Fingerprint processing method, apparatus and system

Abstract

The application discloses a fingerprint processing method, device and system. The method comprises the following steps: acquiring a first fingerprint image; performing image analysis on the first fingerprint image to determine the image type corresponding to the first fingerprint image; if the image type corresponding to the first fingerprint image is a weak template type, determining a target fingerprint image based on the first fingerprint image and caching the target fingerprint image; if the image type corresponding to the first fingerprint image is a strong template type, registering the first fingerprint image as a strong fingerprint template and updating the current state data corresponding to the strong fingerprint template; when the current state data meets the registration end condition, registering the target fingerprint image as a weak fingerprint template and completing fingerprint registration based on the strong fingerprint template and the weak fingerprint template. The method realizes classification processing of the first fingerprint image without affecting the image quality of the registered fingerprint template and the subsequent recognition success rate, improves the registration efficiency and enhances the user registration experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the fingerprint identification technical field, and especially relates to a fingerprint processing method, device and system. BACKGROUND

[0002] The fingerprint identification technology is a technology of identifying the user identity according to the fingerprint, and before the fingerprint identification, the fingerprint registration is generally needed, the collected fingerprint image is registered in the fingerprint template library, and when used, the real-time collected fingerprint image is compared with the fingerprint template library to realize the fingerprint identification.

[0003] In the existing fingerprint registration process, multiple fingerprint images need to be collected, and whether each fingerprint image meets the registration standard is evaluated, and the fingerprint image meeting the registration standard is registered in the fingerprint template library, and the process is time-consuming, the registration efficiency is low, and the user registration experience is affected. SUMMARY

[0004] Embodiments of the present application provide a fingerprint processing method, device and system to solve the problem of time-consuming, low registration efficiency and affecting user registration experience in the existing fingerprint registration process.

[0005] A fingerprint processing method, comprising:

[0006] obtaining a first fingerprint image, the first fingerprint image being a fingerprint image collected at a current time;

[0007] performing image analysis on the first fingerprint image to determine an image type corresponding to the first fingerprint image;

[0008] if the image type corresponding to the first fingerprint image is a weak template type, determining a target fingerprint image based on the first fingerprint image, and caching the target fingerprint image;

[0009] if the image type corresponding to the first fingerprint image is a strong template type, registering the first fingerprint image as a strong fingerprint template, and updating current state data corresponding to the strong fingerprint template;

[0010] when the current state data corresponding to the strong fingerprint template meets a registration end condition, registering the target fingerprint image as a weak fingerprint template, and completing fingerprint registration based on the strong fingerprint template and the weak fingerprint template.

[0011] Preferably, the image analysis on the first fingerprint image to determine the image type corresponding to the first fingerprint image comprises:

[0012] performing motion blur identification based on the first fingerprint image and a second fingerprint image to determine a motion blur type corresponding to the first fingerprint image, the second fingerprint image being a fingerprint image collected at a previous time.

[0013] detecting the effective area and the image quality of the first fingerprint image to determine a first detection result corresponding to the first fingerprint image;

[0014] detecting the repetition rate based on the first fingerprint image and a third fingerprint image to determine a second detection result corresponding to the first fingerprint image, the third fingerprint image being a fingerprint image of a type of strong template type or weak template type determined before the current time;

[0015] determining the image type corresponding to the first fingerprint image based on the motion blur type corresponding to the first fingerprint image, the first detection result and the second detection result.

[0016] Preferably, the motion blur identification based on the first fingerprint image and the second fingerprint image to determine the motion blur type corresponding to the first fingerprint image comprises:

[0017] identifying the motion blur of the first fingerprint image and the second fingerprint image to determine a target motion blur value corresponding to the first fingerprint image;

[0018] determining the motion blur type corresponding to the first fingerprint image based on the target motion blur value.

[0019] Preferably, the detection of the effective area and the image quality of the first fingerprint image to determine the first detection result corresponding to the first fingerprint image comprises:

[0020] detecting the effective area and the image quality of the first fingerprint image to determine the effective area and the quality score corresponding to the first fingerprint image;

[0021] if the effective area is greater than a target area threshold and the quality score is greater than a target quality threshold, determining that the first detection result corresponding to the first fingerprint image is a detection pass;

[0022] if the effective area is not greater than the target area threshold or the quality score is not greater than the target quality threshold, determining that the first detection result corresponding to the first fingerprint image is a detection fail.

[0023] Preferably, before the detection of the effective area and the image quality of the first fingerprint image to determine the first detection result corresponding to the first fingerprint image, the fingerprint processing method further comprises:

[0024] identifying the motion blur of the first fingerprint image and the second fingerprint image to determine a target motion blur value corresponding to the first fingerprint image;

[0025] determine the target area threshold and the target quality threshold based on the target motion blur value;

[0026] wherein the target area threshold is positively correlated with the target motion blur value, and the target quality threshold is positively correlated with the target motion blur value.

[0027] Preferably, the third fingerprint image comprises a fourth fingerprint image, and the fourth fingerprint image is a fingerprint image of a strong template type determined before the current time.

[0028] The repeated rate detection based on the first fingerprint image and the third fingerprint image to determine the second detection result corresponding to the first fingerprint image comprises:

[0029] The repeated rate detection based on the first fingerprint image and all the fourth fingerprint images to determine a first measured repeated rate;

[0030] The repeated rate detection based on the first fingerprint image and the third fingerprint image closest to the current time to determine a second measured repeated rate;

[0031] If the first measured repeated rate is less than a first repeated rate threshold, and the second measured repeated rate is less than a second repeated rate threshold, it is determined that the second detection result corresponding to the first fingerprint image is a detection pass.

[0032] If the first measured repeated rate is not less than the first repeated rate threshold, or the second measured repeated rate is not less than the second repeated rate threshold, it is determined that the second detection result corresponding to the first fingerprint image is a detection fail.

[0033] Preferably, the first measured repeated rate is the maximum value in single repeated rates corresponding to all the fourth fingerprint images, and the single repeated rate is the repeated rate between the fourth fingerprint image and the first fingerprint image.

[0034] Alternatively, the first measured repeated rate is the repeated rate between a spliced fingerprint image and the first fingerprint image, and the spliced fingerprint image is a fingerprint image obtained by splicing all the fourth fingerprint images.

[0035] Preferably, before the repeated rate detection based on the first fingerprint image and the third fingerprint image to determine the second detection result corresponding to the first fingerprint image, the fingerprint processing method further comprises:

[0036] obtaining a first cumulative number, the first cumulative number being a number of continuously obtained first fingerprint images of the image type being a weak template type;

[0037] determining the first repeated rate threshold and the second repeated rate threshold based on the first cumulative number;

[0038] wherein the first repetition rate threshold is negatively related to the first cumulative number; and the second repetition rate threshold is negatively related to the first cumulative number.

[0039] Preferably, the determining the image type corresponding to the first fingerprint image based on the motion blur type corresponding to the first fingerprint image, the first detection result and the second detection result comprises:

[0040] if the motion blur type is a non-blur type, the first detection result is a detection pass, and the second detection result is a detection pass, determining that the image type corresponding to the first fingerprint image is a strong template type;

[0041] if the motion blur type is a non-blur type, the first detection result is a detection pass, and the second detection result is a detection pass, determining that the image type corresponding to the first fingerprint image is a strong template type;

[0042] or, if the motion blur type is a semi-blur type, the first detection result is a detection pass, and the second detection result is a detection pass, determining that the image type corresponding to the first fingerprint image is a weak template type.

[0043] Preferably, the determining the target fingerprint image based on the first fingerprint image comprises:

[0044] obtaining the number of images corresponding to the fifth fingerprint image, the fifth fingerprint image being a fingerprint image of which the image type determined before the current time is a weak template type;

[0045] if the number of images corresponding to the fifth fingerprint image is less than a first number threshold, determining the first fingerprint image and all the fifth fingerprint images as target fingerprint images;

[0046] if the number of images corresponding to the fifth fingerprint image is not less than the first number threshold, analyzing the first fingerprint image and all the fifth fingerprint images to determine target fingerprint images.

[0047] Preferably, the analyzing the first fingerprint image and all the fifth fingerprint images to determine target fingerprint images comprises:

[0048] determining the priority of the first fingerprint image;

[0049] if the priority of the first fingerprint image is less than the minimum priority of all the fifth fingerprint images, deleting the first fingerprint image and determining all the fifth fingerprint images as target fingerprint images;

[0050] If the priority corresponding to the first fingerprint image is not less than the minimum priority of all the fifth fingerprint images, the fifth fingerprint image corresponding to the minimum priority is deleted, and the first fingerprint image and the remaining fifth fingerprint images are determined as target fingerprint images.

[0051] Preferably, the determining of the priority corresponding to the first fingerprint image comprises:

[0052] Based on the first fingerprint image and the second fingerprint image, motion blur recognition is performed to determine a target motion blur value, and based on the target motion blur value, a first index score corresponding to the first fingerprint image is determined, the second fingerprint image being a fingerprint image collected at a previous moment;

[0053] The first fingerprint image is subjected to effective area and image quality detection to determine an effective area and quality score, and based on the effective area and the quality score, a second index score corresponding to the first fingerprint image is determined;

[0054] Based on the first fingerprint image and the third fingerprint image, repetition rate detection is performed to determine a measured repetition rate, and based on the measured repetition rate, a third index score corresponding to the first fingerprint image is determined, the third fingerprint image being a fingerprint image of which the image type determined before the current moment is a strong template type or a weak template type;

[0055] Based on the first index score, the second index score and the third index score corresponding to the first fingerprint image, a priority corresponding to the first fingerprint image is determined.

[0056] Preferably, the determining of the priority corresponding to the first fingerprint image based on the first index score, the second index score and the third index score corresponding to the first fingerprint image comprises:

[0057] The fingerprint collection mode corresponding to the first fingerprint image is determined.

[0058] If the fingerprint collection mode is a pressing mode, the first index score, the second index score and the third index score corresponding to the first fingerprint image are subjected to weighting processing based on a first weight combination to determine the priority corresponding to the first fingerprint image, and in the first weight combination, the weight of the first index score is less than the weight of the third index score.

[0059] If the fingerprint collection mode is a sliding mode, the first index score, the second index score and the third index score corresponding to the first fingerprint image are subjected to weighting processing based on a second weight combination to determine the priority corresponding to the first fingerprint image, and in the second weight combination, the weight of the first index score is greater than the weight of the third index score.

[0060] Preferably, the current state data comprises a second cumulative number, the second cumulative number being a number of strong fingerprint templates that have been registered; and the registration end condition comprises that the second cumulative number is greater than a second number threshold.

[0061] Alternatively, the current state data comprises an effective area ratio, the effective area ratio being a ratio of an overlapping area after splicing of all strong fingerprint templates to an area of a standard fingerprint template; and the registration end condition comprises that the effective area ratio is greater than a preset ratio threshold.

[0062] A fingerprint processing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the fingerprint processing method when executing the computer program.

[0063] A fingerprint processing system, comprising a fingerprint sensor and the fingerprint processing device, wherein the fingerprint processing device is connected to the fingerprint sensor.

[0064] The fingerprint processing method, device, and system perform image analysis on the first fingerprint image, determine the image type corresponding to the first fingerprint image, and process the first fingerprint image according to the image type. Specifically, when the image type is a weak template type, the target fingerprint image that needs to be cached is determined based on the first fingerprint image, and the target fingerprint image is cached first, so that the target fingerprint image is registered as a weak fingerprint template when the registration end condition is met, which helps to improve the processing efficiency. When the image type is a strong template type, the first fingerprint image can be directly registered as a strong fingerprint template. In this scheme, the first fingerprint image of the strong template type is processed in series, and the first fingerprint image of the weak template type is processed in parallel. The processing strategy of combining series and parallel processing is adopted to realize the classification processing of the first fingerprint image, improve the registration efficiency, and further improve the user registration experience without affecting the image quality of the registered fingerprint template and the subsequent recognition success rate. BRIEF DESCRIPTION OF DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0066] Figure 1 is a schematic diagram of an ultrasonic fingerprint system;

[0067] Figure 2 is a flowchart of a fingerprint processing method in an embodiment of the present application;

[0068] Figure 3 isFigure 2 A flowchart of step S2 in the method;

[0069] Figure 4 is Figure 2 A flowchart of step S3 in the method. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be clearly and completely described 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 a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0071] The fingerprint processing method provided by the embodiments of the present application can be applied to a fingerprint processing system, which includes a fingerprint processing device and a fingerprint sensor. The fingerprint processing device is connected to the fingerprint sensor and can acquire a fingerprint image collected by the fingerprint sensor. The fingerprint processing method can quickly complete a fingerprint registration operation based on the fingerprint image. The image quality of a registered fingerprint template is considered, and the registration efficiency is improved to improve the user registration experience.

[0072] As an example, the fingerprint sensor can be a press-type fingerprint sensor, which can collect a fingerprint image based on a press operation of a user. Alternatively, the fingerprint sensor can be a sliding-type fingerprint sensor, which can collect a fingerprint image based on a sliding or pushing operation of a user. Alternatively, the fingerprint sensor can be an ultrasonic fingerprint sensor in an ultrasonic fingerprint system, which can collect a corresponding fingerprint image by triggering an ultrasonic signal.

[0073] As an example, the fingerprint processing system can be an ultrasonic fingerprint system, such as Figure 1 As shown in the figure, the fingerprint sensor of the ultrasonic fingerprint system is an ultrasonic fingerprint sensor. The ultrasonic fingerprint system is a biometric technology based on ultrasonic technology. A corresponding fingerprint image is acquired by emitting and receiving ultrasonic signals. The difference in acoustic impedance between a screen, a finger, and air is used to distinguish valleys and ridges on a fingerprint, so as to acquire fingerprint features for identity recognition.

[0074] The fingerprint processing device of the ultrasonic fingerprint system can include a controller, an ultrasonic fingerprint sensor, a data processor, an ADC converter, and an algorithm processor. In this example, the controller is connected to the ultrasonic fingerprint sensor, the data processor, the ADC converter, and the algorithm processor, and is used to control the operation of each module. The working process is as follows: the controller controls the ultrasonic fingerprint sensor to generate and receive ultrasonic signals; the ADC converter performs digital-to-analog conversion on the received ultrasonic signals and sends the converted signals to the data processor; the data processor completes data rearrangement and packaging; and the algorithm processor performs algorithm processing on the converted data to complete fingerprint registration and identification. In this example, the fingerprint processing device can include a controller, or can include a controller and a data processor, an ADC converter, and an algorithm processor connected thereto. The specific situation can be determined accordingly.

[0075] In the fingerprint registration process of the ultrasonic fingerprint system, the fingerprint lines of the finger need to be collected first as the fingerprint template for identification. The fingerprint template has an important influence on identification. In the fingerprint template registration process, the ultrasonic frame rate is generally adjusted to collect a fingerprint image that has not been blurred by motion. Generally speaking, the higher the frame rate, the more likely it is to collect a fingerprint image that has not been blurred by motion, thereby improving the accuracy of registration and user experience. However, this way of improving the ultrasonic frame rate has the following disadvantages: first, increasing the frame rate increases the processing burden of the device, leading to a decrease in device performance and an increase in power consumption; second, the frame rate is limited by the processing capacity of the entire system, environmental light, and finger state, so increasing the frame rate cannot guarantee that a clear fingerprint image will be collected every time, i.e., the image quality of the collected fingerprint image cannot be guaranteed. Therefore, in order to improve the experience and efficiency of users during application, the finger is slid over the fingerprint line collection area during the working process of the system, the fingerprint image of the moving finger is collected and analyzed through the processing of each module of the system, and through step-by-step processing strategies for data, the collection efficiency of the template is improved without affecting the identification efficiency, effectively reducing the time of the registration process.

[0076] The embodiment of the present application provides a fingerprint processing method, which is described by taking the fingerprint processing device as an example, as shown in the figure, the fingerprint processing method comprises: Figure 2

[0077] S1: acquiring a first fingerprint image, the first fingerprint image being a fingerprint image collected at a current moment;

[0078] S2: performing image analysis on the first fingerprint image to determine an image type corresponding to the first fingerprint image;

[0079] S3: if the image type corresponding to the first fingerprint image is a weak template type, determining a target fingerprint image based on the first fingerprint image, and caching the target fingerprint image;

[0080] S4: if the image type corresponding to the first fingerprint image is a strong template type, registering the first fingerprint image as a strong fingerprint template, and updating current state data corresponding to the strong fingerprint template;

[0081] S5: when the current state data corresponding to the strong fingerprint template satisfies a registration end condition, registering the target fingerprint image as a weak fingerprint template, and completing fingerprint registration based on the strong fingerprint template and the weak fingerprint template.

[0082] As an example, in step S1, the fingerprint processing device is connected with a fingerprint sensor, and the first fingerprint image collected by the fingerprint sensor in real time can be acquired. In the present example, the fingerprint processing device can acquire the first fingerprint image formed by a finger pressing a specific area through a press-type fingerprint sensor, or the fingerprint processing device can acquire the first fingerprint image formed by a finger sliding or dragging a specific area through a sliding-type fingerprint sensor, or the fingerprint processing device can acquire the first fingerprint image formed by a finger sliding a specific area through an ultrasonic fingerprint sensor.

[0083] As an example, in step S2, the fingerprint processing device can call the built-in image analysis logic to perform image analysis on the first fingerprint image to determine the image type corresponding to the first fingerprint image after acquiring the first fingerprint image. In the present example, the image type can be any one of a strong template type, a weak template type and an invalid type, and the strong template type and the weak template type are two valid types with different validity degrees; the strong template type herein refers to a type that can be used as a strong fingerprint template, the weak template type refers to a type that can be used as a weak fingerprint template, and the invalid type refers to a type that cannot be used as a fingerprint template. The strong fingerprint template refers to a valid fingerprint image completely filtered. The weak fingerprint template refers to a valid fingerprint image partially filtered, and is a fingerprint template used to assist the strong fingerprint template in fingerprint identification. The valid fingerprint image refers to a fingerprint image that can be stored in a fingerprint template library for fingerprint identification.

[0084] ​The target fingerprint image is a fingerprint image that needs to be cached.

[0085] As an example, in step S3, when the image type corresponding to the first fingerprint image is the weak template type, the fingerprint processing apparatus can determine that the first fingerprint image passes the screening, i.e., it is determined that the first fingerprint image meets the partial screening condition for being registered as a fingerprint template. In this case, the first fingerprint image can be directly determined as the target fingerprint image, and the target fingerprint image can be cached. Alternatively, the first fingerprint image collected at the current time and the target fingerprint image cached before the current time can be analyzed and processed to update the target fingerprint image that can be cached as a weak fingerprint template. The specific determination can be made autonomously according to actual conditions. In the example, when the image type of the first fingerprint image is the weak template type, the target fingerprint image is updated based on the first fingerprint image, and the target fingerprint image is cached without registration processing, which helps to improve the processing efficiency and further improve the user registration experience.

[0086] The current state data corresponding to the strong fingerprint template is data related to the strong fingerprint template, specifically data used to evaluate whether the registration process needs to be ended. The registration end condition is a condition set in advance for ending the current fingerprint registration operation.

[0087] As an example, in step S4, when the image type corresponding to the first fingerprint image is the strong template type, the fingerprint processing apparatus can determine that the first fingerprint image completely passes the screening, i.e., it is determined that the first fingerprint image meets all the screening conditions for being registered as a fingerprint template. Therefore, the first fingerprint image is registered as a strong fingerprint template, and the current state data corresponding to the strong fingerprint template is updated, so that the current state data corresponding to the strong fingerprint template is compared with the registration end condition set in advance, and the subsequent step is executed according to the comparison result.

[0088] As an example, in step S5, when the current state data corresponding to the strong fingerprint template meets the registration end condition, the fingerprint processing apparatus can register the cached target fingerprint image as a weak fingerprint template, and then complete the fingerprint registration operation based on all the strong fingerprint templates and all the weak fingerprint templates, i.e., all the strong fingerprint templates and all the weak fingerprint templates are determined as registered fingerprint templates, and the registered fingerprint templates are stored in the fingerprint template library, so that the registered fingerprint templates are used for subsequent fingerprint recognition based on real-time collected fingerprint images. In the example, the weak fingerprint template can assist the strong fingerprint template to complete the fingerprint recognition operation, so as to guarantee the recognition success rate of subsequent fingerprint recognition.

[0089] In this example, when the fingerprint processing device determines that the image type corresponding to the first fingerprint image is invalid, it can determine that the first fingerprint image cannot be used as a registration fingerprint template. Therefore, the first fingerprint image needs to be deleted. For example, it can be deleted after performing motion blur recognition on the fingerprint image acquired at the next moment, and the first fingerprint image will no longer be processed. This helps to save processing resources and improve the efficiency of acquiring registration fingerprint templates. At this time, it is necessary to repeat the acquisition of the first fingerprint image and subsequent steps.

[0090] In this example, image analysis is performed on the first fingerprint image to determine its corresponding image type. Different processing methods are then applied based on the image type. Specifically, when the image type is a weak template type, a target fingerprint image that needs to be cached is determined based on the first fingerprint image. Cache is performed first, so that the target fingerprint image is registered as a weak fingerprint template only when the registration termination condition is met, thus improving processing efficiency. When the image type is a strong template type, the first fingerprint image can be directly registered as a strong fingerprint template. This example employs a combined serial and parallel processing strategy, processing the first fingerprint image serially and the first fingerprint image in parallel for the weak template type. This achieves classification processing of the first fingerprint image without affecting the image quality of the registered fingerprint template or the subsequent recognition success rate, improving registration efficiency and ultimately enhancing the user registration experience.

[0091] In one embodiment, such as Figure 3 As shown, step S2, which involves image analysis of the first fingerprint image to determine the image type corresponding to the first fingerprint image, includes:

[0092] S21: Motion blur recognition is performed based on the first fingerprint image and the second fingerprint image to determine the motion blur type corresponding to the first fingerprint image, and the second fingerprint image is the fingerprint image acquired at the previous moment.

[0093] S22: Perform effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image;

[0094] S23: Perform repetition rate detection based on the first fingerprint image and the third fingerprint image, determine the second detection result corresponding to the first fingerprint image, and the third fingerprint image is a fingerprint image whose image type was previously determined to be either a strong template type or a weak template type at the current moment;

[0095] S24: Based on the motion blur type corresponding to the first fingerprint image, the first detection result, and the second detection result, determine the image type corresponding to the first fingerprint image.

[0096] The second fingerprint image is a fingerprint image collected at a previous time, and the second fingerprint image is cached in the memory, so that the second fingerprint image can be directly called when motion blur recognition is needed. Motion blur refers to the phenomenon of deformation, distortion, and trailing of fingerprint lines during the movement of a finger, and can reflect the situation that the collected fingerprint lines do not match the actual fingerprint lines.

[0097] As an example, in step S21, after obtaining the first fingerprint image, the fingerprint processing device can call the pre-set motion blur recognition logic to recognize the first fingerprint image and the second fingerprint image, and determine the motion blur type corresponding to the first fingerprint image. The motion blur type can reflect the change of the fingerprint lines of the two frames of fingerprint images collected before and after the movement of the finger during the sliding, dragging, or other movement of the finger. In this example, the motion blur type is any one of a full blur type, a half blur type, and a non-blur type. The full blur type refers to a completely blurred state, i.e., a state in which the fingerprint lines are deformed and the deformation degree is large. The non-blur type refers to a state in which no blur occurs, i.e., a state in which the fingerprint lines are normal. The half blur type is a state between the full blur type and the non-blur type, i.e., a state in which the fingerprint lines are deformed but the deformation degree is small.

[0098] As an example, in step S22, after obtaining the first fingerprint image, the fingerprint processing device can perform effective area and image quality detection on the first fingerprint image to determine the effective area and the image quality corresponding to the first fingerprint image, and then compare the two evaluation indexes with the pre-set fingerprint effective standard. If both evaluation indexes satisfy the fingerprint effective standard, it is determined that the first detection result corresponding to the first fingerprint image is a detection pass. Otherwise, if at least one of the two evaluation indexes does not satisfy the fingerprint effective standard, it is determined that the first detection result corresponding to the first fingerprint image is a detection fail. The fingerprint effective standard can be understood as a standard for evaluating whether the collected fingerprint image is valid, so that it satisfies the subsequent fingerprint recognition requirements. In this example, the first detection result corresponding to the first fingerprint image is used to reflect whether the first fingerprint image itself can satisfy the fingerprint recognition requirements.

[0099] The third fingerprint image is a fingerprint image of which the image type is a strong template type or a weak template type and which is determined before the current time, i.e., an effective fingerprint image determined before the current time. The effective fingerprint image refers to a fingerprint image that can be stored in a fingerprint template library for fingerprint recognition, i.e., an effective fingerprint image is a fingerprint image of which the image type is a strong template type or a weak template type. In this example, the third fingerprint image can be one or multiple, which can be a strong fingerprint template determined before the current time or a target fingerprint image determined before the current time.

[0100] As an example, in step S23, after obtaining the first fingerprint image, the fingerprint processing device can read the third fingerprint image from the memory, and then perform the repetition rate detection based on the first fingerprint image and the third fingerprint image, determine the overlapping area of the two, and then determine the ratio of the overlapping area to the area of the first fingerprint image, and determine the measured repetition rate as the ratio of the two. The measured repetition rate is compared with the pre-set repetition rate threshold. If the measured repetition rate is less than the repetition rate threshold, it means that the overlapping part of the first fingerprint image and the third fingerprint image accounts for a small proportion, and the first fingerprint image needs to be retained to ensure the integrity and comprehensiveness of the fingerprint features in the registered fingerprint template. Therefore, the second detection result corresponding to the first fingerprint image is determined as a detection pass. On the contrary, if the measured repetition rate is not less than the repetition rate threshold, it means that the overlapping part of the first fingerprint image and the third fingerprint image accounts for a large proportion, and the first fingerprint image does not need to be retained and will not affect the integrity and comprehensiveness of the fingerprint features in the registered fingerprint template. Therefore, the second detection result corresponding to the first fingerprint image is determined as a detection fail.

[0101] As an example, in step S24, after determining the motion blur type corresponding to the first fingerprint image, the first detection result and the second detection result, the fingerprint processing device can determine the image type corresponding to the first fingerprint image based on the specific combination of the motion blur type, the first detection result and the second detection result. In this example, the motion blur type can be any one of the full blur type, the half blur type and the non-blur type; the first detection result can be any one of the detection pass and the detection fail; the second detection result can be any one of the detection pass and the detection fail; the three can form any one of the 12 combinations, and the specific combination is queried in the pre-set combination type mapping table to determine the corresponding image type. The combination type mapping table is a data table for reflecting the mapping relationship between different combinations and their corresponding image types.

[0102] In this example, the motion blur recognition is performed based on the first fingerprint image and the second fingerprint image, the fingerprint line change is analyzed according to the difference between the two fingerprint images to determine the motion blur type reflecting the fingerprint line change; the effective area and image quality detection are performed on the first fingerprint image to evaluate whether it can meet the quality standard of the fingerprint template from the quality of the fingerprint image itself, so as to determine the first detection result corresponding thereto; the repetition rate detection is performed on the first fingerprint image and the third fingerprint image to analyze the repetition of the fingerprint features of the two, and then determine the second detection result corresponding thereto. Based on the motion blur type, the first detection result and the second detection result corresponding to the first fingerprint image, the image type corresponding thereto is determined, which can realize the comprehensive determination of the image type corresponding thereto from multiple dimensions, so as to determine whether it can be used as a fingerprint template according to the image type, thereby ensuring the recognition success rate of the fingerprint recognition.

[0103] In an embodiment, the step S21, i.e. the motion blur recognition based on the first fingerprint image and the second fingerprint image, determines the motion blur type corresponding to the first fingerprint image, comprising:

[0104] S211: performing motion blur recognition on the first fingerprint image and the second fingerprint image to determine a target motion blur value corresponding to the first fingerprint image;

[0105] S212: determining the motion blur type corresponding to the first fingerprint image based on the target motion blur value.

[0106] The target motion blur value is a specific numerical value used to evaluate the motion blur type.

[0107] As an example, in the step S211, after obtaining the first fingerprint image, the fingerprint processing device can use a pre-set motion blur algorithm to calculate the first fingerprint image and the second fingerprint image, and determine the output result as an initial motion blur value. The initial motion blur value can be directly determined as the target motion blur value. Alternatively, according to specific conditions, a target correction coefficient can be determined, and the initial motion blur value is corrected by using the target correction coefficient to determine the target motion blur value corresponding to the first fingerprint image.

[0108] For example, the motion blur algorithm can be S=std(diff(phase1-phase2)) / signal, where phase1 and phase2 are the first fingerprint image and the second fingerprint image, which can be understood as two frames of fingerprint images of the same configuration collected at the beginning and the end of image acquisition; std is a function for calculating the standard deviation of data space; diff is a function for calculating the difference between the two; signal is the signal amount of the fingerprint image, which is used to reflect the pressing force and signal size of the fingerprint; and S is the initial motion blur value, which can reflect the motion blur degree of the two frames of fingerprint images.

[0109] In this example, the step S211, i.e. the motion blur recognition on the first fingerprint image and the second fingerprint image to determine the target motion blur value corresponding to the first fingerprint image, specifically comprises:

[0110] S2111: performing motion blur recognition on the first fingerprint image and the second fingerprint image to determine an initial motion blur value corresponding to the first fingerprint image.

[0111] S2112: performing quality analysis on the first fingerprint image to determine a quality score corresponding to the first fingerprint image. The quality score is a score determined by quality analysis on the first fingerprint image, which is used to reflect the contrast and clarity of the fingerprint, etc.

[0112] S2113: The initial motion blur value is corrected by using the quality score to determine the target motion blur value corresponding to the first fingerprint image.

[0113] Since the fingerprint image with a high quality score has better anti-motion blur capability, the initial motion blur value can be corrected by using the quality score corresponding to the first fingerprint image to ensure that the corrected target motion blur value can more easily pass the motion blur control, thereby helping to improve the fingerprint registration efficiency.

[0114] Further, the system pre-sets two quality thresholds, i.e., a first quality threshold and a second quality threshold, the first quality threshold being greater than the second quality threshold, for example, the first quality threshold can be set to 50%, and the second quality threshold can be set to 35%. In the step S2113, the initial motion blur value is corrected by using the quality score to determine the target motion blur value corresponding to the first fingerprint image, specifically including:

[0115] S21131: If the quality score is greater than the first quality threshold, the product of the initial motion blur value corresponding to the first fingerprint image and a first correction coefficient is determined as the target motion blur value corresponding to the first fingerprint image. The first correction coefficient is a value between 0 and 1, for example, the first correction coefficient can be set to 1 / 5, which is used to represent that when the quality score of the first fingerprint image is large, the initial motion blur value is reduced by 5 times to determine the target motion blur value corresponding to the first fingerprint image, so that the first fingerprint image with better image quality can more easily pass the motion blur control to improve the fingerprint registration efficiency.

[0116] S21132: If the quality score is not greater than the first quality threshold and is greater than the second quality threshold, the product of the initial motion blur value corresponding to the first fingerprint image and a second correction coefficient is determined as the target motion blur value corresponding to the first fingerprint image, the first correction coefficient is a value between 0 and 1, and the second correction coefficient is greater than the first correction coefficient, for example, the second correction coefficient can be 1 / 2, which is used to represent that when the quality score of the first fingerprint image is moderate, the initial motion blur value is reduced by 2 times to determine the target motion blur value corresponding to the first fingerprint image, so that the first fingerprint image with moderate image quality can more easily pass the motion blur control to improve the fingerprint registration efficiency.

[0117] S21133: If the quality score is not greater than the second quality threshold, the initial motion blur value corresponding to the first fingerprint image is determined as the target motion blur value corresponding to the first fingerprint image, so that the first fingerprint image with poor image quality is more difficult to pass the motion blur control, and then the image type thereof is determined as an invalid type, so as to be directly deleted subsequently, thereby saving the calculation and processing resources.

[0118] In this example, the initial motion blur value is corrected based on the quality score corresponding to the first fingerprint image, so that the corrected target motion blur value can make the image quality of the first fingerprint image higher, so that the first fingerprint image is more likely to pass the motion blur control, and vice versa, so that the image quality of the first fingerprint image is lower, so that the first fingerprint image is more difficult to pass the motion blur control, so as to ensure the efficiency of fingerprint registration.

[0119] As an example, in step S212, the fingerprint processing device determines the target motion blur value corresponding to the first fingerprint image, and compares the target motion blur value with the pre-set different blur threshold value, determines the blur threshold value range, and determines the motion blur type corresponding to the blur threshold value range as the motion blur type corresponding to the first fingerprint image, that is, determines that it belongs to any one of the full blur type, the half blur type and the non-blur type.

[0120] In this example, the system pre-sets two blur threshold values for defining different motion blur types, which are respectively set as a first blur threshold value and a second blur threshold value. The first blur threshold value is a blur threshold value for defining whether the fingerprint ridge is normal, which can be specifically understood as a blur threshold value for defining the non-blur type and the half blur type, and the second blur threshold value is a blur threshold value for defining the deformation degree of the fingerprint ridge, which can be specifically understood as a blur threshold value for defining the half blur type and the full blur type, and the first blur threshold value is smaller than the second blur threshold value. For example, the first blur threshold value can be set to 20, and the second blur threshold value can be set to 50.

[0121] Step S212, that is, determining the motion blur type corresponding to the first fingerprint image based on the target motion blur value, specifically includes:

[0122] S2121: If the target motion blur value is less than the first blur threshold value, it is determined that the motion blur type corresponding to the first fingerprint image is the non-blur type. That is, when the target motion blur value corresponding to the first fingerprint image is small, it is determined that the fingerprint ridge is normal, and at this time, the motion blur type is determined to be the non-blur type.

[0123] S2122: If the target motion blur value is not less than the first blur threshold value and the target motion blur value is less than the second blur threshold value, it is determined that the motion blur type corresponding to the first fingerprint image is the half blur type. That is, when the target motion blur value corresponding to the first fingerprint image is moderate, it is determined that the fingerprint ridge is deformed, but the deformation degree is small and does not reach the standard of determining that the fingerprint ridge is abnormal, and at this time, the motion blur type is determined to be the half blur type.

[0124] S2123: If the target motion blur value is not less than the second blur threshold, it is determined that the motion blur type corresponding to the first fingerprint image is a full blur type, i.e., the target motion blur value corresponding to the first fingerprint image is large, reaching the standard of identifying abnormal fingerprint lines, at this time, it is determined that the motion blur type is a full blur type.

[0125] In an embodiment, step S22, i.e., effective area and image quality detection on the first fingerprint image, determines the first detection result corresponding to the first fingerprint image, including:

[0126] S221: Effective area and image quality detection is performed on the first fingerprint image to determine the effective area and quality score corresponding to the first fingerprint image.

[0127] S222: If the effective area is greater than the target area threshold, and the quality score is greater than the target quality threshold, it is determined that the first detection result corresponding to the first fingerprint image is detection passed.

[0128] S223: If the effective area is not greater than the target area threshold, or the quality score is not greater than the target quality threshold, it is determined that the first detection result corresponding to the first fingerprint image is detection failed.

[0129] The effective area is the ratio of the area of the effective pressing area in the fingerprint image to the total area of the fingerprint image. Generally, when collecting a fingerprint image, the fingerprint recognition area corresponding to the fingerprint sensor will collect fingerprint features. When the finger does not completely cover the fingerprint recognition area, the collected fingerprint image includes the area image corresponding to the effective pressing area and the background image outside the effective pressing area. The ratio of the area of the area image to the total area of the fingerprint image is determined as the effective area. The target area threshold is a threshold for evaluating whether the effective area meets the fingerprint effective standard. The target area threshold can be a pre-set fixed value or a dynamic value determined according to actual conditions.

[0130] The quality score is a score determined by quality analysis on the first fingerprint image, which is used to reflect the contrast and clarity of the fingerprint. The target quality threshold is a threshold for evaluating whether the quality score meets the fingerprint effective standard. The target quality threshold can be a pre-set fixed value or a dynamic value determined according to actual conditions.

[0131] As an example, in step S221, after obtaining the first fingerprint image, the fingerprint processing device can perform effective area calculation on the first fingerprint image to determine the effective area corresponding to the first fingerprint image, compare the effective area with a target area threshold value set in advance or dynamically determined, perform image quality analysis on the first fingerprint image to determine a quality score corresponding to the first fingerprint image, and compare the quality score with a target quality threshold value set in advance or dynamically determined, so as to determine the first detection result corresponding to the first fingerprint image according to the two comparison results.

[0132] As an example, in step S222, when the effective area is greater than the target area threshold value and the quality score is greater than the target quality threshold value, the fingerprint processing device can determine that the effective area of the first fingerprint image is large, that is, the area image proportion of the finger in the effective pressing area is large, the probability of collecting the fingerprint image of the invalid type is small, and the overall image quality of the collected first fingerprint image is good, and it is determined that the two fingerprint effective standards of effective area and image quality are met at the same time, and therefore, the first detection result corresponding to the first fingerprint image is determined as detection pass.

[0133] As an example, in step S223, when the effective area is not greater than the target area threshold value or the quality score is not greater than the target quality threshold value, the fingerprint processing device can determine that the effective area of the first fingerprint image is small, that is, the area image proportion of the finger in the effective pressing area is small, or the overall image quality of the collected first fingerprint image is poor, and the probability of collecting the fingerprint image of the invalid type is large, and therefore, the first detection result corresponding to the first fingerprint image is determined as detection fail.

[0134] In this example, based on the comparison result of the effective area corresponding to the first fingerprint image and the target area threshold value, and the comparison result of the quality score corresponding to the first fingerprint image and the target quality threshold value, whether the first fingerprint image meets the preset fingerprint effective standard is determined from two evaluation dimensions, and the first detection result corresponding to the first fingerprint image is determined, which can guarantee the accuracy of the first detection result.

[0135] In an embodiment, before step S22, that is, before the effective area and image quality of the first fingerprint image are detected to determine the first detection result corresponding to the first fingerprint image, the fingerprint processing method further comprises:

[0136] S221': performing motion blur identification on the first fingerprint image and the second fingerprint image to determine a target motion blur value corresponding to the first fingerprint image;

[0137] S222': determining a target area threshold value and a target quality threshold value based on the target motion blur value;

[0138] The target area threshold value is positively correlated with the target motion blur value, and the target quality threshold value is positively correlated with the target motion blur value.

[0139] As an example, in step S221', after obtaining the first fingerprint image, the fingerprint processing apparatus can calculate the first fingerprint image and the second fingerprint image by using a pre-set motion blur algorithm, determine the output result of the motion blur algorithm as an initial motion blur value, and directly determine the initial motion blur value as the target motion blur value. Alternatively, the target correction coefficient can be determined according to specific conditions, the initial motion blur value is corrected by using the target correction coefficient, and the target motion blur value corresponding to the first fingerprint image is determined. The processing process is the same as that in step S211. To avoid repetition, details are not described herein.

[0140] As an example, in step S222', after obtaining the target motion blur value corresponding to the first fingerprint image, the fingerprint processing apparatus can dynamically determine the target area threshold corresponding to the target motion blur value based on a mapping relationship table or a mapping function between the motion blur value and the area threshold, so as to subsequently evaluate whether the effective area of the first fingerprint image reaches the fingerprint effective standard based on the target area threshold. In this example, the target area threshold is positively correlated with the target motion blur value, that is, the smaller the target motion blur value, the clearer the first fingerprint image (i.e., closer to the non-blurred type), and the smaller the target area threshold needs to be set at this time, so that the clearer first fingerprint image is more likely to pass the effective area control, thereby improving the fingerprint registration efficiency and improving the user registration experience. Conversely, the larger the target motion blur value, the more blurred the first fingerprint image (i.e., closer to the full-blurred type), and the larger the target area threshold needs to be set at this time, so that the more blurred first fingerprint image is more difficult to pass the effective area control, thereby ensuring the effectiveness of the final registered fingerprint template.

[0141] Correspondingly, after obtaining the target motion blur value corresponding to the first fingerprint image, the fingerprint processing apparatus can dynamically determine the target quality threshold corresponding to the target motion blur value based on a mapping relationship table or a mapping function between the motion blur value and the quality threshold, so as to subsequently evaluate whether the quality score corresponding to the first fingerprint image reaches the fingerprint effective standard based on the target quality threshold. In this example, the target quality threshold is positively correlated with the target motion blur value, that is, the smaller the target motion blur value, the clearer the first fingerprint image (i.e., closer to the non-blurred type), and the smaller the target quality threshold needs to be set at this time, so that the clearer first fingerprint image is more likely to pass the image quality control, thereby improving the fingerprint registration efficiency and improving the user registration experience. Conversely, the larger the target motion blur value, the more blurred the first fingerprint image (i.e., closer to the full-blurred type), and the larger the target quality threshold needs to be set at this time, so that the more blurred first fingerprint image is more difficult to pass the image quality control, thereby ensuring the effectiveness of the final registered fingerprint template.

[0142] In an embodiment, the third fingerprint image includes a fourth fingerprint image, and the fourth fingerprint image is a fingerprint image of which the image type determined before the current time is the strong template type.

[0143] The step S23, i.e., the repetition rate detection based on the first fingerprint image and the third fingerprint image, determines the second detection result corresponding to the first fingerprint image, including:

[0144] S231: The repetition rate detection is performed based on the first fingerprint image and all the fourth fingerprint images to determine a first measured repetition rate;

[0145] S232: The repetition rate detection is performed based on the first fingerprint image and the third fingerprint image closest to the current time to determine a second measured repetition rate;

[0146] S233: If the first measured repetition rate is less than the first repetition rate threshold, and the second measured repetition rate is less than the second repetition rate threshold, it is determined that the second detection result corresponding to the first fingerprint image is a detection pass;

[0147] S234: If the first measured repetition rate is not less than the first repetition rate threshold, or the second measured repetition rate is not less than the second repetition rate threshold, it is determined that the second detection result corresponding to the first fingerprint image is a detection fail.

[0148] As an example, in the step S231, after obtaining the first fingerprint image, the fingerprint processing device needs to read all the fourth fingerprint images from the memory. The fourth fingerprint image is a fingerprint image of which the image type determined before the current time is a strong template type, and the strong fingerprint template here is at least one. Then, the first fingerprint image and the fourth fingerprint image are subjected to the repetition rate detection to determine the first measured repetition rate. In this example, the fingerprint processing device can first determine the single repetition rate corresponding to the first fingerprint image and each fourth fingerprint image, and then process the single repetition rates corresponding to all the fourth fingerprint images to determine the first measured repetition rate. The single repetition rate here can be that the overlapping area between the first fingerprint image and a fourth fingerprint image is calculated first, and then the proportion of the overlapping area to the area of the first fingerprint image is calculated, and the proportion is determined as the single repetition rate corresponding to the fourth fingerprint image.

[0149] As an example, the first measured repetition rate is the maximum value in the single repetition rates corresponding to all the fourth fingerprint images, and the single repetition rate is the repetition rate between the fourth fingerprint image and the first fingerprint image.

[0150] In this example, the fingerprint processing apparatus can calculate the repetition rate of the at least one fourth fingerprint image and the first fingerprint image, and determine a single repetition rate corresponding to each fourth fingerprint image. For example, the overlapping area of each fourth fingerprint image and the first fingerprint image is calculated first, and then the proportion of the overlapping area to the area of the first fingerprint image is calculated, and the proportion is determined as the single repetition rate corresponding to the fourth fingerprint image. Then, the fingerprint processing apparatus can compare the single repetition rates corresponding to the at least one fourth fingerprint image, and determine the maximum value as the first measured repetition rate. The greater the first measured repetition rate, the greater the overlapping area of the first fingerprint image and one of the strong fingerprint templates, which should not be retained as a registered fingerprint template to avoid the registration fingerprint database containing multiple fingerprint images with large repetition of fingerprint features, affecting the recognition success rate of subsequent fingerprint recognition; otherwise, the smaller the first measured repetition rate, the more fingerprint features it contains that are different from the fourth fingerprint image with the largest overlapping area, which should be retained as a registered fingerprint template to ensure the recognition success rate of subsequent fingerprint recognition.

[0151] As an example, the first measured repetition rate is the repetition rate between the spliced fingerprint image and the first fingerprint image, and the spliced fingerprint image is the fingerprint image obtained by splicing all fourth fingerprint images.

[0152] In this example, after reading the fourth fingerprint image, the fingerprint processing apparatus can perform splicing processing on all fourth fingerprint images, for example, perform set processing on the fingerprint features corresponding to all fourth fingerprint images to obtain a spliced fingerprint image. Then, the fingerprint processing apparatus can calculate the repetition rate of the spliced fingerprint image and the first fingerprint image to determine the corresponding first measured repetition rate, for example, calculate the overlapping area of the spliced fingerprint image and the first fingerprint image, and then calculate the proportion of the overlapping area to the area of the first fingerprint image, and determine the proportion as the first measured repetition rate. The greater the first measured repetition rate, the greater the overlapping area of the first fingerprint image and the spliced fingerprint image, which should not be retained as a registered fingerprint template; otherwise, the smaller the first measured repetition rate, the more fingerprint features it contains that are different from the spliced fingerprint image, which should be retained as a registered fingerprint template to ensure the recognition success rate of subsequent fingerprint recognition.

[0153] As an example, in step S232, after obtaining the first fingerprint image, the fingerprint processing apparatus can determine the third fingerprint image closest to the current time (which can be a strong template type fingerprint image or a weak template type fingerprint image) from all third fingerprint images; and then perform repetition rate detection on the first fingerprint image and the third fingerprint image closest to the current time to determine the second measured repetition rate. In this example, the fingerprint processing apparatus can first determine the overlapping area of the first fingerprint image and the third fingerprint image closest to the current time, and then calculate the proportion of the overlapping area to the total area of the first fingerprint image, and determine the proportion as the second measured repetition rate.

[0154] The first repetition rate threshold and the second repetition rate threshold are two pre-set repetition rate thresholds, which can be the same or different, for example, both can be a specific value between 70% and 90%.

[0155] As an example, in step S233, when the first measured repetition rate is less than the first repetition rate threshold and the second measured repetition rate is less than the second repetition rate threshold, the fingerprint processing device can determine that there are fewer repeated fingerprint features between the first fingerprint image and all fourth fingerprint images, and there are fewer repeated fingerprint features between the first fingerprint image and the third fingerprint image closest to the current time. It indicates that the first fingerprint image contains more fingerprint features that are not in the third fingerprint image, and should be retained as a registered fingerprint template to ensure the accuracy and effectiveness of subsequent identification. Therefore, the second detection result corresponding to the first fingerprint image is determined to be a detection pass, so that it passes the repetition rate control.

[0156] As an example, in step S234, when the first measured repetition rate is not less than the first repetition rate threshold, or the second measured repetition rate is not less than the second repetition rate threshold, the fingerprint processing device can determine that there are more repeated fingerprint features between the first fingerprint image and the fourth fingerprint image, or there are more repeated fingerprint features between the first fingerprint image and the third fingerprint image closest to the current time. It indicates that the first fingerprint image contains fewer fingerprint features that are not in the third fingerprint image, and should not be retained as a registered fingerprint template to avoid the number of effective fingerprint templates being limited to identify the fingerprint features, which affects the accuracy and effectiveness of subsequent identification. Therefore, the second detection result corresponding to the first fingerprint image is determined to be a detection failure, so that it cannot pass the repetition rate control.

[0157] In an embodiment, before step S23, i.e., based on the first fingerprint image and the third fingerprint image, the repetition rate detection is performed to determine the second detection result corresponding to the first fingerprint image, the fingerprint processing method further comprises:

[0158] S231’: Obtain a first cumulative number, the first cumulative number being a number of continuously obtained first fingerprint images of a weak template type;

[0159] S232’: Determine the first repetition rate threshold and the second repetition rate threshold based on the first cumulative number;

[0160] The first repetition rate threshold is negatively related to the first cumulative number; and the second repetition rate threshold is negatively related to the first cumulative number.

[0161] As an example, in step S231', the fingerprint processing device, after acquiring each first fingerprint image, performing image analysis on the first fingerprint image, and determining the image type corresponding to the first fingerprint image, needs to update the first cumulative number based on the image type corresponding to the first fingerprint image, where the first cumulative number is the number of consecutively acquired first fingerprint images of the weak template type. In this example, if the image type corresponding to the first fingerprint image is the weak template type, the first cumulative number N is updated as N=N+1; if the image type corresponding to the first fingerprint image is not the weak template type, i.e., the image type is the weak template type or the invalid type, the first cumulative number N is updated as N=0, so as to count the number of consecutively acquired fingerprint images of the weak template type.

[0162] As an example, in step S232', after acquiring the first cumulative number, the fingerprint processing device can dynamically determine the first repetition rate threshold and the second repetition rate threshold corresponding to the first cumulative number based on a mapping relationship table or a mapping function between the cumulative number and the repetition rate threshold. In this example, the first repetition rate threshold is negatively correlated with the first cumulative number, and the second repetition rate threshold is negatively correlated with the first cumulative number, i.e., the greater the first cumulative number, the more the consecutively acquired fingerprint images of the weak template type, at this time, the first repetition rate threshold and the second repetition rate threshold need to be increased to improve the card control requirement of the repetition rate, so as to avoid that the number of acquired fingerprint images of the weak template type reaches its first number threshold (which is a pre-set number of weak fingerprint templates to be acquired, for example, can be set to 20) too quickly, resulting in subsequent determination of the processing efficiency of the cached target fingerprint image; on the contrary, the smaller the first cumulative number, the fewer the consecutively acquired fingerprint images of the weak template type, at this time, the first repetition rate threshold and the second repetition rate threshold need to be reduced to reduce the card control requirement of the repetition rate.

[0163] In an embodiment, step S24, i.e., determining the image type corresponding to the first fingerprint image based on the motion blur type corresponding to the first fingerprint image, the first detection result, and the second detection result, comprises:

[0164] S241: if the motion blur type is the non-blur type, the first detection result is the detection pass, and the second detection result is the detection pass, it is determined that the image type corresponding to the first fingerprint image is the strong template type;

[0165] S242: if the motion blur type is the non-blur type, the first detection result is the detection pass, and the second detection result is the detection fail, it is determined that the image type corresponding to the first fingerprint image is the weak template type; or, if the motion blur type is the semi-blur type, the first detection result is the detection pass, and the second detection result is the detection pass, it is determined that the image type corresponding to the first fingerprint image is the weak template type.

[0166] As an example, in step S241, when the motion blur type corresponding to the first fingerprint image is the non-blur type, the first detection result corresponding to the first fingerprint image is the detection pass, and the second detection result corresponding to the first fingerprint image is the detection pass, it can be determined that the fingerprint ridge of the first fingerprint image is normal, the effectiveness determined based on the effective area and the image quality is high, and the repetition rate with the third fingerprint image is low, which meets the preset condition for determining the strong fingerprint template, and thus the image type of the first fingerprint image can be determined as the strong template type. That is, when the definition of the first fingerprint image is high, the effectiveness is high, and the repetition rate with other registered fingerprint templates is low, it can be registered as a strong fingerprint template.

[0167] As an example, in step S242, when the motion blur type corresponding to the first fingerprint image is the non-blur type, the first detection result corresponding to the first fingerprint image is the detection pass, and the second detection result corresponding to the first fingerprint image is the detection fail, it can be determined that the fingerprint ridge of the first fingerprint image is normal, the effectiveness determined based on the effective area and the image quality is high, but the repetition rate with the third fingerprint image is high, which meets the preset condition for determining the weak fingerprint template, and thus the image type of the first fingerprint image can be determined as the weak template type. That is, when the definition of the first fingerprint image is high, the effectiveness is high, and the repetition rate with other registered fingerprint templates is high, in order to avoid missing the fingerprint features of part of the effective pressing area, it can be cached as a target fingerprint image, so as to be registered as a weak fingerprint template subsequently.

[0168] Alternatively, when the motion blur type corresponding to the first fingerprint image is the semi-blur type, the first detection result corresponding to the first fingerprint image is the detection pass, and the second detection result corresponding to the first fingerprint image is the detection pass, it can be determined that the fingerprint ridge of the first fingerprint image is deformed but in a state of small deformation degree, the effectiveness determined based on the effective area and the image quality is high, and the repetition rate with the third fingerprint image is low, which meets the preset condition for determining the weak fingerprint template, and thus the image type of the first fingerprint image can be determined as the weak template type. That is, when the definition of the first fingerprint image is moderate, the effectiveness is high, and the repetition rate with other registered fingerprint templates is low, it can be registered as a strong fingerprint template, in order to avoid missing the fingerprint features of part of the effective pressing area, it can be cached as a target fingerprint image, so as to be registered as a weak fingerprint template subsequently.

[0169] Further, the fingerprint processing apparatus determines that the image type corresponding to the first fingerprint image is an invalid type when the motion blur type corresponding to the first fingerprint image, the first detection result and the second detection result do not satisfy the preset condition for determining the strong template type or the weak template type, so as to delete the first fingerprint image subsequently and no longer process the first fingerprint image, which helps to save processing resources and repeatedly perform the operations of obtaining the first fingerprint image and the subsequent operations.

[0170] In an embodiment, as shown in FIG. 3, the step S3 of determining the target fingerprint image based on the first fingerprint image comprises: Figure 4

[0171] S31: Obtain the image quantity corresponding to the fifth fingerprint image, the fifth fingerprint image being a fingerprint image of which the image type determined before the current time is the weak template type.

[0172] S32: If the image quantity corresponding to the fifth fingerprint image is less than a first quantity threshold, determine the first fingerprint image and all the fifth fingerprint images as the target fingerprint image.

[0173] S33: If the image quantity corresponding to the fifth fingerprint image is not less than the first quantity threshold, analyze the first fingerprint image and all the fifth fingerprint images to determine the target fingerprint image.

[0174] The first quantity threshold is a preset quantity of weak fingerprint templates to be collected, and can also be understood as a maximum quantity of target fingerprint images that can be buffered, for example, the first quantity threshold can be set to 20.

[0175] As an example, in the step S31, when the fingerprint processing apparatus determines that the image type corresponding to the first fingerprint image is the weak template type, it needs to first determine the image quantity corresponding to the fifth fingerprint image pre-buffered in the memory, compare the image quantity with the first quantity threshold set in advance, and determine the target fingerprint image to be buffered according to the comparison result.

[0176] As an example, in the step S32, when the image quantity corresponding to the fifth fingerprint image is less than the first quantity threshold, the fingerprint processing apparatus can determine that the fifth fingerprint image buffered in the memory at the current time is less, and can directly determine the first fingerprint image as the target fingerprint image to be buffered. That is, in this case, the first fingerprint image and all the fifth fingerprint images can be determined as the target fingerprint image to be buffered.

[0177] ​As an example, in step S33, the fingerprint processing apparatus can determine that there are too many fifth fingerprint images stored in the memory at the current time, when the number of images corresponding to the fifth fingerprint images is not less than the first quantity threshold, and needs to analyze and process the first fingerprint image and all the fifth fingerprint images to delete one of them and keep the rest as target fingerprint images that need to be stored. That is, in this case, according to the analysis result, the first fingerprint image can be deleted, or one of the fifth fingerprint images can be deleted, and the rest of the fingerprint images are determined as target fingerprint images.

[0178] For example, when the number of images corresponding to the fifth fingerprint images is not less than the first quantity threshold, the fingerprint processing apparatus can obtain the storage time of all the fifth fingerprint images, delete the fifth fingerprint image with the longest storage time, and determine the rest of the fifth fingerprint images and the first fingerprint image as target fingerprint images that need to be stored, so that all the target fingerprint images are registered as weak fingerprint templates at the end of the fingerprint registration.

[0179] In an embodiment, step S33, i.e., analyzing the first fingerprint image and all the fifth fingerprint images to determine the target fingerprint images, includes:

[0180] S331: determining the priority corresponding to the first fingerprint image;

[0181] S332: if the priority corresponding to the first fingerprint image is less than the minimum priority of all the fifth fingerprint images, deleting the first fingerprint image and determining all the fifth fingerprint images as target fingerprint images;

[0182] S333: if the priority corresponding to the first fingerprint image is not less than the minimum priority of all the fifth fingerprint images, deleting the fifth fingerprint image corresponding to the minimum priority and determining the first fingerprint image and the rest of the fifth fingerprint images as target fingerprint images.

[0183] As an example, in step S331, the fingerprint processing apparatus can use a pre-set priority determination logic to analyze and process the first fingerprint image to determine the priority of the first fingerprint image, which can be determined based on at least one of the image properties such as the clarity, effective area, image quality and repetition rate of the fingerprint image. Then, the priority of the first fingerprint image is compared with the minimum priority corresponding to all the fifth fingerprint images in the cache to determine the fingerprint image that needs to be deleted according to the comparison result. In this example, the fingerprint processing apparatus analyzes and stores the priority corresponding to the first fingerprint image each time the first fingerprint image is obtained, so as to facilitate subsequent priority comparison.

[0184] As an example, in step S332, when the priority corresponding to the first fingerprint image is less than the minimum priority of all the fifth fingerprint images, the fingerprint processing device determines that the priority of the first fingerprint image is less than the priority of all the fifth fingerprint images, and thus the first fingerprint image can be deleted, and all the fifth fingerprint images are determined as the target fingerprint images, so as to ensure the priority of the fingerprint image with higher priority.

[0185] As an example, in step S333, when the priority corresponding to the first fingerprint image is greater than or equal to the minimum priority of all the fifth fingerprint images, the fingerprint processing device deletes the fifth fingerprint image corresponding to the minimum priority, and determines the first fingerprint image and the remaining fifth fingerprint images as the target fingerprint images, so as to ensure the priority of the fingerprint image with higher priority and ensure that the target fingerprint image is closest to the current time in terms of cache time.

[0186] In an embodiment, in step S331, determining the priority corresponding to the first fingerprint image comprises:

[0187] S3311: performing motion blur identification based on the first fingerprint image and a second fingerprint image to determine a target motion blur value, and determining a first index score corresponding to the first fingerprint image based on the target motion blur value, the second fingerprint image being a fingerprint image collected at a previous time;

[0188] S3312: performing effective area and image quality detection on the first fingerprint image to determine an effective area and quality score, and determining a second index score corresponding to the first fingerprint image based on the effective area and quality score;

[0189] S3313: performing repetition rate detection based on the first fingerprint image and a third fingerprint image to determine an actual repetition rate, and determining a third index score corresponding to the first fingerprint image based on the actual repetition rate, the third fingerprint image being a fingerprint image of which the image type determined before the current time is a strong template type or a weak template type;

[0190] S3314: determining the priority corresponding to the first fingerprint image based on the first index score, the second index score, and the third index score corresponding to the first fingerprint image.

[0191] As an example, in step S3311, the fingerprint processing device can analyze the first fingerprint image and the second fingerprint image by using a pre-set motion blur algorithm to determine the target motion blur value corresponding to the first fingerprint image, and the processing process is similar to that in step S21. To avoid repetition, details are not described herein. After determining the target motion blur value corresponding to the first fingerprint image, the fingerprint processing device can directly determine the target motion blur value as the first index score, or can normalize or process the target motion blur value to determine the first index score used for evaluating the priority.

[0192] As an example, in step S3312, the fingerprint processing device can perform effective area calculation on the first fingerprint image, determine the effective area corresponding to the first fingerprint image, and perform image quality analysis on the first fingerprint image to determine the quality score corresponding to the first fingerprint image. The processing process is similar to step S22, and details are not repeated here. After determining the effective area and image quality corresponding to the first fingerprint image, the fingerprint processing device can perform normalization processing on the effective area and image quality corresponding to the first fingerprint image to determine a second index score for evaluating priority; or, based on the effective area and image quality corresponding to the first fingerprint image, query a pre-set mapping table reflecting the combination of the two and the index score to determine the second index score for evaluating priority.

[0193] As an example, in step S3313, the fingerprint processing device can perform overlap area detection on the first fingerprint image and the third fingerprint image to determine the overlap area of the two; and then based on the overlap area and the area of the first fingerprint image, determine the ratio of the two, and determine the measured repetition rate as the ratio of the two. The processing process is similar to step S23, and details are not repeated here. After determining the measured repetition rate corresponding to the first fingerprint image, the fingerprint processing device can perform normalization processing on the measured repetition rate to determine a third index score for evaluating priority; or, based on the measured repetition rate, query a pre-set mapping table reflecting the repetition rate and the index score to determine the third index score for evaluating priority.

[0194] As an example, in step S3314, after determining the first index score, the second index score, and the third index score corresponding to the first fingerprint image, the fingerprint processing device can directly add the first index score, the second index score, and the third index score corresponding to the first fingerprint image to determine the priority of the first fingerprint image; or, the fingerprint processing device can perform weighting processing on the first index score, the second index score, and the third index score corresponding to the first fingerprint image to determine the priority of the first fingerprint image. The weights of the index scores in the weighting processing can be fixed values or dynamic values determined according to actual conditions.

[0195] In this example, motion blur recognition is performed based on the first fingerprint image and the second fingerprint image, and a first index score reflecting the change in fingerprint lines is determined based on the determined target motion blur value. The effective area and image quality of the first fingerprint image are detected to determine a second index score reflecting whether the effectiveness standard is met from the quality of the fingerprint image itself. The repetition rate of the first fingerprint image and the third fingerprint image is detected to determine a third index score reflecting the repetition of fingerprint features. The priority of the first fingerprint image is dynamically determined based on the first index score, the second index score, and the third index score corresponding to the first fingerprint image, so as to comprehensively determine the priority thereof from multiple dimensions, thereby ensuring the effectiveness of the subsequent retained target fingerprint image and improving the recognition success of the subsequent fingerprint recognition as a weak fingerprint template.

[0196] In an embodiment, step S3314, i.e., determining the priority of the first fingerprint image based on the first index score, the second index score, and the third index score corresponding to the first fingerprint image, comprises:

[0197] S33141: determining the fingerprint acquisition mode corresponding to the first fingerprint image;

[0198] S33142: if the fingerprint acquisition mode is the pressing mode, performing weighted processing on the first index score, the second index score, and the third index score corresponding to the first fingerprint image based on a first weight combination to determine the priority of the first fingerprint image; in the first weight combination, the weight of the first index score is less than the weight of the third index score.

[0199] S33143: if the fingerprint acquisition mode is the sliding mode, performing weighted processing on the first index score, the second index score, and the third index score corresponding to the first fingerprint image based on a second weight combination to determine the priority of the first fingerprint image; in the second weight combination, the weight of the first index score is greater than the weight of the third index score.

[0200] As an example, in step S33141, the fingerprint processing device can determine the fingerprint acquisition mode corresponding to each first fingerprint image, which can be determined according to the built-in configuration information or by analyzing a plurality of continuous first fingerprint images, i.e., the fingerprint acquisition mode can be determined autonomously according to the actual situation.

[0201] The first weight combination refers to the weight combination for weighting the index scores in the pressing mode, and the first weight combination includes the weight of the first index score, the weight of the second index score, and the weight of the third index score, and the sum of the weights of the three index scores is 1.

[0202] As an example, in step S33142, when the fingerprint processing device determines that the fingerprint collection manner is the pressing manner, i.e., the user's finger is pressed to the fingerprint sensor, the fingerprint sensor collects a corresponding first fingerprint image, and when evaluating the priority of the first fingerprint image, the first weight combination is needed to be based on, the first weight combination is used to weight the first index score, the second index score and the third index score corresponding to the first fingerprint image, and determine the priority of the first fingerprint image; since in the first weight combination, the weight of the first index score is less than the weight of the third index score, the index score of the blur motion factor in the first fingerprint image collected in the pressing manner is reduced, and the index score of the repeatability factor in the first fingerprint image collected in the pressing manner is improved, so as to adapt to specific scenes.

[0203] The second weight combination is a weight combination for weighting the index scores in the sliding manner, and the second weight combination includes the weight of the first index score, the weight of the second index score and the weight of the third index score, and the sum of the weights of the three index scores is 1.

[0204] As an example, in step S33143, when the fingerprint processing device determines that the fingerprint collection manner is the sliding manner, i.e., the user's finger slides or drags through the fingerprint sensor, the fingerprint sensor collects a corresponding first fingerprint image, and when evaluating the priority of the first fingerprint image, the second weight combination is needed to be based on, the second weight combination is used to weight the first index score, the second index score and the third index score corresponding to the first fingerprint image, and determine the priority of the first fingerprint image; since in the first weight combination, the weight of the first index score is greater than the weight of the third index score, the index score of the blur motion factor in the first fingerprint image collected in the pressing manner is improved, and the index score of the repeatability factor in the first fingerprint image collected in the pressing manner is reduced, so as to adapt to specific scenes.

[0205] In an embodiment, the current state data includes a second cumulative number, and the second cumulative number is the number of strong fingerprint templates that have been registered.

[0206] The registration end condition includes that the second cumulative number is greater than a second number threshold.

[0207] The second cumulative number is the number of strong fingerprint templates that have been registered. The second number threshold is a number threshold for evaluating whether to end the fingerprint registration operation.

[0208] As an example, the fingerprint processing device needs to update the second cumulative number M corresponding to the strong fingerprint template to M=M+1 when registering the first fingerprint image as a strong fingerprint template; then, the second cumulative number is compared with the second number threshold; if the second cumulative number is greater than the second number threshold, it is determined that it has met the registration end condition, and the target fingerprint image can be registered as a weak fingerprint template, and the fingerprint registration is completed based on all strong fingerprint templates and all weak fingerprint templates; otherwise, if the second cumulative number is not greater than the second number threshold, it is determined that it does not meet the registration end condition, and repeated image acquisition is needed, that is, step S1 is repeatedly executed.

[0209] In an embodiment, the current state data includes an effective area ratio, and the effective area ratio is a ratio of an overlapping area after splicing of all strong fingerprint templates to an area of a standard fingerprint template.

[0210] The registration end condition includes that the effective area ratio is greater than a preset ratio threshold.

[0211] The effective area ratio is a ratio of an overlapping area after splicing of all strong fingerprint templates to an area of a standard fingerprint template, and specifically, after the first fingerprint image is registered as a strong fingerprint template, all strong fingerprint templates are spliced to determine the corresponding overlapping area, which can be understood as the overlapping area of the first fingerprint image and the previously determined spliced fingerprint image. The preset ratio threshold is a preset ratio threshold for evaluating whether to end the fingerprint registration process. The standard fingerprint template is a pre-set fingerprint template, which can be a fingerprint template with a total area determined by a pre-marking test.

[0212] As an example, after the first fingerprint image is registered as a strong fingerprint template, the fingerprint processing device needs to determine the overlapping area based on splicing of all strong fingerprint templates in the fingerprint template library, and then calculate the effective area ratio by comparing the overlapping area with the area of the standard fingerprint template; then, the effective area ratio is compared with the preset ratio threshold; if the effective area ratio is greater than the preset ratio threshold, it is determined that it has met the registration end condition, and the target fingerprint image can be registered as a weak fingerprint template, and the fingerprint registration is completed based on all strong fingerprint templates and all weak fingerprint templates; otherwise, if the effective area ratio is not greater than the preset ratio threshold, it is determined that it does not meet the registration end condition, and repeated image acquisition is needed, that is, step S1 is repeatedly executed.

[0213] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0214] In an embodiment, a fingerprint processing apparatus is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the fingerprint processing method in the above embodiments when executing the computer program, for example Figure 2 S1-S5, or Figures 3-4 For the sake of brevity, the details are not repeated here.

[0215] In an embodiment, a fingerprint processing system is provided, comprising a fingerprint sensor and the above fingerprint processing apparatus, wherein the fingerprint processing apparatus is connected to the fingerprint sensor, and for the sake of brevity, the details are not repeated here.

[0216] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of fingerprint processing, characterized by, The method comprises the following steps: acquiring a first fingerprint image, the first fingerprint image being a fingerprint image collected at a current time; performing image analysis on the first fingerprint image to determine the image type corresponding to the first fingerprint image; if the image type corresponding to the first fingerprint image is a weak template type, determining a target fingerprint image based on the first fingerprint image, and caching the target fingerprint image, comprising: acquiring the number of images corresponding to a fifth fingerprint image, the fifth fingerprint image being a fingerprint image determined before the current time and having an image type of a weak template type; if the number of images corresponding to the fifth fingerprint image is less than a first quantity threshold, determining the first fingerprint image and all the fifth fingerprint images as the target fingerprint image; if the number of images corresponding to the fifth fingerprint image is not less than the first quantity threshold, performing analysis on the first fingerprint image and all the fifth fingerprint images to determine the target fingerprint image; if the image type corresponding to the first fingerprint image is a strong template type, registering the first fingerprint image as a strong fingerprint template, and updating the current state data corresponding to the strong fingerprint template; when the current state data corresponding to the strong fingerprint template meets a registration end condition, registering the target fingerprint image as a weak fingerprint template, and completing fingerprint registration based on the strong fingerprint template and the weak fingerprint template.

2. The method of claim 1, wherein, The method comprises the following steps: performing motion blur identification based on the first fingerprint image and a second fingerprint image to determine the motion blur type corresponding to the first fingerprint image, the second fingerprint image being a fingerprint image collected at a previous time; performing effective area and image quality detection on the first fingerprint image to determine a first detection result corresponding to the first fingerprint image; performing repetition rate detection based on the first fingerprint image and a third fingerprint image to determine a second detection result corresponding to the first fingerprint image, the third fingerprint image being a fingerprint image determined before the current time and having an image type of a strong template type or a weak template type; determining the image type corresponding to the first fingerprint image based on the motion blur type corresponding to the first fingerprint image, the first detection result, and the second detection result.

3. The method of claim 2, wherein, The method comprises the following steps: performing motion blur identification on the first fingerprint image and the second fingerprint image to determine a target motion blur value corresponding to the first fingerprint image; determining the motion blur type corresponding to the first fingerprint image based on the target motion blur value.

4. The method of claim 2, wherein, The method comprises the following steps: performing effective area and image quality detection on the first fingerprint image to determine the effective area and the quality score corresponding to the first fingerprint image; if the effective area is greater than a target area threshold and the quality score is greater than a target quality threshold, determining that the first detection result corresponding to the first fingerprint image is a detection pass. If the effective area is not greater than the target area threshold or the quality score is not greater than the target quality threshold, it is determined that the first detection result corresponding to the first fingerprint image is a detection failure.

5. The method of claim 4, wherein, Before the effective area and image quality detection on the first fingerprint image is performed to determine the first detection result corresponding to the first fingerprint image, the fingerprint processing method further includes: Performing motion blur identification on the first fingerprint image and the second fingerprint image to determine a target motion blur value corresponding to the first fingerprint image; Determining the target area threshold and the target quality threshold based on the target motion blur value; The target area threshold is positively correlated with the target motion blur value, and the target quality threshold is positively correlated with the target motion blur value.

6. The method of claim 2, wherein, The third fingerprint image includes a fourth fingerprint image, and the fourth fingerprint image is a fingerprint image of which the image type determined before the current time is a strong template type; The repetition rate detection based on the first fingerprint image and the third fingerprint image to determine the second detection result corresponding to the first fingerprint image includes: Performing repetition rate detection based on the first fingerprint image and all the fourth fingerprint images to determine a first measured repetition rate; Performing repetition rate detection based on the first fingerprint image and the third fingerprint image closest to the current time to determine a second measured repetition rate; If the first measured repetition rate is less than a first repetition rate threshold and the second measured repetition rate is less than a second repetition rate threshold, it is determined that the second detection result corresponding to the first fingerprint image is a detection pass; If the first measured repetition rate is not less than the first repetition rate threshold or the second measured repetition rate is not less than the second repetition rate threshold, it is determined that the second detection result corresponding to the first fingerprint image is a detection failure.

7. The method of claim 6, wherein, The first measured repetition rate is the maximum value in single repetition rates corresponding to all the fourth fingerprint images, and the single repetition rate is the repetition rate between the fourth fingerprint image and the first fingerprint image; Or, the first measured repetition rate is the repetition rate between a spliced fingerprint image and the first fingerprint image, and the spliced fingerprint image is a fingerprint image obtained by splicing all the fourth fingerprint images.

8. The method of claim 6, wherein, Before the repetition rate detection based on the first fingerprint image and the third fingerprint image to determine the second detection result corresponding to the first fingerprint image, the fingerprint processing method further includes: Obtaining a first cumulative number, which is the number of first fingerprint images of which the image type is a weak template type that are continuously obtained; Determining the first repetition rate threshold and the second repetition rate threshold based on the first cumulative number; The first repetition rate threshold is negatively correlated with the first cumulative number, and the second repetition rate threshold is negatively correlated with the first cumulative number.

9. The method of claim 2, wherein, The determination of the image type corresponding to the first fingerprint image based on the motion blur type corresponding to the first fingerprint image, the first detection result and the second detection result includes: If the motion blur type is a non-blur type, the first detection result is a detection pass, and the second detection result is a detection pass, it is determined that the image type corresponding to the first fingerprint image is a strong template type; If the motion blur type is a non-blur type, the first detection result is a detection pass, and the second detection result is a detection pass, it is determined that the image type corresponding to the first fingerprint image is a strong template type; Or, if the motion blur type is a semi-blur type, the first detection result is a detection pass, and the second detection result is a detection pass, it is determined that the image type corresponding to the first fingerprint image is a weak template type.

10. The method of claim 1, wherein, The analysis of the first fingerprint image and all the fifth fingerprint images to determine a target fingerprint image comprises: determining the priority of the first fingerprint image; if the priority of the first fingerprint image is less than the minimum priority of all the fifth fingerprint images, deleting the first fingerprint image and determining all the fifth fingerprint images as the target fingerprint image; if the priority of the first fingerprint image is not less than the minimum priority of all the fifth fingerprint images, deleting the fifth fingerprint image corresponding to the minimum priority and determining the first fingerprint image and the remaining fifth fingerprint images as the target fingerprint image.

11. The method of claim 10, wherein, The determination of the priority of the first fingerprint image comprises: motion blur recognition based on the first fingerprint image and a second fingerprint image to determine a target motion blur value, determination of a first index score corresponding to the first fingerprint image based on the target motion blur value, the second fingerprint image being a fingerprint image collected at the previous moment; effective area and image quality detection of the first fingerprint image to determine an effective area and a quality score, determination of a second index score corresponding to the first fingerprint image based on the effective area and the quality score; repetition rate detection based on the first fingerprint image and a third fingerprint image to determine an actual repetition rate, determination of a third index score corresponding to the first fingerprint image based on the actual repetition rate, the third fingerprint image being a fingerprint image whose image type is determined as a strong template type or a weak template type before the current moment; determination of the priority of the first fingerprint image based on the first index score, the second index score, and the third index score corresponding to the first fingerprint image.

12. The method of claim 11, wherein, The determination of the priority of the first fingerprint image based on the first index score, the second index score, and the third index score corresponding to the first fingerprint image comprises: determination of the fingerprint collection mode corresponding to the first fingerprint image; if the fingerprint collection mode is a pressing mode, weighted processing of the first index score, the second index score, and the third index score corresponding to the first fingerprint image based on a first weight combination to determine the priority of the first fingerprint image, the weight of the first index score being less than the weight of the third index score in the first weight combination; If the fingerprint collection mode is the sliding mode, the first index score, the second index score and the third index score corresponding to the first fingerprint image are weighted and processed based on a second weight combination to determine a priority of the first fingerprint image; in the second weight combination, the weight of the first index score is greater than the weight of the third index score.

13. The method of claim 1, wherein, The current state data includes a second cumulative number, which is a number of strong fingerprint templates that have been registered; and the registration end condition includes that the second cumulative number is greater than a second number threshold. Alternatively, the current state data includes an effective area ratio, which is a ratio of an overlapping area after splicing of all strong fingerprint templates to an area of a standard fingerprint template; and the registration end condition includes that the effective area ratio is greater than a preset ratio threshold.

14. A fingerprint processing apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the fingerprint processing method of any one of claims 1 to 13 when executing the computer program.

15. A fingerprint processing system characterized by, The fingerprint processing device of claim 14 is connected to the fingerprint sensor.

Images