Fingerprint unlocking automatic test method and system, electronic equipment and storage medium

Abstract

The invention provides a fingerprint unlocking automatic testing method and system, electronic equipment and a storage medium, and belongs to the technical field of automatic testing. According to the scheme, in response to the automatic test instruction, the target item is determined from the preset automatic test items, the initial fingerprint image is acquired, the feature parameters in the initial fingerprint image are extracted, the first fingerprint feature is obtained, the first fingerprint feature is adjusted according to the target item, and the simulated fingerprint feature set is obtained, the simulated fingerprint feature set comprises a plurality of different simulated fingerprint features, obtaining a corresponding simulated fingerprint image according to each simulated fingerprint feature in the simulated fingerprint feature set, inputting each simulated fingerprint image into the fingerprint recognition controller to be recognized with the initial fingerprint image, and obtaining a feedback signal; and data analysis is performed according to the preset test indexes and all the feedback signals to obtain an automatic test result of fingerprint unlocking, so that repeated manual operation can be reduced, and the test efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of automated testing technology, and in particular to a fingerprint unlocking automated testing method, system, electronic device and storage medium. Background Art

[0002] With the development of science and technology, fingerprint unlocking, as an identification technology, has been widely used in scenarios such as smartphones, access control, vehicles, and payments. Users can use the recorded fingerprint information to turn on the device they need to use. A large number of repeated tests are required during the development of the fingerprint unlocking function. At present, the test of the fingerprint unlocking function is mainly carried out by testers repeatedly entering fingerprint information for comparison, and the test efficiency is low. In addition, the manually entered fingerprint information is often tested in an ideal environment or under standard conditions. Although this test can verify the performance of the fingerprint unlocking function, the test environment is single and it is difficult to cover various complex situations that may be encountered in actual applications, such as incomplete, blurred or low-quality difficult fingerprints such as worn fingerprints, wet fingerprints and shallow textured fingerprints. Due to the lack of testing for complex situations, when a user tries to unlock with a difficult fingerprint, if it cannot be accurately identified, the user needs to try multiple times, and may not even be able to unlock successfully, affecting the user experience. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a fingerprint unlocking automated testing method, system, electronic device and storage medium, aiming to reduce repetitive manual operations, reduce the number of test sample acquisition requirements, increase the coverage of test scenarios, improve the reliability and accuracy of test results, and realize fingerprint unlocking automated testing to improve testing efficiency.

[0004] To achieve the above purpose, an embodiment of the present application provides a fingerprint unlocking automated testing method, the fingerprint unlocking automated testing method comprising:

[0005] In response to the automated test instruction, determining a target item from preset automated test items, wherein the automated test items include an unlocking posture test and a difficult fingerprint recognition test;

[0006] Obtaining an initial fingerprint image;

[0007] Extracting characteristic parameters from the initial fingerprint image to obtain a first fingerprint feature, wherein the first fingerprint feature includes fingerprint size, fingerprint posture, texture clarity, and texture wear;

[0008] Adjusting the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features;

[0009] Obtaining a corresponding simulated fingerprint image according to each of the simulated fingerprint features in the simulated fingerprint feature set;

[0010] Input each of the simulated fingerprint images into a fingerprint recognition controller for recognition with the initial fingerprint image to obtain a feedback signal;

[0011] Data analysis is performed based on preset test indicators and all the feedback signals to obtain automated test results for fingerprint unlocking.

[0012] In some embodiments, adjusting the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set includes the following steps:

[0013] When the target item is the unlocking posture test, the fingerprint size and the fingerprint posture in the first fingerprint feature are adjusted to obtain a plurality of different simulated fingerprint features;

[0014] All the simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

[0015] In some embodiments, adjusting the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set includes the following steps:

[0016] When the target item is the difficult fingerprint recognition test, the texture clarity and the texture wear degree in the first fingerprint feature are adjusted to obtain a plurality of different simulated fingerprint features;

[0017] All the simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

[0018] In some embodiments, the step of adjusting the fingerprint size and the fingerprint posture in the first fingerprint feature to obtain a plurality of different simulated fingerprint features includes the following steps:

[0019] The fingerprint size in the first fingerprint feature is adjusted by scaling the ridges to obtain a plurality of second fingerprint features of different fingerprint sizes;

[0020] By adjusting the fingerprint posture in the first fingerprint feature by changing the angle of the ridges, a third fingerprint feature with multiple different fingerprint posture directions is obtained;

[0021] The second fingerprint feature and the third fingerprint feature are determined as simulated fingerprint features.

[0022] In some embodiments, the adjusting the texture clarity and the texture wear in the first fingerprint feature to obtain a plurality of different simulated fingerprint features comprises the following steps:

[0023] By changing the depth of the grain lines to adjust the texture clarity in the first fingerprint feature, a plurality of fourth fingerprint features with different texture clarity are obtained;

[0024] The texture wear degree in the first fingerprint feature is adjusted by increasing or decreasing the width of the grain line to obtain a plurality of fifth fingerprint features with different texture wear degrees;

[0025] The fourth fingerprint feature and the fifth fingerprint feature are determined as simulated fingerprint features.

[0026] In some embodiments, performing data analysis based on preset test indicators and all the feedback signals to obtain automated test results for fingerprint unlocking includes the following steps:

[0027] Counting the number of successful recognitions and the total number of recognitions of the feedback signal to determine the recognition success rate;

[0028] Compare the recognition success rate with a preset test index to determine whether the recognition success rate is greater than the test index;

[0029] When the recognition success rate is greater than the test index, it is determined that the automated test result of fingerprint unlocking is functioning normally.

[0030] In some embodiments, the automated test item further includes a fingerprint unlocking capacity test, and the method further includes the following steps:

[0031] The simulated fingerprint images corresponding to the simulated fingerprint feature set are sequentially memorized in the order in which the images are generated;

[0032] The automated test result of the fingerprint unlocking capacity is obtained according to the memory quantity of the simulated fingerprint image.

[0033] To achieve the above purpose, another aspect of the embodiment of the present application provides a fingerprint unlocking automatic testing system, the fingerprint unlocking automatic testing system comprising:

[0034] A first module is used to determine a target item from preset automated test items in response to an automated test instruction, wherein the automated test items include an unlocking posture test and a difficult fingerprint recognition test;

[0035] The second module is used to obtain an initial fingerprint image;

[0036] A third module is used to extract characteristic parameters from the initial fingerprint image to obtain a first fingerprint feature, wherein the first fingerprint feature includes fingerprint size, fingerprint posture, texture clarity and texture wear;

[0037] A fourth module is used to adjust the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features;

[0038] A fifth module, configured to obtain a corresponding simulated fingerprint image according to each of the simulated fingerprint features in the simulated fingerprint feature set;

[0039] A sixth module is used to input each of the simulated fingerprint images into a fingerprint recognition controller for recognition with the initial fingerprint image to obtain a feedback signal;

[0040] The seventh module is used to perform data analysis based on preset test indicators and all the feedback signals to obtain automated test results for fingerprint unlocking.

[0041] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0042] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0043] The embodiments of the present application include at least the following beneficial effects: The main purpose of the embodiments of the present application is to propose a fingerprint unlocking automatic test method, system, electronic device and storage medium, which responds to the automatic test instruction, determines the target project from the preset automatic test project, obtains the initial fingerprint image, extracts the feature parameters in the initial fingerprint image, obtains the first fingerprint feature, adjusts the first fingerprint feature according to the target project, and obtains the simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features, and obtains the corresponding simulated fingerprint image according to each simulated fingerprint feature in the simulated fingerprint feature set, which can reduce repeated manual operations, reduce the number of test sample acquisition requirements, increase the coverage of the test scene, and improve the reliability and accuracy of the test results. By inputting each simulated fingerprint image into the fingerprint recognition controller for identification with the initial fingerprint image, a feedback signal is obtained, and data analysis is performed according to the preset test indicators and all feedback signals to obtain the automatic test results of fingerprint unlocking, which can realize the fingerprint unlocking automatic test to improve the test efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the fingerprint unlocking automated testing method provided in an embodiment of the present application;

[0045] Figure 2 yes Figure 1 Specific method step flow chart of step S104;

[0046] Figure 3 Another embodiment of the present application provides Figure 1 Specific method step flow chart of step S104;

[0047] Figure 4 yes Figure 2 Specific method step flow chart of step S201;

[0048] Figure 5 yes Figure 3 Specific method step flow chart of step S301;

[0049] Figure 6 yes Figure 1 Specific method step flow chart of step S107;

[0050] Figure 7 This is a flowchart of the steps of the fingerprint unlocking automated testing method provided by another embodiment of the present application;

[0051] Figure 8 This is a schematic diagram of the application architecture system of the fingerprint unlocking automated testing method provided in the embodiment of the present application;

[0052] Fig. 9 It is a schematic diagram of the structure of the test management system provided in the embodiment of the present application;

[0053] Fig.10 This is a schematic diagram of the structure of the simulated fingerprint generation system provided in the embodiment of the present application.

[0054] Fig.11 It is a structural diagram of a fingerprint unlocking automated testing system provided in an embodiment of the present application;

[0055] Fig.12 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0057] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0058] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] Before specifically introducing the technical solutions of the embodiments of the present application, the technical background or technical evolution context on which the embodiments of the present application are based is first introduced.

[0061] As a biometric recognition technology, fingerprint unlocking has become relatively complete with the continuous advancement of fingerprint recognition algorithms and the increasing maturity of sensor technology. Taking the application of fingerprint unlocking in vehicles as an example, vehicle intelligence has become an important trend in the development of the vehicle industry. Among them, vehicle fingerprint unlocking, as a key function to improve vehicle safety and convenience, is widely used in various types of vehicles to achieve functions such as unlocking, locking, starting, and shutting down. However, most patents currently focus on the design and optimization of fingerprint unlocking devices. There is a lack of systematic research and specialized testing equipment for comprehensive testing and verification of fingerprint unlocking in actual application scenarios, especially for recognition performance in special situations, such as the recognition rate of incomplete, blurred or low-quality difficult fingerprints such as worn fingerprints, wet fingerprints and shallow textured fingerprints.

[0062] In view of this, the present application provides a fingerprint unlocking automated testing method, system, electronic device and storage medium. The scheme determines the target project from the preset automated testing projects in response to the automated testing instruction, obtains the initial fingerprint image, extracts the feature parameters in the initial fingerprint image, obtains the first fingerprint feature, adjusts the first fingerprint feature according to the target project, and obtains a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features, and obtains the corresponding simulated fingerprint image according to each simulated fingerprint feature in the simulated fingerprint feature set, which can reduce repeated manual operations, reduce the number of test sample acquisition requirements, increase the coverage of the test scene, and improve the reliability and accuracy of the test results. By inputting each simulated fingerprint image into the fingerprint recognition controller for identification with the initial fingerprint image, a feedback signal is obtained, and data analysis is performed according to the preset test indicators and all feedback signals to obtain the automated test results of fingerprint unlocking, which can realize the automated test of fingerprint unlocking to improve the test efficiency.

[0063] Next, some nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0064] (1) Direction field: Direction field refers to the set of ridge directions in the fingerprint image, reflecting the overall trend and local changes of the ridge directions in the fingerprint image.

[0065] (2) Core point: The core point refers to the point where the lines in the fingerprint image gradually converge or diverge. It is usually located at the asymptotic center of the fingerprint lines and is the most stable feature point in the fingerprint image. The lines around the core point are roughly semicircular.

[0066] (3) Triangulation point: A triangulation point refers to the first bifurcation point or breakpoint starting from the core point, or the convergence point of two lines, an isolated point, a turning point, or points to these singular points.

[0067] The method provided in the embodiment of the present application can be applied to a terminal, a server, or software running in a terminal or a server. In some embodiments, the terminal can be a touch display terminal, a multi-function operation terminal, and a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application of the implementation method, etc., but is not limited to the above forms.

[0068] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on user information, user behavior data, user historical data and other data related to user identity or characteristics, the user's permission or consent will be obtained first, and the collection, use and processing of these data will comply with relevant laws, regulations and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or jump to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0069] Reference Figure 1 , Figure 1 It is an optional flowchart of the fingerprint unlocking automated testing method provided in an embodiment of the present application. The fingerprint unlocking automated testing method may include but is not limited to steps S101 to S107.

[0070] Step S101, in response to an automated test instruction, determining a target item from preset automated test items, wherein the automated test items include an unlocking gesture test and a difficult fingerprint recognition test.

[0071] Step S102, obtaining an initial fingerprint image.

[0072] Step S103, extracting characteristic parameters from the initial fingerprint image to obtain a first fingerprint feature, wherein the first fingerprint feature includes fingerprint size, fingerprint posture, texture clarity and texture wear.

[0073] Step S104, adjusting the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features.

[0074] Step S105, obtaining a corresponding simulated fingerprint image according to each simulated fingerprint feature in the simulated fingerprint feature set.

[0075] Step S106: input each simulated fingerprint image into a fingerprint recognition controller for recognition with the initial fingerprint image to obtain a feedback signal.

[0076] Step S107, performing data analysis according to preset test indicators and all feedback signals to obtain automated test results of fingerprint unlocking.

[0077] In this embodiment, considering that the current testing method of manually repeatedly entering fingerprints is inefficient and the application scenario of the test is single, after collecting the initial fingerprint, the present application modifies some features based on the initial fingerprint to generate simulated fingerprints for multiple application scenarios without relying on a large number of human fingerprint samples, thereby reducing the demand for a large number of samples during the test and improving the test efficiency.

[0078] First, in response to the automated testing instruction, the target item is determined from the preset automated testing items, wherein the automated testing items include an unlocking posture test and a difficult fingerprint recognition test. The unlocking posture test is used to simulate the fingerprint unlocking situation under different fingerprint postures, and the difficult fingerprint recognition test is used to simulate the fingerprint unlocking situation under different scenarios.

[0079] Exemplarily, the test system monitors and receives automated test instructions sent by testers. Automated test instructions are a series of commands or scripts that execute test tasks by programming or configuring automated test tools. The automated test instructions are parsed to obtain specific test project identifiers that specify the test items to be executed, and the target items are determined from the preset automated test items according to the project identifiers.

[0080] It is understandable that the automated test instructions may be received through a variety of methods such as a network interface, serial port communication, file writing, etc., and the embodiments of the present application do not impose any specific limitations thereto.

[0081] Next, obtain the initial fingerprint image. Fingerprints refer to the lines formed by the raised skin on the fingers. These lines appear in different shapes and arrangements on the fingers, making fingerprints a unique identification for each person. Specifically, when it is detected that the tester's finger contacts the fingerprint collection module, the lines in contact with the fingerprint collection module in the specified area are collected to obtain the initial fingerprint image, and the initial fingerprint image is used as a template for subsequent adjustment according to the test item requirements, thereby reducing the number of test sample acquisition requirements.

[0082] After the initial fingerprint image is obtained, feature parameters in the initial fingerprint image are extracted to obtain a first fingerprint feature, wherein the first fingerprint feature includes fingerprint size, fingerprint posture, texture clarity and texture wear.

[0083] Specifically, the collected initial fingerprint image is subjected to preprocessing steps such as grayscale, image enhancement, noise reduction, binarization and thinning to improve the image quality and facilitate subsequent feature extraction. Through the feature extraction algorithm, the fingerprint size, fingerprint posture, texture clarity and texture wear are extracted to obtain the first fingerprint feature.

[0084] For example, the fingerprint size is obtained by measuring the length and width of the fingerprint in the image; the fingerprint posture is obtained by measuring the direction field, core point and triangulation point of the ridges; the texture clarity is obtained by analyzing the gray value or contrast of the ridges. It should be noted that the texture clarity is proportional to the gray value or contrast; the width of the ridges is measured and the change of the ridge width is calculated to evaluate the texture wear. All the extracted feature parameters are integrated into a unified data structure as the first fingerprint feature output.

[0085] Further, the first fingerprint feature is adjusted according to the target project to obtain a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features. In view of the differences in the content of fingerprint unlocking test for different target projects, different feature parameters in the first fingerprint feature are adjusted according to the target project selection.

[0086] Specifically, at least one characteristic parameter in the first fingerprint feature is adjusted according to the target project to obtain a simulated fingerprint feature set. When adjusting the characteristic parameters, at least one characteristic parameter is different between different simulated fingerprint features. Optionally, one of the characteristic parameters can be adjusted, or multiple characteristic parameters can be adjusted at the same time to simulate the real fingerprint under different conditions to obtain different simulated fingerprint features, and all simulated fingerprint features are integrated to obtain the simulated fingerprint feature set of the target project.

[0087] For example, according to the specific needs of the target project test, the characteristic parameters in the first fingerprint feature are adjusted differently to expand the number of samples and improve the diversity of samples. In actual applications, users may place fingerprints on the fingerprint collection module at different angles and directions. This change will cause the collected fingerprint image features to rotate or tilt. Therefore, the fingerprint posture in the first fingerprint feature is adjusted, and the fingerprint posture is rotated based on the preset change amplitude, such as rotating 1°, 2°, ..., 180° in clockwise order with the core point as the rotation center, thereby changing the fingerprint posture to simulate the user's fingerprint features in different directions and obtain multiple simulated fingerprint features.

[0088] It should be noted that fingerprint unlocking recognition technology mainly relies on the characteristic points in the fingerprint (such as endpoints, bifurcation points, etc.). These characteristic points are not affected by the adjustment of characteristic parameters and remain relatively stable.

[0089] To facilitate subsequent fingerprint unlocking and identification, after obtaining the simulated fingerprint feature set, the fingerprint features need to be converted into simulated data (such as images). Therefore, a corresponding simulated fingerprint image is obtained according to each simulated fingerprint feature in the simulated fingerprint feature set. The simulated fingerprint features in the simulated fingerprint feature set are sequentially input into the fingerprint simulation module for simulation to obtain a simulated fingerprint image corresponding to each simulated fingerprint feature. The simulated fingerprint image is generated based on the initial fingerprint image, which provides the basic fingerprint morphology and features. The simulated fingerprint image generated based on the simulated fingerprint features after adjusting the feature parameters can restore the changes in the user's real fingerprint under different circumstances.

[0090] For example, in the unlocking posture test, the simulated fingerprint image focuses on simulating the fingerprint state of the user placing the finger at different angles or in different ways, and generates fingerprint images of various unlocking postures according to the simulated fingerprint feature set, and obtains simulated fingerprint images of various directions and angles such as forward fingerprint, lateral fingerprint and reverse fingerprint to simulate the user's unlocking posture in different scenarios.

[0091] Alternatively, in the difficult fingerprint recognition test, the simulated fingerprint image focuses on simulating the fingerprint state that is difficult to recognize, and a variety of difficult fingerprint images are generated according to the simulated fingerprint feature set to obtain a variety of simulated fingerprint images that are easy to cause recognition difficulties, such as wet fingerprints and shallow texture fingerprints, in order to test the recognition rate of fingerprint unlocking for difficult fingerprints.

[0092] Then, each simulated fingerprint image is input into the fingerprint recognition controller for identification with the initial fingerprint image to obtain a feedback signal. The simulated fingerprint image is received through the software interface or hardware, and the simulated fingerprint image is used as the test input of the fingerprint recognition controller. The fingerprint recognition controller executes the recognition algorithm, identifies and compares each simulated fingerprint image with the initial fingerprint image in turn, determines whether they match, and captures and records the feedback signal output by the fingerprint recognition controller, which includes recognition success or recognition failure.

[0093] Finally, data analysis is performed based on preset test indicators and all feedback signals to obtain automated test results that are used to evaluate whether the different functions of fingerprint unlocking are normal, or to evaluate the performance level of each function.

[0094] Reference Figure 2 In some embodiments, step S104 may include but is not limited to steps S201 to S202.

[0095] Step S201, when the target item is an unlocking posture test, the fingerprint size and fingerprint posture in the first fingerprint feature are adjusted to obtain a plurality of different simulated fingerprint features.

[0096] Step S202: Integrate all simulated fingerprint features to obtain a simulated fingerprint feature set.

[0097] Since fingerprint unlocking tests are usually conducted under idealized conditions, it is impossible to fully simulate the various postures that users may use in actual use. Considering that the initial fingerprint image collected is usually a static snapshot of the user's fingerprint, it is impossible to cover all the feature changes that the user may show in actual use. In actual use, users may use a variety of unlocking postures to unlock their fingerprints in different directions and angles. The change in posture will affect the contact mode and quality between the fingerprint and the fingerprint recognition device, thereby affecting the recognition effect.

[0098] In order to test the accuracy and stability of the fingerprint unlocking device in recognizing different unlocking postures of the user, in this embodiment, when the target item is the unlocking posture test, the fingerprint size and fingerprint posture in the first fingerprint feature are adjusted to obtain multiple different simulated fingerprint features, and all the simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

[0099] In another embodiment, when the target item is an unlocking posture test, the fingerprint size or fingerprint posture in the first fingerprint feature is adjusted to obtain a plurality of different simulated fingerprint features, and all simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

[0100] Specifically, the unlocking posture test may include a test at different tilt angles in the same direction, a test at the same tilt angle in different directions, and a test at different tilt angles and in different directions.

[0101] Exemplarily, by adjusting the fingerprint size in the first fingerprint feature, a plurality of different simulated fingerprint features are obtained to simulate a plurality of unlocking postures of the user performing fingerprint unlocking at different tilt angles in the same direction.

[0102] Alternatively, by adjusting the fingerprint posture in the first fingerprint feature, a plurality of different simulated fingerprint features are obtained to simulate a plurality of unlocking postures of the user using the same tilt angle but different directions to perform fingerprint unlocking.

[0103] Alternatively, by adjusting the fingerprint size and fingerprint posture in the first fingerprint feature at the same time, multiple different simulated fingerprint features are obtained to simulate the user's multiple unlocking postures at different tilt angles and directions. For example, a deep learning algorithm is used to learn the mapping relationship of fingerprints in different directions and angles through learning models such as generative adversarial networks, thereby adjusting the fingerprint size and fingerprint posture to obtain multiple different simulated fingerprint features.

[0104] Reference Figure 3 In some embodiments, step S104 may include but is not limited to steps S301 to S302.

[0105] Step S301, when the target item is a difficult fingerprint recognition test, the texture clarity and texture wear in the first fingerprint feature are adjusted to obtain a plurality of different simulated fingerprint features.

[0106] Step S302: Integrate all simulated fingerprint features to obtain a simulated fingerprint feature set.

[0107] Taking into account that when users use different strengths to unlock their fingerprints, the clarity of the fingerprint lines is different. When the lines are too shallow, it is easy to make it difficult to capture enough feature points for comparison; or when the user's finger fingerprint texture changes due to long-term labor or friction, injury, etc.; and when fingerprint unlocking is performed, there is water stains, sweat and other moisture attached to the surface of the finger, blocking part of the fingerprint texture, etc., the above situations will increase the difficulty of fingerprint recognition. If there is a lack of testing for difficult fingerprint recognition, it is impossible to fully and accurately evaluate the recognition rate of fingerprint unlocking.

[0108] In view of this, in this embodiment, when the target item is a difficult fingerprint recognition test, the texture clarity and texture wear in the first fingerprint feature are adjusted to obtain a plurality of different simulated fingerprint features, and all simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

[0109] In another embodiment, when the target item is a difficult fingerprint recognition test, the texture clarity or texture wear in the first fingerprint feature is adjusted to obtain a plurality of different simulated fingerprint features, and all simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

[0110] Specifically, the difficult fingerprint recognition test may include a test of a shallow texture fingerprint, a test of a worn fingerprint, and a test of a wet fingerprint.

[0111] Exemplarily, by adjusting the texture clarity in the first fingerprint feature, a plurality of different simulated fingerprint features are obtained to simulate a plurality of shallow texture fingerprints of the user under different strengths.

[0112] Alternatively, by adjusting the texture wear degree of the first fingerprint feature, a plurality of different simulated fingerprint features are obtained to simulate a plurality of wear fingerprints of users with different wear degrees.

[0113] Alternatively, by adjusting the texture clarity and texture wear of the first fingerprint feature at the same time, a plurality of different simulated fingerprint features are obtained to simulate a plurality of wet fingerprints of the user with different degrees of moisture attached to the fingers.

[0114] Reference Figure 4 In some embodiments, step S201 may include but is not limited to steps S401 to S403.

[0115] Step S401, scaling the ridges to adjust the fingerprint size in the first fingerprint feature to obtain a plurality of second fingerprint features of different fingerprint sizes.

[0116] Step S402, adjusting the fingerprint posture in the first fingerprint feature by performing angle transformation on the ridges to obtain a plurality of third fingerprint features in different fingerprint posture directions.

[0117] Step S403: determine the second fingerprint feature and the third fingerprint feature as simulated fingerprint features.

[0118] In step S401 of some embodiments, the fingerprint size in the first fingerprint feature is adjusted by scaling the ridges to obtain a plurality of second fingerprint features of different fingerprint sizes.

[0119] For example, when simulating a user unlocking the fingerprint at different tilt angles, the fingerprint size can be adjusted by applying perspective change technology, and the projection of the fingerprint image in three-dimensional space is simulated according to the perspective relationship. The fingerprint size in the first fingerprint feature is scaled in length and width according to a certain ratio to obtain a second fingerprint feature of multiple different fingerprint sizes.

[0120] In step S402 of some embodiments, the fingerprint posture in the first fingerprint feature is adjusted by performing angle transformation on the ridges to obtain a plurality of third fingerprint features in different fingerprint posture directions.

[0121] For example, when simulating a user to unlock the fingerprint in different directions, different rotation parameters can be set to transform the angle of the lines in the plane to rotate the fingerprint posture and obtain multiple different third fingerprint features. For example, based on the current first fingerprint feature, the left-facing fingerprint, the reverse fingerprint and the right-facing fingerprint are obtained by rotating the plane counterclockwise by 90°, 180° and 270° respectively.

[0122] In step S403 of some embodiments, all the adjusted second fingerprint features and third fingerprint features are determined as simulated fingerprint features, so that a simulated fingerprint image can be subsequently generated according to the simulated fingerprint features for fingerprint unlocking recognition.

[0123] In another embodiment, the fingerprint size in the first fingerprint feature is adjusted by scaling the ridges to obtain a plurality of second fingerprint features of different fingerprint sizes, and the second fingerprint feature is determined as a simulated fingerprint feature.

[0124] In another embodiment, the fingerprint posture in the first fingerprint feature is adjusted by changing the angle of the grain lines to obtain a third fingerprint feature with multiple different fingerprint posture directions, and the third fingerprint feature is determined as a simulated fingerprint feature.

[0125] Reference Figure 5 In some embodiments, step S301 may include but is not limited to steps S501 to S503.

[0126] Step S501, adjusting the texture clarity in the first fingerprint feature by performing a depth conversion on the ridge lines to obtain a plurality of fourth fingerprint features with different texture clarity.

[0127] Step S502, adjusting the texture wear degree in the first fingerprint feature by increasing or decreasing the width of the grain lines to obtain a plurality of fifth fingerprint features with different texture wear degrees.

[0128] Step S503: determine the fourth fingerprint feature and the fifth fingerprint feature as simulated fingerprint features.

[0129] In step S501 of some embodiments, the texture clarity of the first fingerprint feature is adjusted by performing a depth transformation on the ridge lines to obtain a plurality of fourth fingerprint features with different texture clarity.

[0130] Exemplarily, when simulating a user's shallow texture fingerprint, the texture clarity can be represented by the grayscale value or contrast of the grain lines, and the texture clarity in the first fingerprint feature can be adjusted by transforming the depth of the grain lines. For example, a function transformation is performed based on the grayscale value or contrast corresponding to the texture clarity in the first fingerprint feature, and the grayscale value or contrast of the original grain lines is mapped to a new value through the function, thereby obtaining a fourth fingerprint feature with multiple different texture clarity to simulate different degrees of shallow texture effects.

[0131] It should be noted that the function transformation can be a linear transformation or a nonlinear transformation (logarithmic transformation, exponential transformation, piecewise linear transformation, etc.), and the embodiments of the present application do not impose specific limitations.

[0132] In step S502 of some embodiments, the texture wear degree in the first fingerprint feature is adjusted by increasing or decreasing the width of the ridge lines to obtain a plurality of fifth fingerprint features with different texture wear degrees.

[0133] Exemplarily, when simulating a worn or wet fingerprint of a user, the texture wear degree in the first fingerprint feature can be adjusted by increasing or decreasing the width of the lines. For example, a corrosion operation is randomly performed on a certain section of the lines to reduce the width of the lines to simulate the reduction of details of the fingerprint due to long-term use or wear, or a dilation operation is randomly performed on a certain section of the lines to increase the width of the lines to simulate the blurring of the boundaries between the lines due to moisture in the fingerprint, or the water film formed on the fingerprint surface when the finger is wet fills the concave part between the lines, resulting in an increase in the width of the lines in the imaging, thereby obtaining a plurality of fifth fingerprint features with different texture wear degrees to simulate worn fingerprints or wet fingerprints with different wear degrees.

[0134] In step S503 of some embodiments, all the adjusted fourth fingerprint features and fifth fingerprint features are determined as simulated fingerprint features, so that a simulated fingerprint image can be subsequently generated according to the simulated fingerprint features for fingerprint unlocking recognition.

[0135] In another embodiment, the texture clarity in the first fingerprint feature is adjusted by changing the depth of the grain lines to obtain a plurality of fourth fingerprint features with different texture clarity, and the fourth fingerprint feature is determined as a simulated fingerprint feature.

[0136] In another embodiment, the texture wear degree in the first fingerprint feature is adjusted by increasing or decreasing the width of the grain lines to obtain a plurality of fifth fingerprint features with different texture wear degrees, and the fifth fingerprint feature is determined as a simulated fingerprint feature.

[0137] Reference Figure 6 In some embodiments, step S107 may include but is not limited to steps S601 to S603.

[0138] Step S601: Count the number of successful recognitions of the feedback signal and the total number of recognitions to determine the recognition success rate.

[0139] Step S602, comparing the recognition success rate with a preset test index to determine whether the recognition success rate is greater than the test index.

[0140] Step S603: When the recognition success rate is greater than the test index, it is determined that the automated test result of fingerprint unlocking is functioning normally.

[0141] Exemplarily, if the preset test indicator is a success rate threshold, and the automated test result is used to characterize whether the fingerprint unlocking function is normal, the test system counts the number of successful recognitions and the total number of recognitions in the feedback signal, calculates the recognition success rate of this test, and compares the recognition success rate with the success rate threshold. When the recognition success rate is greater than the success rate threshold, it is confirmed that the automated test result of this fingerprint unlocking is functional.

[0142] It is understandable that the preset test indicators may also include multiple success rate thresholds, and the automated test results are used to characterize the performance of fingerprint unlocking. For example, a first threshold and a second threshold are preset, wherein the first threshold is greater than the second threshold, and when the recognition success rate of this test is greater than the first threshold, the performance of fingerprint unlocking is confirmed to be excellent, when the recognition success rate is less than the first threshold and greater than the second threshold, the performance of fingerprint unlocking is confirmed to be good, and when the recognition success rate is less than the second threshold, the performance of fingerprint unlocking is confirmed to be poor.

[0143] Reference Figure 7 In some embodiments, the fingerprint unlocking automated testing method may also include but is not limited to steps S701 to S702.

[0144] Step S701, fingerprint memorizing is performed in sequence on the simulated fingerprint images corresponding to the simulated fingerprint feature set according to the image generation sequence.

[0145] Step S702, obtaining an automated test result of the fingerprint unlocking capacity according to the memory quantity of the simulated fingerprint image.

[0146] In this embodiment, the preset automated test items in the fingerprint unlocking automated test method also include a fingerprint unlocking capacity test. When the test system receives an automated test instruction and parses it to find that the target item is a fingerprint unlocking capacity test, the simulated fingerprint images corresponding to the simulated fingerprint feature set are memorized in sequence according to the order in which the images are generated.

[0147] Optionally, simulated fingerprint images generated by other automated test projects can be used as test samples for fingerprint unlocking capacity testing. The simulated fingerprint images generated by historical tests are read from the fingerprint image storage module and input into the fingerprint unlocking device to be tested in the order of image generation for fingerprint memory. Alternatively, feature adjustment can be performed based on the initial fingerprint image to obtain new simulated fingerprint images, which are input into the fingerprint unlocking device to be tested in the order of image generation for fingerprint memory.

[0148] Next, the automated test result of the fingerprint unlocking capacity is obtained according to the memory quantity of the simulated fingerprint image. When the number of fingerprint memories of the fingerprint unlocking device is saturated and no new fingerprints can be recorded for memory, the memory quantity of the current simulated fingerprint image is determined as the automated test result of the fingerprint unlocking capacity.

[0149] It should be noted that the fingerprint unlocking automation test method can also set different automated test items according to actual test requirements such as testing fingerprint unlocking response time, testing false recognition rate (treating fingerprints that should not be matched as matched fingerprints), testing fingerprint entry and deletion, etc., to generate simulated fingerprint images that meet different test requirements for testing, thereby improving the coverage of fingerprint unlocking test content and improving the reliability of fingerprint unlocking test.

[0150] The following is a detailed introduction and description of the solution of the embodiment of the present invention in conjunction with a specific application example.

[0151] Reference Figure 8 The embodiment of the present application provides an application architecture of a fingerprint unlocking automated testing method, including a test management system, a simulated fingerprint generation system and a fingerprint recognition controller.

[0152] Among them, the test management system is used to control the simulated fingerprint generation system, and the two interact through data streams; the simulated fingerprint generation system is used to collect fingerprints and generate simulated fingerprint data with similar characteristics to the real fingerprint (i.e., the initial fingerprint image) in order to test the accuracy and robustness of the fingerprint recognition algorithm; the fingerprint recognition controller is used to compare the real fingerprint with the generated simulated fingerprint.

[0153] Reference Fig. 9 ,The test management system includes the host computer control interface and ,automatic testing system.

[0154] Among them, the host computer control interface can be used as a control platform to set initial parameter values ​​and receive data feedback from the fingerprint identifier, thereby confirming the status of the fingerprint recognition controller.

[0155] The automated testing system is used to write automated testing sequences, which can convert the designed test methods into automated testing programs, complete automated testing, and improve test consistency and efficiency.

[0156] Reference Fig.10 The fingerprint simulation generation system includes a fingerprint acquisition module, a fingerprint feature adjustment module, a fingerprint simulation module, and a fingerprint image storage module.

[0157] Among them, the fingerprint collection module is used to collect the initial fingerprint. The collected fingerprint will be used as a template and adjusted according to subsequent test requirements.

[0158] The fingerprint feature adjustment module is used to adjust the features of the collected initial fingerprint, such as adjusting the depth of the fingerprint texture, adjusting the fingerprint size, adjusting the fingerprint wear, etc.

[0159] The fingerprint simulation module is used to receive the fingerprint feature adjustment module signal, simulate it, and output the simulation signal, such as shallow texture fingerprint and wear fingerprint, so that the simulated fingerprint generation system can operate normally and adapt to different working scenarios.

[0160] The fingerprint image storage module is used to store the simulation data generated by the fingerprint simulation module, providing a shortcut for subsequent testing.

[0161] The following uses the automated testing of vehicle fingerprint unlocking in multiple postures, the automated testing of difficult fingerprint recognition rate, and the automated testing of fingerprint unlocking capacity as examples to illustrate the working process of the application architecture.

[0162] When conducting automated testing of fingerprint unlocking with multiple postures, first set the parameters of the same fingerprint in multiple postures through the host computer control interface in the test management system. By setting multiple unlocking postures, simulate the fingerprint unlocking conditions under different fingerprint postures when manually unlocking the vehicle. For example, simulate the parameters corresponding to the forward fingerprint, side fingerprint, reverse fingerprint and other unlocking postures.

[0163] Then, the set parameters are transmitted to the simulated fingerprint generation system through the data stream. The fingerprint feature adjustment module of the fingerprint simulation generation system adjusts the collected initial fingerprint according to the received parameters. For example, the rotation angle and tilt angle of the fingerprint when pressed in different postures are simulated by perspective transformation technology. The fingerprint simulation module simulates according to the adjusted fingerprint features and converts them into simulated data to be transmitted to the fingerprint recognition controller for fingerprint unlocking operation.

[0164] Finally, after the fingerprint images in different directions pass through the fingerprint recognition controller, the feedback signal of the fingerprint recognition controller is observed through the host computer control interface to analyze the accuracy of vehicle fingerprint unlocking.

[0165] When conducting automated testing of difficult fingerprint recognition rates, first set the parameters of the same fingerprint in different states through the host computer control interface in the test management system. By setting multiple fingerprint states, simulate the fingerprint unlocking situation of manually unlocking the vehicle in different scenarios. For example, simulate the parameters corresponding to difficult fingerprints such as worn fingerprints and shallow texture fingerprints.

[0166] Next, the set parameters are transmitted to the simulated fingerprint generation system through the data stream. The fingerprint feature adjustment module of the fingerprint simulation generation system adjusts the collected initial fingerprint according to the received parameters. Taking the simulation of worn fingerprints as an example, by setting different parameters of fingerprint wear, the fingerprint is simulated to reduce the details due to long-term use or wear. Optionally, the width of the fingerprint lines can be reduced by corrosion operation, or the irregular reflection and scattering caused by wear on the finger surface can be simulated by adding noise, and severely worn fingerprints can be simulated by randomly adding scratches or randomly erasing lines. The fingerprint simulation module simulates according to the adjusted fingerprint features, and converts them into simulated data for transmission to the fingerprint recognition controller to perform fingerprint unlocking operations.

[0167] Finally, after the fingerprint images with different degrees of wear and different texture depths pass through the fingerprint recognition controller, the feedback signal of the fingerprint recognition controller is observed through the host computer control interface to analyze the recognition rate of vehicle fingerprint unlocking.

[0168] When performing the automated test of fingerprint unlocking capacity, firstly, different fingerprints are entered through the host computer control interface in the test management system. Optionally, the entered fingerprints can be simulated fingerprints stored in the fingerprint image storage module for automated testing of fingerprint multiple posture unlocking and automated testing of difficult fingerprint recognition rate, or fingerprints with different parameters regenerated by the fingerprint simulation generation system to simulate the entry of vehicle fingerprints.

[0169] Exemplarily, the simulated fingerprints are transmitted to the fingerprint recognition controller in order of generation time from earliest to latest for fingerprint memory. After the simulated fingerprints with different parameters are entered into the fingerprint recognition controller, the memory quantity is observed to analyze the capacity of vehicle fingerprint unlocking.

[0170] Reference Fig.11 The embodiment of the present application also provides a fingerprint unlocking automatic testing system, which can implement the above-mentioned fingerprint unlocking automatic testing method, and the system includes:

[0171] A first module is used to determine a target item from preset automated test items in response to an automated test instruction, wherein the automated test items include an unlocking posture test and a difficult fingerprint recognition test;

[0172] The second module is used to obtain an initial fingerprint image;

[0173] The third module is used to extract feature parameters from the initial fingerprint image to obtain a first fingerprint feature, wherein the first fingerprint feature includes fingerprint size, fingerprint posture, texture clarity and texture wear;

[0174] A fourth module is used to adjust the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features;

[0175] A fifth module, used to obtain a corresponding simulated fingerprint image according to each simulated fingerprint feature in the simulated fingerprint feature set;

[0176] The sixth module is used to input each simulated fingerprint image into the fingerprint recognition controller for recognition with the initial fingerprint image to obtain a feedback signal;

[0177] The seventh module is used to perform data analysis based on preset test indicators and all feedback signals to obtain automated test results for fingerprint unlocking.

[0178] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0179] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned fingerprint unlocking automatic test method when executing the computer program. The electronic device can be any intelligent terminal including a vehicle-mounted computer.

[0180] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0181] See also Fig.12 , Fig.12 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0182] The processor 110 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0183] The memory 120 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 120 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 120, and the processor 110 calls and executes the fingerprint unlocking automatic test method of the embodiment of this application.

[0184] The input / output interface 130 is used to implement information input and output.

[0185] The communication interface 140 is used to realize communication interaction between this device and other devices. Communication can be realized through wired methods (such as USB, network cable, etc.) or wireless methods (such as mobile network, WIFI, Bluetooth, etc.).

[0186] The bus 150 transmits information between various components of the device (eg, the processor 110 , the memory 120 , the input / output interface 130 , and the communication interface 140 ).

[0187] The processor 110 , the memory 120 , the input / output interface 130 , and the communication interface 140 are connected to each other in communication within the device via the bus 150 .

[0188] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned fingerprint unlocking automatic testing method is implemented.

[0189] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0190] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0191] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0192] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0193] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0194] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0195] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0196] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A fingerprint unlocking automated testing method, characterized in that: The method comprises the following steps: In response to the automated test instruction, determining a target item from preset automated test items, wherein the automated test items include an unlocking posture test and a difficult fingerprint recognition test; Obtaining an initial fingerprint image; Extracting characteristic parameters from the initial fingerprint image to obtain a first fingerprint feature, wherein the first fingerprint feature includes fingerprint size, fingerprint posture, texture clarity, and texture wear; Adjusting the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features; Obtaining a corresponding simulated fingerprint image according to each of the simulated fingerprint features in the simulated fingerprint feature set; Input each of the simulated fingerprint images into a fingerprint recognition controller for recognition with the initial fingerprint image to obtain a feedback signal; Data analysis is performed based on preset test indicators and all the feedback signals to obtain automated test results for fingerprint unlocking.

2. The method according to claim 1, characterized in that The step of adjusting the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set includes the following steps: When the target item is the unlocking posture test, the fingerprint size and the fingerprint posture in the first fingerprint feature are adjusted to obtain a plurality of different simulated fingerprint features; All the simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

3. The method according to claim 1, characterized in that The step of adjusting the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set includes the following steps: When the target item is the difficult fingerprint recognition test, the texture clarity and the texture wear degree in the first fingerprint feature are adjusted to obtain a plurality of different simulated fingerprint features; All the simulated fingerprint features are integrated to obtain a simulated fingerprint feature set.

4. The method according to claim 2, characterized in that: The step of adjusting the fingerprint size and the fingerprint posture in the first fingerprint feature to obtain a plurality of different simulated fingerprint features comprises the following steps: The fingerprint size in the first fingerprint feature is adjusted by scaling the ridges to obtain a plurality of second fingerprint features of different fingerprint sizes; By adjusting the fingerprint posture in the first fingerprint feature by changing the angle of the ridges, a third fingerprint feature with multiple different fingerprint posture directions is obtained; The second fingerprint feature and the third fingerprint feature are determined as simulated fingerprint features.

5. The method according to claim 3, characterized in that: The step of adjusting the texture clarity and the texture wear degree in the first fingerprint feature to obtain a plurality of different simulated fingerprint features comprises the following steps: By changing the depth of the grain lines to adjust the texture clarity in the first fingerprint feature, a plurality of fourth fingerprint features with different texture clarity are obtained; The texture wear degree in the first fingerprint feature is adjusted by increasing or decreasing the width of the grain line to obtain a plurality of fifth fingerprint features with different texture wear degrees; The fourth fingerprint feature and the fifth fingerprint feature are determined as simulated fingerprint features.

6. The method according to claim 1, characterized in that The step of performing data analysis according to the preset test indicators and all the feedback signals to obtain the automated test results of fingerprint unlocking includes the following steps: Counting the number of successful recognitions and the total number of recognitions of the feedback signal to determine the recognition success rate; Compare the recognition success rate with a preset test index to determine whether the recognition success rate is greater than the test index; When the recognition success rate is greater than the test index, it is determined that the automated test result of fingerprint unlocking is functioning normally.

7. The method according to claim 1, characterized in that The automated test project also includes a fingerprint unlocking capacity test, and the method further includes the following steps: The simulated fingerprint images corresponding to the simulated fingerprint feature set are sequentially memorized in the order in which the images are generated; The automated test result of the fingerprint unlocking capacity is obtained according to the memory quantity of the simulated fingerprint image.

8. A fingerprint unlocking automatic test system, characterized in that: The system comprises: A first module is used to determine a target item from preset automated test items in response to an automated test instruction, wherein the automated test items include an unlocking posture test and a difficult fingerprint recognition test; The second module is used to obtain an initial fingerprint image; A third module is used to extract characteristic parameters from the initial fingerprint image to obtain a first fingerprint feature, wherein the first fingerprint feature includes fingerprint size, fingerprint posture, texture clarity and texture wear; A fourth module is used to adjust the first fingerprint feature according to the target project to obtain a simulated fingerprint feature set, wherein the simulated fingerprint feature set includes a plurality of different simulated fingerprint features; A fifth module, configured to obtain a corresponding simulated fingerprint image according to each of the simulated fingerprint features in the simulated fingerprint feature set; A sixth module is used to input each of the simulated fingerprint images into a fingerprint recognition controller for recognition with the initial fingerprint image to obtain a feedback signal; The seventh module is used to perform data analysis based on preset test indicators and all the feedback signals to obtain automated test results for fingerprint unlocking.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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