Fingerprint image verification method and device, computer device and storage medium

Abstract

The application relates to the technical field of intelligent locks and discloses a fingerprint image verification method and device, computer equipment and a storage medium, the method comprises the following steps: detecting a pressing operation of a user on a curved surface coating; when the pressing operation is detected, acquiring fingerprint images collected by a plurality of fingerprint sensors on the curved surface coating; matching a first fingerprint image collected by a first sensor in the fingerprint sensors with a first fingerprint template associated with the first sensor to obtain a first matching number; and triggering a hierarchical verification mechanism according to the first matching number to verify the remaining fingerprint images and obtaining a verification result. The application solves the problems that a planar fingerprint module has poor adhesion, the sensor area is small, registration is complicated and identification is prone to failure.

Description

Technical Field

[0001] This invention relates to the field of smart lock technology, specifically to fingerprint image verification methods, devices, computer equipment, and storage media. Background Technology

[0002] In the field of smart locks, fingerprint recognition technology, as an important authentication method, is widely used in various smart devices. However, current fingerprint recognition modules are typically planar in design, with the fingerprint sensor being much smaller than the overall module size and located in the center of the acquisition surface. This design means that each time a finger is pressed, only a fingerprint area equivalent to the sensor size can be acquired, while other areas of the acquisition surface cannot effectively acquire fingerprint information. Therefore, during the fingerprint registration stage, users need to move their fingers multiple times to acquire multiple fingerprint images, increasing the complexity and time cost of the registration process. Furthermore, during the fingerprint recognition stage, due to the limited recognition area, not enough fingerprints can be acquired for recognition, requiring users to move their fingers multiple times to meet the verification requirements, resulting in low recognition efficiency. This further affects the convenience of using the fingerprint recognition function, making the entire fingerprint recognition process significantly inadequate in practical applications.

[0003] Existing fingerprint modules suffer from poor fit and small, single-sensor design. The planar acquisition surface design fails to adequately consider the natural curvature of the finger, leading to issues like light, heavy, or off-center presses during acquisition, impacting accuracy and user experience. Furthermore, the single-sensor design limits the fingerprint acquisition area and the amount of feature information, increasing the number of scans required during registration and reducing success rate and efficiency during recognition. These problems severely restrict the development and application of fingerprint recognition technology. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a fingerprint image verification method, apparatus, computer equipment, and storage medium to solve the problems of poor fit of planar fingerprint modules and small sensor area leading to cumbersome registration and easy recognition failure.

[0005] In a first aspect, embodiments of the present invention provide a fingerprint image verification method, the method comprising:

[0006] Test the user's pressing operation on the curved surface coating;

[0007] When a press operation is detected, fingerprint images are acquired by multiple fingerprint sensors on the curved surface coating.

[0008] The first fingerprint image acquired by the first sensor in the fingerprint sensor is matched with the first fingerprint template associated with the first sensor to obtain the first matching number;

[0009] According to the first matching number, a hierarchical verification mechanism is triggered to verify the remaining fingerprint images, and a verification result is obtained.

[0010] Further, before detecting the pressing operation of the user on the curved cover coating, the method further comprises:

[0011] Obtaining original fingerprint images collected by a plurality of fingerprint sensors on the curved cover coating;

[0012] According to a preset enrollment standard, the original fingerprint images are screened to obtain a plurality of standard fingerprint images;

[0013] A plurality of preset feature points of the standard fingerprint images are extracted, a fingerprint template is constructed using the preset feature points, and the fingerprint template is stored in association with the corresponding fingerprint sensor in a template storage space.

[0014] Further, the matching of the first fingerprint image collected by the first sensor in the fingerprint sensors with the first fingerprint template associated with the first sensor to obtain the first matching number comprises:

[0015] Iterating through a preset number corresponding to the fingerprint sensor;

[0016] Determining a first sensor of a first priority according to the preset number;

[0017] Obtaining the first fingerprint image collected by the first sensor and the corresponding first fingerprint template;

[0018] Extracting a plurality of first feature points in the first fingerprint image, and matching the first feature points with preset feature points in the first fingerprint template to obtain the first matching number.

[0019] Further, according to the first matching number, a hierarchical verification mechanism is triggered to verify the remaining fingerprint images, and a verification result is obtained, which comprises:

[0020] Comparing the first matching number with a preset matching number;

[0021] If the first matching number does not reach the preset matching number, the hierarchical verification mechanism is triggered to verify the remaining fingerprint images, and a verification result is obtained;

[0022] Or, if the first matching number reaches the preset matching number, it is determined that the verification result is a fingerprint verification success.

[0023] Further, the triggering of the hierarchical verification mechanism to verify the remaining fingerprint images to obtain the verification result comprises:

[0024] Obtaining a distribution state of the remaining sensors in the fingerprint sensors;

[0025] If the distribution state is a plurality of facet array sensors, a verification operation of the remaining fingerprint images is performed based on a first strategy to obtain a first verification result;

[0026] If the distribution state is a ring-shaped sensor, a verification operation of the remaining fingerprint images is performed based on a second strategy to obtain a second verification result.

[0027] Further, the verification operation of the remaining fingerprint images based on the first strategy to obtain the first verification result comprises:

[0028] Determining a second sensor of the next priority according to a preset number corresponding to the fingerprint sensor;

[0029] Obtaining a second fingerprint image collected by the second sensor and a corresponding second fingerprint template;

[0030] Extracting a plurality of second feature points in the second fingerprint image, and matching the second feature points with preset feature points in the second fingerprint template to obtain a second matching number;

[0031] Calculating a sum value of the first matching number and the second matching number, and determining whether the sum value reaches a first verification threshold;

[0032] If the first verification threshold is reached, it is determined that the first verification result is a fingerprint verification success, and the matching operation is terminated, or if the first verification threshold is not reached, the matching operation is repeated until the fingerprint sensor is matched and the first verification threshold is not reached, and it is determined that the first verification result is a fingerprint verification failure.

[0033] Further, the verification operation of the remaining fingerprint images based on the second strategy to obtain the second verification result comprises:

[0034] Obtaining a third fingerprint image collected by the ring-shaped sensor and a corresponding third fingerprint template;

[0035] Extracting a plurality of third feature points in the third fingerprint image, and classifying the third feature points according to a preset area to obtain a feature point set corresponding to each area;

[0036] Matching the feature point set corresponding to each area with third preset feature points in the third fingerprint template to obtain a plurality of matching areas;

[0037] Determining whether the number of areas of the matching areas reaches a third verification threshold;

[0038] If the third verification threshold is reached, the second verification result is determined as a success of the fingerprint verification, or if the third verification threshold is not reached, the second verification result is determined as a failure of the fingerprint verification.

[0039] In a second aspect, an embodiment of the present application provides a fingerprint image verification device, the device comprising:

[0040] a detection module configured to detect a pressing operation of a user on the curved cover coating;

[0041] a obtaining module configured to obtain fingerprint images collected by a plurality of fingerprint sensors on the curved cover coating when the pressing operation is detected;

[0042] a matching module configured to match a first fingerprint image collected by a first sensor of the fingerprint sensors with a first fingerprint template associated with the first sensor to obtain a first matching number;

[0043] a triggering module configured to trigger a hierarchical verification mechanism according to the first matching number to verify the remaining fingerprint images to obtain a verification result.

[0044] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory and a processor, the memory and the processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the method of the first aspect or any of the corresponding embodiments thereof.

[0045] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium stores computer instructions, and the computer instructions are used to make a computer perform the method of the first aspect or any of the corresponding embodiments thereof.

[0046] The method provided by the embodiments of the present application has the following beneficial effects:

[0047] The method provided by the embodiments of the present application detects the pressing operation of the curved cover coating, adapts to the natural curvature of the finger, improves the comfort and accuracy of user operation, and reduces the situation of pressing deviation. For the problem of small sensor area, multiple fingerprint sensors can collect more fingerprint information, enrich fingerprint features, and improve identification accuracy and success rate. The matching number obtained by matching the fingerprint image with the template provides a basis for hierarchical verification, improves verification efficiency, and reduces unnecessary steps. When the matching number does not reach the preset value, the hierarchical verification mechanism is triggered, different verification strategies are adopted for different types of sensors by taking advantage of multiple sensors, the fingerprint verification accuracy and reliability are improved, the misjudgment rate is reduced, and the problem of easy failure of planar fingerprint module identification is solved. The original fingerprint image is filtered by the preset registration standard, the quality of the template is ensured, the interference is reduced, the fingerprint template is associated and stored with the sensor, the registration and verification efficiency is improved, and the fingerprint template is quickly called.

[0048] The method provided by the embodiment of the present application is aimed at the distribution state of multiple facet array sensors, adopts a first strategy to sequentially determine the priority sensors for matching, increases the matching quantity to improve the verification accuracy, can reduce the verification failure caused by the inaccuracy of a single sensor, and more comprehensively analyzes the fingerprint features. For the distribution state of the ring-shaped sensor, a second strategy is adopted to classify and match the feature points according to the preset area, improves the verification accuracy, and the specific verification strategy for different sensor types can better adapt to various fingerprint collection conditions, and improves the fingerprint recognition success rate and reliability. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the specific embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0050] Figure 1 is a flowchart of a fingerprint image verification method according to an embodiment of the present application;

[0051] Figure 2 is a structural schematic diagram of an existing fingerprint identification module according to an embodiment of the present application;

[0052] Figure 3 is a structural schematic diagram of a facet array sensor layout according to an embodiment of the present application;

[0053] Figure 4 is a structural schematic diagram of a ring-shaped sensor layout according to an embodiment of the present application;

[0054] Figure 5 is a flowchart of a facet array sensor hierarchical verification strategy according to an embodiment of the present application;

[0055] Figure 6 is a flowchart of a ring-shaped sensor hierarchical verification strategy according to an embodiment of the present application;

[0056] Figure 7 is a structural block diagram of a fingerprint image verification device according to an embodiment of the present application;

[0057] Figure 8 is a hardware structural schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0059] According to the embodiments of the present application, a fingerprint image verification method, device, computer equipment and storage medium are provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0060] In the present embodiment, a fingerprint image verification method is provided, Figure 1 is a flowchart of the fingerprint image verification method according to the embodiments of the present application, as Figure 1 shown, the flow includes the following steps:

[0061] Step S11, detecting a pressing operation of a user on the curved cover coating.

[0062] In the embodiments of the present application, the curved cover coating refers to a protective layer with a curved surface covering the fingerprint sensor. On the one hand, it plays a role in protecting the fingerprint sensor, preventing the sensor from being damaged by external environment such as dust, moisture, scratching, etc. On the other hand, the curved design of the curved cover coating can better adapt to the natural curvature of the finger, so that the user's finger is more closely contacted with the coating during the pressing operation, improving the comfort and accuracy of the user's operation, and reducing the occurrence of pressing light, pressing heavy or pressing bias. At the same time, the curved cover coating has special material or processing technology to ensure that the fingerprint sensor can accurately collect fingerprint information and will not have negative impact on the quality of the fingerprint image.

[0063] In the embodiments of the present application, before detecting the pressing operation of the user on the curved cover coating, the method further includes the following steps A1-A3:

[0064] Step A1, obtaining original fingerprint images collected by a plurality of fingerprint sensors on the curved cover coating.

[0065] Specifically, in the early stage of fingerprint identification, a plurality of fingerprint sensors on the curved cover coating are started, including a central main sensor and a small facet array or ring-shaped partial sensor. The curved multi-sensor can collect fingerprints from different angles, and the user's finger is placed on the sensor at the same time, obtaining rich fingerprint feature information to provide a basis for subsequent processing.

[0066] Step A2, screening the original fingerprint image according to the preset entry standard to obtain a plurality of standard fingerprint images.

[0067] Specifically, after obtaining the original fingerprint image, the image is screened according to the standards of image definition, integrity, contrast, etc. For example, the minimum resolution is set, the integrity of the lines is ensured, and the difference between the lines and the background is required to be large, etc. The poor images are removed to obtain standard fingerprint images for constructing templates, thereby improving the recognition accuracy and reliability.

[0068] Step A3, extracting a plurality of preset feature points of the standard fingerprint image, and constructing a fingerprint template using the preset feature points, and storing the fingerprint template and the corresponding fingerprint sensor in the template storage space.

[0069] Specifically, the detail features such as end points and bifurcation points and the global features such as line directions of the standard fingerprint image are extracted and converted into representative data. The fingerprint template is constructed using these feature points, and the template is encoded and combined into a feature vector. The template is stored in an independent space in association with the corresponding sensor, so that the corresponding template can be quickly found during fingerprint recognition, the comparison efficiency is improved, the template confusion is avoided, and the accuracy is ensured.

[0070] Step S12, when the pressing operation is detected, acquiring the fingerprint images collected by the plurality of fingerprint sensors on the curved surface covering coating.

[0071] It should be noted that, as shown in Figure 2 The existing fingerprint recognition module is commonly square or circular, and the size of the fingerprint sensor is usually much smaller than that of the curved surface covering coating. The fingerprint sensor is generally located at the center position of the fingerprint collection surface. Each time the finger is pressed, the fingerprint is collected by the sensor. The area of the collected fingerprint is less than or equal to the size of the sensor chip, and the fingerprint cannot be collected in other areas of the collection surface.

[0072] In the embodiments of the present application, as shown in Figure 3 The central main sensor is numbered first, and the others are numbered in turn. When the user presses, the small facet array sensor works simultaneously, and can collect multiple images of different parts of the finger, thereby increasing the richness of the fingerprint features and providing detailed information for subsequent identification. As shown in Figure 4 In the ring sensor configuration, the central large sensor is numbered first, and the ring sensor is numbered second. One pressing of the user can realize the collection of multiple images by the double sensors. The ring sensor can collect images in all directions around the finger, thereby improving the quality and integrity of the fingerprint images.

[0073] Step S13, matching the first fingerprint image collected by the first sensor in the fingerprint sensor with the first fingerprint template associated with the first sensor to obtain a first matching number.

[0074] In the embodiments of the present application, step S13 includes the following steps B1-B4:

[0075] Step B1, traverse the preset number corresponding to the fingerprint sensor.

[0076] Specifically, in the process of fingerprint image matching, it is necessary to traverse the preset number corresponding to the fingerprint sensor first. Since multiple fingerprint sensors are placed in the curved coating and numbered in a certain order, by traversing these numbers, each sensor can be processed and analyzed in turn. For example, for the layout of the facet array sensor, the central area main sensor is numbered as the first sensor, and other facet array sensors are numbered in turn. For the configuration of the ring-shaped sensor, the central area large sensor is numbered first, and the ring-shaped sensor is numbered second. The process of traversing the number can ensure that the system does not miss any sensor, so as to comprehensively process the fingerprint image.

[0077] Step B2, determine the first sensor of the first priority according to the preset number.

[0078] Specifically, after traversing the number, the first sensor of the first priority is determined according to the preset numbering rule. The main sensor in the central area is usually given a higher priority because it plays a leading role in the fingerprint collection process. The first priority sensor is used as the starting point for fingerprint image matching. For example, in the layout of the facet array sensor, the central main sensor is the first priority sensor, and the fingerprint image collected by it contains more key features.

[0079] Step B3, obtain the first fingerprint image collected by the first sensor and the corresponding first fingerprint template.

[0080] Specifically, after determining the first priority sensor, the first fingerprint image collected by the sensor and the corresponding first fingerprint template are obtained. The first fingerprint image is the real-time fingerprint image collected by the first sensor when the user presses the fingerprint module. The first fingerprint template is constructed by extracting feature points from the original fingerprint image collected by the sensor in the fingerprint entry stage according to the preset standard, and is stored in the template storage space associated with the sensor. Obtaining these two data is to perform subsequent feature point matching to determine the similarity between the first fingerprint image and the first fingerprint template.

[0081] Step B4, extract a plurality of first feature points in the first fingerprint image, and match the first feature points with preset feature points in the first fingerprint template to obtain a first matching number.

[0082] Specifically, after obtaining the first fingerprint image and the first fingerprint template, a plurality of first feature points in the first fingerprint image are extracted. The feature points can be detailed features of the fingerprint, such as end points, bifurcation points, etc., or global features, such as the direction and frequency of the lines, etc. Then, the first feature points are matched with preset feature points in the first fingerprint template. By comparing the feature points in the two sets, the similarity between them is determined, and the first matching number, i.e., the number of feature points in the first fingerprint image that match the feature points in the first fingerprint template, is obtained. The number will be one of the bases for subsequent judgment of whether the fingerprint verification is successful.

[0083] In step S14, the hierarchical verification mechanism is triggered according to the first matching number to verify the remaining fingerprint images and obtain a verification result.

[0084] In the embodiments of the present application, step S14 includes the following steps C1-C3:

[0085] In step C1, the first matching number is compared with a preset matching number.

[0086] Specifically, the first matching number is compared with the preset matching number. The first matching number is the feature point matching number obtained by matching the first fingerprint image collected by the first sensor with the first fingerprint template. The preset matching number is a threshold value preset according to design requirements and performance indicators. By comparing the two numbers, the progress of the fingerprint verification can be preliminarily judged. If the first matching number reaches or exceeds the preset matching number, it means that the fingerprint verification has a high probability of success in the first stage; if the first matching number does not reach the preset matching number, the hierarchical verification mechanism needs to be further triggered to more comprehensively evaluate the authenticity of the fingerprint.

[0087] In step C2, if the first matching number does not reach the preset matching number, the hierarchical verification mechanism is triggered to verify the remaining fingerprint images and obtain a verification result.

[0088] It should be noted that the hierarchical verification mechanism is started when the first matching number does not reach the preset value. Different strategies are adopted according to the types of the remaining sensors. The small-area sensor determines the next priority sensor according to the first strategy, and the matching feature point judgment threshold value determines the result; the ring-shaped sensor determines the result of the matching feature point judgment threshold value according to the second strategy. This mechanism uses the advantages of different sensors to verify the fingerprint from multiple angles, improving the accuracy and reliability.

[0089] Specifically, the hierarchical verification mechanism is triggered to verify the remaining fingerprint images and obtain a verification result, including the following steps D1-D3:

[0090] In step D1, the distribution state of the remaining sensors in the fingerprint sensor is obtained.

[0091] Specifically, the remaining sensor refers to the sensor that has not participated in matching after the first sensor completes the matching of the fingerprint image. The distribution state includes but is not limited to the distribution state of the plurality of small facet array sensors and the distribution state of the ring-shaped sensor. If it is a plurality of small facet array sensors, it is in a state of being scattered at different positions of the curved coating so as to collect fingerprint images from different angles; if it is a ring-shaped sensor, it is distributed around the central area large sensor to form a ring-shaped structure, which can collect fingerprint information of the finger in all directions.

[0092] Step D2, if the distribution state is a plurality of small facet array sensors, performing a verification operation on the remaining fingerprint images based on a first strategy to obtain a first verification result.

[0093] As an example, as shown in Figure 5 The first strategy includes: when the finger presses the fingerprint module, starting the identification process. A plurality of sensors simultaneously collect fingerprint images of the finger, and then perform image processing to extract key information. Then, in sequence, first preferentially using the large facet array sensor to compare the feature points from the first sensor template, if the threshold is not reached, combining the feature points of the next sensor template to continue comparison, and so on, until the feature points reach the threshold to enter the next process or all sensor template feature points are compared and still do not reach the threshold. Finally, if the feature points reach the threshold during the comparison process, the identification is passed, otherwise the identification is not passed.

[0094] Specifically, step D2 includes steps D21-D25:

[0095] Step D21, determining a second sensor of the next priority according to a preset number corresponding to the fingerprint sensor.

[0096] Specifically, when performing hierarchical verification on a plurality of small facet array sensors, the next priority sensor needs to be determined first. Since each sensor has a corresponding preset number, the order and priority of the sensor can be determined by the number. After the matching of the first sensor has been completed, if the first matching number does not reach the preset matching number, the second sensor of the next priority needs to be determined according to the number order. For example, the central area main sensor is the first sensor, and the small facet array sensors around it are numbered in order. When the second sensor is determined, the next sensor that should be matched is found according to this number order.

[0097] Step D22, acquiring a second fingerprint image collected by the second sensor and a corresponding second fingerprint template.

[0098] Specifically, after the second sensor is determined, a second fingerprint image collected by the second sensor is acquired. The image is the fingerprint information collected by the second sensor in real time when the user presses the fingerprint module. Meanwhile, a corresponding second fingerprint template also needs to be acquired. The second fingerprint template is constructed according to the feature points extracted from the original fingerprint image collected by the second sensor in the fingerprint input stage according to a preset standard, and is stored in the template storage space in association with the second sensor. The two data are acquired to perform subsequent feature point matching.

[0099] Step D23, a plurality of second feature points in the second fingerprint image are extracted, and the second feature points are matched with the preset feature points in the second fingerprint template to obtain a second matching number.

[0100] Specifically, after the second fingerprint image and the second fingerprint template are acquired, a plurality of second feature points in the second fingerprint image are extracted. The feature points can include the detail features and the global features of the fingerprint. The second feature points are matched with the preset feature points in the second fingerprint template. By comparing the feature points in the two sets, the similarity between them is determined, and thus the second matching number, i.e., the number of feature points in the second fingerprint image matched with the feature points in the second fingerprint template, is obtained.

[0101] Step D24, a sum of the first matching number and the second matching number is calculated, and it is judged whether the sum reaches a first verification threshold.

[0102] Specifically, after the second matching number is obtained, it is summed with the first matching number. The sum represents the comprehensive matching degree of the two sensors that have participated in matching. Then, it is judged whether the sum reaches the first verification threshold. The first verification threshold judges whether the fingerprint verification is successful. If the sum reaches the first verification threshold, it means that the matching degree of the fingerprint is high, and there is a high possibility that the fingerprint is a real fingerprint.

[0103] Step D25, if the first verification threshold is reached, it is determined that the first verification result is that the fingerprint verification is successful, and the matching operation is terminated, or if the first verification threshold is not reached, the matching operation is repeated until the fingerprint sensors are all compared and the first verification threshold is not reached, and it is determined that the first verification result is that the fingerprint verification fails.

[0104] Specifically, if the sum of the fingerprints reaches the first verification threshold, the first verification result is determined to be successful fingerprint verification, and the matching operation terminates. This is because the verification requirements have been met, and there is no need to match other sensors. If the sum of the fingerprints does not reach the first verification threshold, the matching operation continues, determining the next priority sensor according to its number, repeating steps D21-D23, recalculating the sum of the fingerprints, and determining whether the first verification threshold has been reached. This process continues until all fingerprint sensors have been compared and the first verification threshold has still not been reached, at which point the first verification result is determined to be fingerprint verification failure.

[0105] Step D3: If the distribution state is a ring sensor, then perform the verification operation of the remaining fingerprint images based on the second strategy to obtain the second verification result.

[0106] As an example, such as Figure 6 As shown, the second strategy includes: when a finger presses the fingerprint module, the fingerprint recognition process is initiated. Multiple sensors work simultaneously to acquire fingerprint images, with the large-area sensor taking priority and other sensors such as the ring sensor assisting. Next, image processing is performed to extract key feature points. Then, comparison begins with the feature points of the first sensor template from the large-area sensor. If the threshold is not reached, comparison continues by combining the feature points from the ring sensor template, until the feature points reach the threshold and proceed to the next step, or until the threshold is still not reached after comparing all sensor template feature points. Finally, if the feature points in the comparison reach the threshold, recognition is successful; otherwise, it fails.

[0107] Specifically, step D3 includes the following steps D31-D35:

[0108] Step D31: Obtain the third fingerprint image and the corresponding third fingerprint template collected by the ring sensor.

[0109] Specifically, when the remaining sensors are distributed in a ring pattern, the third fingerprint image acquired by the ring sensor is first obtained. This image is the fingerprint information collected in real time by the ring sensor when the user presses the fingerprint module. Simultaneously, the corresponding third fingerprint template is acquired. The third fingerprint template is constructed during the fingerprint enrollment stage by extracting feature points from the original fingerprint image acquired by the ring sensor according to preset standards, and is associated with the ring sensor and stored in the template storage space.

[0110] Step D32: Extract multiple third feature points from the third fingerprint image, and classify the third feature points according to preset regions to obtain the feature point set corresponding to each region.

[0111] Specifically, after the third fingerprint image is acquired, a plurality of third feature points in the third fingerprint image are extracted. The feature points can include both the detail features and the global features of the fingerprint. Then, the third feature points are classified according to preset regions. The preset regions can be divided according to the structural features of the ring-shaped sensor and the distribution law of the fingerprint, for example, the ring-shaped sensor can be divided into a plurality of fan-shaped regions or ring-shaped regions. Through the classification operation, a feature point set corresponding to each region is obtained.

[0112] Step D33, matching the feature point set corresponding to each region with the third preset feature points in the third fingerprint template to obtain a plurality of matching regions.

[0113] Specifically, the feature point set corresponding to each region is matched with the third preset feature points in the third fingerprint template respectively. The third preset feature points are feature points extracted when the third fingerprint template is constructed, and correspond to the feature points in the third fingerprint image. Through the matching operation, it can be determined which regions of feature points match the feature points in the template, thereby obtaining a plurality of matching regions.

[0114] Step D34, determining whether the number of regions of the matching regions reaches a third verification threshold.

[0115] Specifically, the number of regions of the matching regions is counted to determine whether the number of regions of the matching regions reaches a third verification threshold. The third verification threshold is used to determine whether the fingerprint verification of the ring-shaped sensor is successful.

[0116] Step D35, if the third verification threshold is reached, determining that the second verification result is that the fingerprint verification is successful, or if the third verification threshold is not reached, determining that the second verification result is that the fingerprint verification fails.

[0117] Specifically, if the number of regions of the matching regions reaches the third verification threshold, it indicates that the matching degree of the fingerprint collected by the ring-shaped sensor and the template is high, and it is determined that the second verification result is that the fingerprint verification is successful. If the third verification threshold is not reached, it indicates that the matching degree is insufficient, and it is determined that the second verification result is that the fingerprint verification fails.

[0118] In the embodiments of the present application, the method further includes: monitoring the working state of the ring-shaped sensor; if the working state is a fault state, detecting a fault region of the ring-shaped sensor, and adjusting the comparison region of the second matching strategy to be a non-fault region; if the number of hits of the non-fault region reaches a second verification threshold, determining that the verification is successful, or if the second verification threshold is not reached, determining that the verification fails.

[0119] Step C3, if the first matching number reaches a preset matching number, determining that the verification result is that the fingerprint verification is successful.

[0120] Specifically, if the first matching number reaches the preset matching number, it indicates that the matching degree of the first fingerprint image collected by the first sensor and the first fingerprint template is relatively high, and the authenticity of the fingerprint can be determined in the first stage. At this time, the hierarchical verification mechanism is not required, and the verification result is directly determined as fingerprint verification success.

[0121] In the embodiment, a mechanical structure-based curved surface coating adjustment method is provided, and the flow includes the following steps:

[0122] Step S21, detecting first pressure data of the curved surface coating;

[0123] Step S22, calculating an adjustment height of each micro telescopic strut in the mechanical structure below the curved surface coating according to the first pressure data;

[0124] Step S23, controlling the curved surface coating to perform a moving operation based on the adjustment height of each micro telescopic strut;

[0125] Step S24, detecting second pressure data of the adjusted curved surface coating, and judging whether the current curved surface coating is uniformly stressed according to the second pressure data;

[0126] Step S25, if the current curved surface coating is uniformly stressed, storing a state parameter of each micro telescopic strut to a preset information base.

[0127] In the embodiment, the mechanical structure below the curved surface coating is composed of a plurality of micro telescopic struts and a pressure sensing module, and is installed between the curved surface coating and the fingerprint sensor. When a user's finger presses on the curved surface coating, the pressure sensing module quickly detects the pressure distribution of the finger, and activates the micro telescopic struts to make corresponding adjustments according to the pressure data.

[0128] It should be noted that the micro telescopic struts are made of high-strength but light-weight alloy materials, and have good durability and stability. The diameter of each strut is about several millimeters, and the height can be adjusted within a certain range. The inside of the strut adopts a precise spiral spring and a micro motor driving system. When receiving the signal of the pressure sensing module, the micro motor rotates according to the instruction, and adjusts the height of the strut through the compression or expansion of the spiral spring. A plurality of micro telescopic struts are uniformly distributed below the curved surface coating to form a support grid, ensuring that the coating can be flexibly adjusted in all directions.

[0129] Specifically, when the user places a finger on the curved coating for fingerprint recognition, the pressure sensing module immediately detects the pressure data of the finger and transmits it to the central control system. The central control system calculates the adjustment height of the micro telescopic struts based on the pressure distribution, and then sends control instructions to the micro motor drive system. After receiving the instructions, the micro motor drive system quickly adjusts the height of the struts, so that the curved coating can conform to the shape of the finger, allowing the fingerprint sensor to more accurately capture the fingerprint image. For example, when the finger is thicker, the pressure sensing module will detect a larger pressure distribution range, and the central control system will adjust the strut height accordingly, causing the coating to protrude outward to adapt to the shape of the finger; when the finger is thinner, the central control system will adjust the strut height to make the coating shrink inward to ensure a tight fit with the finger. The design of this mechanical structure allows the curved coating to automatically adjust, providing users with a more comfortable and accurate fingerprint recognition experience.

[0130] In the embodiments of the present application, in addition to the above-mentioned curved coating adjustment method based on mechanical structure and related design, a small facet array sensor that can dynamically adjust its position or sensitivity is also used to optimize the collection efficiency, especially in the edge and tip areas of the finger, which are traditional collection difficulty areas. Through dynamic adjustment, the small facet array sensor can better adapt to the characteristics of different finger parts and obtain more accurate fingerprint information.

[0131] In addition, a near-infrared light source is integrated under the curved coating, realizing dual collection and identification of fingerprints and finger veins. Because vein identification is based on internal biological characteristics and is difficult to copy, this dual identification method provides more reliable protection for user identity verification. In the entire fingerprint identification process, all parts work together, from pressure detection to coating adjustment, to optimized collection by the sensor and the application of dual identification technology, all working together to create an efficient, accurate and secure fingerprint identification experience for users.

[0132] In the embodiments, a fingerprint image verification device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0133] The present embodiments provide a fingerprint image verification device, as shown in Figure 7 , comprising:

[0134] The detection module 71 is configured to detect a pressing operation of a user on the curved coating.

[0135] The acquisition module 72 is configured to acquire the fingerprint images collected by the plurality of fingerprint sensors on the curved coating when the pressing operation is detected.

[0136] The matching module 73 is configured to match the first fingerprint image collected by the first sensor of the fingerprint sensors with the first fingerprint template associated with the first sensor to obtain a first matching number.

[0137] The triggering module 74 is configured to trigger the hierarchical verification mechanism according to the first matching number to verify the remaining fingerprint images to obtain a verification result.

[0138] In an optional embodiment of the present application, the device further comprises a storage module configured to acquire original fingerprint images collected by the plurality of fingerprint sensors on the curved coating; filter the original fingerprint images according to a preset enrollment standard to obtain a plurality of standard fingerprint images; extract a plurality of preset feature points of the standard fingerprint images, and construct a fingerprint template using the preset feature points, and store the fingerprint template and the corresponding fingerprint sensor in the template storage space.

[0139] In an optional embodiment of the present application, the matching module 73 is configured to traverse the preset numbers corresponding to the fingerprint sensors; determine the first sensor of the first priority according to the preset numbers; acquire the first fingerprint image collected by the first sensor and the corresponding first fingerprint template; extract a plurality of first feature points in the first fingerprint image, and match the first feature points with the preset feature points in the first fingerprint template to obtain a first matching number.

[0140] In an optional embodiment of the present application, the triggering module 74 is configured to compare the first matching number with a preset matching number; if the first matching number does not reach the preset matching number, trigger the hierarchical verification mechanism to verify the remaining fingerprint images to obtain a verification result; or, if the first matching number reaches the preset matching number, determine that the verification result is a fingerprint verification success.

[0141] In an optional embodiment of the present application, the triggering module 74 is configured to acquire a distribution state of the remaining sensors of the fingerprint sensors; if the distribution state is a plurality of facet array sensors, perform a verification operation of the remaining fingerprint images based on a first strategy to obtain a first verification result; if the distribution state is a ring-shaped sensor, perform a verification operation of the remaining fingerprint images based on a second strategy to obtain a second verification result.

[0142] In an optional embodiment of the present application, the triggering module 74 is configured to determine the second sensor of the next priority according to the preset number corresponding to the fingerprint sensor; acquire the second fingerprint image collected by the second sensor and the corresponding second fingerprint template; extract a plurality of second feature points in the second fingerprint image, and match the second feature points with the preset feature points in the second fingerprint template to obtain a second matching number; calculate the sum of the first matching number and the second matching number, and determine whether the sum reaches the first verification threshold; if the first verification threshold is reached, it is determined that the first verification result is a fingerprint verification success, and the matching operation is terminated, or if the first verification threshold is not reached, the matching operation is repeated until the fingerprint sensors are all matched and the first verification threshold is not reached, and it is determined that the first verification result is a fingerprint verification failure.

[0143] In an optional embodiment of the present application, the triggering module 74 is configured to acquire the third fingerprint image collected by the annular sensor and the corresponding third fingerprint template; extract a plurality of third feature points in the third fingerprint image, and classify the third feature points according to a preset area to obtain a feature point set corresponding to each area; match the feature point set corresponding to each area with the third preset feature points in the third fingerprint template to obtain a plurality of matching areas; determine whether the number of areas of the matching areas reaches a third verification threshold; if the third verification threshold is reached, it is determined that the second verification result is a fingerprint verification success, or if the third verification threshold is not reached, it is determined that the second verification result is a fingerprint verification failure.

[0144] Please refer to Figure 8 , Figure 8 is a structural schematic diagram of a computer device provided by an optional embodiment of the present application, as shown in Figure 8 , the computer device comprises one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Various components are communicatively connected with each other by using different buses, and can be installed on a common mainboard or in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in the memory or graphics information of the memory to display a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, each device providing part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system).

[0145] The processor 10 can be a central processor, a network processor, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic gate array, a generic array logic, or any combination thereof.

[0146] The memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods illustrated in the above embodiments.

[0147] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs required by at least one function, and the like. The data storage area can store data created by the use of the computer device according to the presentation of a small program landing page, and the like. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some optional embodiments, the memory 20 can optionally include a memory disposed remotely relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0148] The memory 20 can include a volatile memory, such as a random access memory, and can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid state disk. The memory 20 can further include a combination of the above-mentioned types of memories.

[0149] The computer device further includes a communication interface 30 for communication of the computer device with other devices or communication networks.

[0150] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or be implemented as computer codes stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded through a network and stored in a local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer codes, when the software or computer codes are accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0151] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A method of verifying a fingerprint image, characterized by, The method comprises: detecting a pressing operation of a user on a curved cover coating, wherein a central region of the curved cover coating is provided with a main sensor, and a plurality of facet array sensors are distributed around the main sensor; when the pressing operation is detected, acquiring fingerprint images collected by a plurality of fingerprint sensors on the curved cover coating; matching a first fingerprint image collected by a first sensor of the fingerprint sensors with a first fingerprint template associated with the first sensor to obtain a first matching number, wherein the first sensor is the main sensor; triggering a hierarchical verification mechanism according to the first matching number to verify the remaining fingerprint images to obtain a verification result.

2. The method of claim 1, wherein, Before detecting the pressing operation of the user on the curved cover coating, the method further comprises: acquiring original fingerprint images collected by a plurality of fingerprint sensors on the curved cover coating; screening the original fingerprint images according to a preset enrollment standard to obtain a plurality of standard fingerprint images; extracting a plurality of preset feature points of the standard fingerprint images, constructing a fingerprint template using the preset feature points, and storing the fingerprint template in association with the corresponding fingerprint sensor in a template storage space.

3. The method of claim 1, wherein, The matching of the first fingerprint image collected by the first sensor of the fingerprint sensors with the first fingerprint template associated with the first sensor to obtain the first matching number comprises: traversing a preset number corresponding to the fingerprint sensor; determining a first sensor of a first priority according to the preset number; acquiring a first fingerprint image collected by the first sensor and a corresponding first fingerprint template; extracting a plurality of first feature points in the first fingerprint image, and matching the first feature points with preset feature points in the first fingerprint template to obtain the first matching number.

4. The method of claim 1, wherein, The triggering of the hierarchical verification mechanism according to the first matching number to verify the remaining fingerprint images to obtain the verification result comprises: comparing the first matching number with a preset matching number; if the first matching number does not reach the preset matching number, triggering the hierarchical verification mechanism to verify the remaining fingerprint images to obtain the verification result; or, if the first matching number reaches the preset matching number, determining that the verification result is a fingerprint verification success.

5. The method of claim 4, wherein, The triggering of the hierarchical verification mechanism to verify the remaining fingerprint images to obtain the verification result comprises: acquiring a distribution state of the remaining sensors of the fingerprint sensors; if the distribution state is a plurality of facet array sensors, performing a verification operation of the remaining fingerprint images based on a first strategy to obtain a first verification result; if the distribution state is a ring-shaped sensor, performing a verification operation of the remaining fingerprint images based on a second strategy to obtain a second verification result.

6. The method of claim 5, wherein, The performing of the verification operation of the remaining fingerprint images based on the first strategy to obtain the first verification result comprises: determining a second sensor of a next priority according to a preset number corresponding to the fingerprint sensor; acquiring a second fingerprint image collected by the second sensor and a corresponding second fingerprint template; extract a plurality of second feature points in the second fingerprint image, and match the second feature points with preset feature points in the second fingerprint template to obtain a second matching number; calculate a sum value of the first matching number and the second matching number, and determine whether the sum value reaches a first verification threshold; if the first verification threshold is reached, determine that a first verification result is fingerprint verification success, and terminate the matching operation, or if the first verification threshold is not reached, repeat the matching operation until the fingerprint sensor is matched completely and the first verification threshold is not reached, and determine that a first verification result is fingerprint verification failure.

7. The method of claim 5, wherein, the verification operation of the remaining fingerprint images is performed based on a second strategy to obtain a second verification result, including: obtaining a third fingerprint image collected by the ring-shaped sensor and a corresponding third fingerprint template; extracting a plurality of third feature points in the third fingerprint image, and classifying the third feature points according to a preset area to obtain a feature point set corresponding to each area; matching the feature point set corresponding to each area with third preset feature points in the third fingerprint template to obtain a plurality of matching areas; determining whether the number of matching areas reaches a third verification threshold; if the third verification threshold is reached, determining that a second verification result is fingerprint verification success, or if the third verification threshold is not reached, determining that a second verification result is fingerprint verification failure.

8. An apparatus for verifying a fingerprint image, characterized by the device includes: a detection module configured to detect a pressing operation of a user on a curved coating, wherein a central area of the curved coating is provided with a main sensor, and a plurality of facet array sensors are distributed around the main sensor on a surrounding curved surface; an acquisition module configured to acquire fingerprint images collected by a plurality of fingerprint sensors on the curved coating when the pressing operation is detected; a matching module configured to match a first fingerprint image collected by a first sensor of the fingerprint sensors with a first fingerprint template associated with the first sensor to obtain a first matching number, wherein the first sensor is the main sensor; a triggering module configured to trigger a hierarchical verification mechanism according to the first matching number to verify remaining fingerprint images to obtain a verification result.

9. A computer device, comprising: including: a memory and a processor, which are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the method of any one of claims 1 to 7.

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