "acquisition module for acquiring fingerprint images".

[0108] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may be included separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method for deleting fingerprint feature points as described in the above embodiment.

[0109] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. A method for deleting fingerprint feature points, characterized in that: include: Acquire fingerprint image; Processing the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include breakpoints and / or bifurcation points of the fingerprint; Traversing the breakpoints and / or the bifurcation points to determine whether the feature point image contains feature points corresponding to the breaklines of the fingerprint; If included, deleting the feature points corresponding to the fracture pattern of the fingerprint; The determining whether the feature point image contains feature points corresponding to the fingerprint fracture pattern includes: Taking any breakpoint or bifurcation point in the feature point image as a reference feature point, an initial feature point set is selected, wherein the initial feature point set includes three adjacent feature points; Determining whether the three feature points in the initial feature point set are feature points corresponding to suspected faults; If so, select a new feature point set, determine the new feature point set as the initial feature point set, and repeat the above process of determining whether the three feature points in the initial feature point set are feature points corresponding to suspected faults, until it is determined that the number of feature points corresponding to suspected faults is greater than or equal to the number threshold, and determine that the feature point image contains feature points corresponding to the faults of the fingerprint.

2. The method according to claim 1, characterized in that The determining whether the three feature points in the initial feature point set are feature points corresponding to suspected faults includes: Determine a feature point distance between any two of the three feature points to obtain a first feature point distance, a second feature point distance, and a third feature point distance; Determine the maximum feature point distance among the first feature point distance, the second feature point distance, and the third feature point distance; Determine a sum of feature point distances of two non-maximum feature point distances among the first feature point distance, the second feature point distance, and the third feature point distance; Based on the relationship between the sum value and the maximum feature point distance, it is determined whether the three feature points are feature points corresponding to the suspected fault lines.

3. The method according to claim 2, characterized in that The determining whether the three feature points are feature points corresponding to suspected faults based on the relationship between the sum value and the maximum feature point distance includes: Determine whether the difference between the sum and the maximum feature point distance is less than a difference threshold; If so, the three feature points are determined to be feature points corresponding to the suspected fault lines.

4. The method according to claim 2, characterized in that: The determining whether the three feature points are feature points corresponding to suspected faults based on the relationship between the sum value and the maximum feature point distance includes: Determine whether the sum value is equal to the distance of the maximum feature point; If they are equal, the three feature points are determined to be feature points corresponding to the suspected fault lines.

5. The method according to claim 2, characterized in that: The determining whether the three feature points are feature points corresponding to suspected faults based on the relationship between the sum value and the maximum feature point distance includes: Determine whether the difference between the sum and the maximum feature point distance is greater than 0 and less than a difference threshold; If so, the three feature points are determined to be feature points corresponding to the suspected fault lines.

6. The method according to claim 1, characterized in that The selecting of a new feature point set comprises: Selecting the reference feature point in the initial feature point set as the first target feature point; Select any feature point in the initial feature point set that is adjacent to the first target feature point as a second target feature point; Selecting a feature point adjacent to the second target feature point in the feature point image as a third target feature point; The first target feature point, the second target feature point and the third target feature point are determined as a new feature point set.

7. The method according to claim 1, characterized in that The processing of the fingerprint image to obtain a feature point image includes: Binarizing the fingerprint image to obtain a binary image; The binary image is pixelated to obtain a feature point image.

8. A device for deleting fingerprint feature points, characterized in that: include: An acquisition module, configured to acquire a fingerprint image; A processing module, configured to process the fingerprint image to obtain a feature point image, wherein the feature points in the feature point image include breakpoints and / or bifurcation points of the fingerprint; a determination module configured to traverse the breakpoints and / or the bifurcation points to determine whether the feature point image contains feature points corresponding to the breaklines of the fingerprint; a deletion module configured to delete, if included, feature points corresponding to the fracture pattern of the fingerprint; The determination module is further configured to: select an initial feature point set by taking any breakpoint or bifurcation point in the feature point image as a reference feature point, wherein the initial feature point set includes three adjacent feature points; Determining whether the three feature points in the initial feature point set are feature points corresponding to suspected faults; If so, select a new feature point set, determine the new feature point set as the initial feature point set, and repeat the above process of determining whether the three feature points in the initial feature point set are feature points corresponding to suspected faults, until it is determined that the number of feature points corresponding to suspected faults is greater than or equal to the number threshold, and determine that the feature point image contains feature points corresponding to the faults of the fingerprint.

9. A computer device, characterized in that: The computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is used to implement the method according to any one of claims 1 to 7.

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