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] This invention relates to the field of fingerprint recognition technology, and in particular to a fingerprint processing method, apparatus and system. Background Technology

[0002] Fingerprint recognition technology is used in electronic devices to identify users based on their fingerprints. Before fingerprint recognition, fingerprint registration is usually required, which involves registering the collected fingerprint images into a fingerprint template library. During use, the fingerprint images collected in real time are compared with the fingerprint template library to achieve fingerprint recognition.

[0003] The current fingerprint registration process requires collecting multiple fingerprint images and evaluating whether each fingerprint image meets the registration standards. Fingerprint images that meet the registration standards are then registered into the fingerprint template library. This process is time-consuming, inefficient, and negatively impacts the user registration experience. Summary of the Invention

[0004] This invention provides a fingerprint processing method, apparatus, and system to solve the problems of long registration time, low registration efficiency, and negative impact on user registration experience in existing fingerprint registration processes.

[0005] A fingerprint processing method, comprising: Acquire a first fingerprint image, which is the fingerprint image collected at the current moment; Perform 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, then based on the first fingerprint image, a target fingerprint image is determined and the target fingerprint image is cached; If the image type corresponding to the first fingerprint image is a strong template type, then the first fingerprint image is registered as a strong fingerprint template, and the current state data corresponding to the strong fingerprint template is updated. When the current state data corresponding to the strong fingerprint template meets the registration end condition, the target fingerprint image is registered as a weak fingerprint template, and fingerprint registration is completed based on the strong fingerprint template and the weak fingerprint template.

[0006] Preferably, the step of performing image analysis on the first fingerprint image to determine the image type corresponding to the first fingerprint image includes: 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. Perform effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image; Based on the first fingerprint image and the third fingerprint image, a repetition rate detection is performed to determine the second detection result corresponding to the first fingerprint image. 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. Based on the motion blur type corresponding to the first fingerprint image, the first detection result, and the second detection result, the image type corresponding to the first fingerprint image is determined.

[0007] Preferably, the step of performing motion blur recognition based on the first fingerprint image and the second fingerprint image to determine the motion blur type corresponding to the first fingerprint image includes: Motion blur recognition is performed on the first fingerprint image and the second fingerprint image to determine the target motion blur value corresponding to the first fingerprint image; Based on the target motion blur value, the motion blur type corresponding to the first fingerprint image is determined.

[0008] Preferably, the step of performing effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image includes: The effective area and image quality of the first fingerprint image are detected to determine the effective area and quality score of the first fingerprint image. If the effective area is greater than the target area threshold and the quality score is greater than the target quality threshold, then the first detection result corresponding to the first fingerprint image is determined to be a successful detection. If the effective area is not greater than the target area threshold, or the quality score is not greater than the target quality threshold, then the first detection result corresponding to the first fingerprint image is determined to be a detection failure.

[0009] Preferably, before performing effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image, the fingerprint processing method further includes: Motion blur recognition is performed on the first fingerprint image and the second fingerprint image to determine the target motion blur value corresponding to the first fingerprint image; Based on the target motion blur value, the target area threshold and the target mass threshold are determined; 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.

[0010] Preferably, the third fingerprint image includes a fourth fingerprint image, which is a fingerprint image whose image type was previously determined to be a strong template type at the current moment; The step of performing 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: Based on the first fingerprint image and all the fourth fingerprint images, a repetition rate detection is performed to determine the first measured repetition rate; Based on the first fingerprint image and the third fingerprint image closest to the current moment, a repetition rate is detected to determine the second measured repetition rate; 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, then the second detection result corresponding to the first fingerprint image is determined to be a successful detection. 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, then the second detection result corresponding to the first fingerprint image is determined to be a detection failure.

[0011] Preferably, the first measured repetition rate is the maximum value among 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; Alternatively, the first measured repetition rate is the repetition rate between the stitched fingerprint image and the first fingerprint image, where the stitched fingerprint image is the fingerprint image obtained by stitching together all the fourth fingerprint images.

[0012] Preferably, before performing 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: Obtain a first cumulative number, where the first cumulative number is the number of consecutively acquired first fingerprint images of the weak template type. Based on the first cumulative number, determine the first repetition rate threshold and the second repetition rate threshold; Wherein, the first repetition rate threshold is negatively correlated with the first cumulative quantity; the second repetition rate threshold is negatively correlated with the first cumulative quantity.

[0013] Preferably, determining the image type corresponding to the first fingerprint image based on the motion blur type, the first detection result, and the second detection result includes: If the motion blur type is a non-blurred type, the first detection result is a pass, and the second detection result is a pass, then the image type corresponding to the first fingerprint image is determined to be a strong template type. If the motion blur type is non-blurred, the first detection result is a successful detection, and the second detection result is a failed detection, then the image type corresponding to the first fingerprint image is determined to be a weak template type. Alternatively, if the motion blur type is a semi-blurred type, the first detection result is a pass, and the second detection result is a pass, then the image type corresponding to the first fingerprint image is determined to be a weak template type.

[0014] Preferably, determining the target fingerprint image based on the first fingerprint image includes: Obtain the number of images corresponding to the fifth fingerprint image, wherein the fifth fingerprint image is a fingerprint image whose image type was previously determined to be a weak template type at the current moment; If the number of images corresponding to the fifth fingerprint image is less than the first number threshold, then the first fingerprint image and all the fifth fingerprint images are determined as the target fingerprint images; If the number of images corresponding to the fifth fingerprint image is not less than the first number threshold, then the first fingerprint image and all the fifth fingerprint images are analyzed to determine the target fingerprint image.

[0015] Preferably, the step of analyzing the first fingerprint image and all the fifth fingerprint images to determine the target fingerprint image includes: Determine the priority corresponding to the first fingerprint image; If the priority of the first fingerprint image is less than the minimum priority of all the fifth fingerprint images, then the first fingerprint image is deleted and all the fifth fingerprint images are determined as the target fingerprint images. If the priority of the first fingerprint image is not less than the minimum priority of all the fifth fingerprint images, then delete the fifth fingerprint image corresponding to the minimum priority, and determine the first fingerprint image and the remaining fifth fingerprint images as the target fingerprint images.

[0016] Preferably, determining the priority corresponding to the first fingerprint image includes: Motion blur recognition is performed based on the first fingerprint image and the second fingerprint image to determine the target motion blur value. Based on the target motion blur value, the first index score corresponding to the first fingerprint image is determined. The second fingerprint image is the fingerprint image collected at the previous moment. The effective area and image quality of the first fingerprint image are detected to determine the effective area and quality score. Based on the effective area and the quality score, the second index score corresponding to the first fingerprint image is determined. Based on the first fingerprint image and the third fingerprint image, a repetition rate is detected to determine the measured repetition rate. Based on the measured repetition rate, a third index score corresponding to the first fingerprint image is determined. The third fingerprint image is a fingerprint image whose image type was determined to be either a strong template type or a weak template type before the current time. The priority of the first fingerprint image is determined based on the first index score, the second index score, and the third index score corresponding to the first fingerprint image.

[0017] Preferably, determining the priority of the first fingerprint image based on the first index score, second index score, and third index score corresponding to the first fingerprint image includes: Determine the fingerprint acquisition method corresponding to the first fingerprint image; If the fingerprint acquisition method is a press method, then based on the first weight combination, the first indicator score, the second indicator score and the third indicator score corresponding to the first fingerprint image are weighted to determine the priority of the first fingerprint image; in the first weight combination, the weight of the first indicator score is less than the weight of the third indicator score. If the fingerprint acquisition method is a sliding method, then based on the second weight combination, the first indicator score, the second indicator score and the third indicator score corresponding to the first fingerprint image are weighted to determine the priority of the first fingerprint image; in the second weight combination, the weight of the first indicator score is greater than the weight of the third indicator score.

[0018] Preferably, the current status data includes a second cumulative number, which is the number of times a strong fingerprint template has been registered; the registration termination condition includes the second cumulative number being greater than a second threshold number. Alternatively, the current status data may include an effective area ratio, which is the ratio of the overlapping area of ​​all strong fingerprint templates to the area of ​​the standard fingerprint template; the registration termination condition may include the effective area ratio being greater than a preset ratio threshold.

[0019] A fingerprint processing apparatus includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the fingerprint processing method described above.

[0020] A fingerprint processing system includes a fingerprint sensor and the aforementioned fingerprint processing device, wherein the fingerprint processing device is connected to the fingerprint sensor.

[0021] The aforementioned fingerprint processing method, apparatus, and system perform image analysis on the first fingerprint image to determine its corresponding image type. Based on the image type, different processing methods are applied. Specifically, when the image type is a weak template type, a target fingerprint image to be cached is determined based on the first fingerprint image. Cache operations are 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. In this scheme, by performing serial processing on the first fingerprint image of the strong template type and parallel processing on the first fingerprint image of the weak template type, a combined serial and parallel processing strategy is adopted. 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 thus enhancing the user registration experience. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of an ultrasonic fingerprint system; Figure 2 This is a flowchart of a fingerprint processing method according to an embodiment of the present invention; Figure 3 yes Figure 2 A flowchart of step S2; Figure 4 yes Figure 2 A flowchart of step S3. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The fingerprint processing method provided in this invention 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 the fingerprint image collected by the fingerprint sensor. It can realize the fingerprint registration operation quickly based on the fingerprint image, improve the registration efficiency while taking into account the image quality of the registered fingerprint template, and enhance the user registration experience.

[0026] As an example, the fingerprint sensor can be a press-type fingerprint sensor, which can acquire a fingerprint image based on the user's pressing action; or, the fingerprint sensor can be a swipe-type fingerprint sensor, which can acquire a fingerprint image based on the user's swiping or pushing action; or, the fingerprint sensor can be an ultrasonic fingerprint sensor in an ultrasonic fingerprint system, which can acquire a corresponding fingerprint image by triggering an ultrasonic signal.

[0027] As an example, the fingerprint processing system can be an ultrasonic fingerprint system, such as... Figure 1 As shown, the fingerprint sensor in an ultrasonic fingerprint system is an ultrasonic fingerprint sensor. An ultrasonic fingerprint system is a biometric technology based on ultrasonic technology. It acquires corresponding fingerprint images by emitting and receiving ultrasonic signals. Combining the differences in acoustic impedance between the screen, the finger, and the air, it distinguishes the valleys and ridges on the fingerprint, thereby obtaining fingerprint features for identity verification.

[0028] The fingerprint processing device of an ultrasonic fingerprint system may 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 to control the operation of each module. Its operation 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; the data processor rearranges and packages the data, and then sends the converted data to the algorithm processor for algorithm processing to complete fingerprint registration and recognition. In this example, the fingerprint processing device may include the controller, or it may include the controller and the connected data processor, ADC converter, and algorithm processor, depending on the specific circumstances.

[0029] In the fingerprint registration process of an ultrasonic fingerprint system, the fingerprint ridges of the finger are first collected as a fingerprint template for recognition. The fingerprint template has a significant impact on recognition. During fingerprint template registration, the ultrasonic frame rate is generally adjusted to acquire fingerprint images without motion blur. Generally, a higher frame rate increases the likelihood of acquiring fingerprint images without motion blur, thereby improving registration accuracy and user experience. However, this method of increasing the ultrasonic frame rate has the following drawbacks: First, increasing the frame rate increases the processing load on the device, leading to decreased device performance and increased power consumption; second, the frame rate is limited by factors such as the overall system processing power, ambient light, and finger condition, meaning that increasing the frame rate cannot guarantee the acquisition of clear fingerprint images every time, i.e., the image quality of the acquired fingerprint images cannot be guaranteed. Therefore, to improve the user experience and efficiency during application, the system allows the finger to slide across the fingerprint ridge acquisition area during operation. Through the processing of various modules in the system, the fingerprint images of the moving finger are acquired and analyzed. By using a step-by-step data processing strategy, the acquisition efficiency of the template is improved without affecting the recognition efficiency, effectively reducing the registration process time.

[0030] This invention provides a fingerprint processing method, which will be described using an example of its application in a fingerprint processing device. Figure 2 As shown, the fingerprint processing method includes: S1: Acquire the first fingerprint image, which is the fingerprint image acquired at the current moment; S2: Perform image analysis on the first fingerprint image to determine the image type corresponding to the first fingerprint image; S3: If the image type corresponding to the first fingerprint image is a weak template type, then based on the first fingerprint image, determine the target fingerprint image and cache the target fingerprint image; S4: If the image type corresponding to the first fingerprint image is a strong template type, then register the first fingerprint image as a strong fingerprint template and update the current state data corresponding to the strong fingerprint template. S5: When the current state data corresponding to the strong fingerprint template meets the registration end condition, the target fingerprint image is registered as a weak fingerprint template, and fingerprint registration is completed based on the strong fingerprint template and the weak fingerprint template.

[0031] As an example, in step S1, the fingerprint processing device is connected to the fingerprint sensor and can acquire the first fingerprint image collected by the fingerprint sensor in real time. In this example, the fingerprint processing device can acquire the first fingerprint image formed by a finger pressing through a specific area collected by a press-type fingerprint sensor, or the fingerprint processing device can acquire the first fingerprint image formed by a finger sliding or dragging through a specific area collected by a slide-type fingerprint sensor, or the fingerprint processing device can acquire the first fingerprint image formed by a finger sliding through a specific area collected by an ultrasonic fingerprint sensor.

[0032] As an example, in step S2, after acquiring the first fingerprint image, the fingerprint processing device can call its 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. In this example, the image type can be any one of a strong template type, a weak template type, and an invalid type. The strong template type and the weak template type are two valid types with different degrees of validity. Here, the strong template type 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. Among them, a strong fingerprint template refers to a valid fingerprint image that has passed all screening. A weak fingerprint template refers to a valid fingerprint image that has passed some screening and is used to assist the strong fingerprint template in fingerprint recognition. A valid fingerprint image refers to a fingerprint image that can be stored in the fingerprint template library for fingerprint recognition.

[0033] The target fingerprint image is the fingerprint image that needs to be cached.

[0034] As an example, in step S3, when the image type corresponding to the first fingerprint image is a weak template type, the fingerprint processing device can determine that part of it passes the screening, that is, it is determined that it meets the partial screening conditions for being registered as a fingerprint template. At this time, the first fingerprint image can be directly determined as the target fingerprint image and cached. Alternatively, the first fingerprint image acquired at the current moment and the target fingerprint images cached at previous moments can be analyzed and processed to update the target fingerprint images that can be cached as weak fingerprint templates. The specific method can be determined independently according to the actual situation. In this example, when the image type of the first fingerprint image is a weak template type, updating the target fingerprint image based on the first fingerprint image and caching the target fingerprint image without performing registration processing helps to improve its processing efficiency and thus improve the user registration experience.

[0035] The current status data corresponding to the strong fingerprint template is data related to the strong fingerprint template, specifically data used to assess whether the registration process needs to be terminated. The registration termination condition is a pre-set condition used to end the current fingerprint registration operation.

[0036] As an example, in step S4, when the image type corresponding to the first fingerprint image is a strong template type, the fingerprint processing device can determine that it has completely passed the screening, that is, it is determined that it meets all the screening conditions that can be registered as a fingerprint template. Therefore, the first fingerprint image is registered as a strong fingerprint template, and the current status data corresponding to the strong fingerprint template is updated so that the current status data corresponding to the strong fingerprint template can be compared with the pre-set registration end conditions. Based on the comparison result, the subsequent steps are executed.

[0037] As an example, in step S5, when the current state data corresponding to a strong fingerprint template meets the registration termination condition, the fingerprint processing device can register the cached target fingerprint image as a weak fingerprint template. Then, based on all strong and weak fingerprint templates, the fingerprint registration operation is completed. That is, all strong and weak fingerprint templates are determined as registered fingerprint templates, and the registered fingerprint templates are stored in the fingerprint template library so that fingerprint recognition can be performed on the real-time acquired fingerprint images based on the registered fingerprint templates. In this example, weak fingerprint templates can assist strong fingerprint templates in completing the fingerprint recognition operation, ensuring the success rate of subsequent fingerprint recognition.

[0038] 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.

[0039] 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.

[0040] 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: 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. 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; 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; 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.

[0041] The second fingerprint image is the fingerprint image acquired at the previous moment. This second fingerprint image is cached in memory and can be directly accessed when motion blur recognition is needed. Motion blur refers to phenomena such as fingerprint morphology deformation, distortion, and ghosting during movement, which can reflect situations where the acquired fingerprint morphology does not match the actual fingerprint morphology.

[0042] As an example, in step S21, after acquiring the first fingerprint image, the fingerprint processing device can invoke pre-set motion blur recognition logic to identify the first and second fingerprint images, determining the motion blur type corresponding to the first fingerprint image. This motion blur type reflects the changes in fingerprint patterns between the two captured fingerprint images during finger sliding, dragging, or other movements. In this example, the motion blur type can be any one of a fully blurred type, a semi-blurred type, or a non-blurred type. Here, the fully blurred type refers to a completely blurred state, i.e., a state where the fingerprint patterns are deformed and the degree of deformation is large; the non-blurred type refers to a state where no blurring occurs, i.e., a state where the fingerprint patterns are normal; the semi-blurred type is a state between the fully blurred type and the non-blurred type, i.e., a state where the fingerprint patterns are deformed but the degree of deformation is small.

[0043] As an example, in step S22, after acquiring 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 image quality corresponding to the first fingerprint image. Then, these two evaluation indicators, effective area and image quality, are compared with a pre-set fingerprint validity standard. If both evaluation indicators simultaneously meet the fingerprint validity standard, the first detection result corresponding to the first fingerprint image is determined to be a successful detection; otherwise, if at least one of the two evaluation indicators does not meet the fingerprint validity standard, the first detection result corresponding to the first fingerprint image is determined to be a failed detection. Here, the fingerprint validity standard can be understood as a standard used to evaluate whether the acquired fingerprint image is valid and meets the requirements for subsequent fingerprint recognition. In this example, the first detection result corresponding to the first fingerprint image reflects whether the first fingerprint image itself can meet the fingerprint recognition requirements.

[0044] In this example, the third fingerprint image is a fingerprint image whose image type was determined before the current time to be either a strong template type or a weak template type; that is, a valid fingerprint image determined before the current time. A valid fingerprint image refers to a fingerprint image that can be stored in the fingerprint template library for fingerprint recognition; that is, a valid fingerprint image is a fingerprint image of the strong template type or a weak template type. In this example, the third fingerprint image can be one or multiple; it can be a strong fingerprint template determined before the current time or a target fingerprint image determined before the current time.

[0045] As an example, in step S23, after acquiring the first fingerprint image, the fingerprint processing device can read the third fingerprint image from memory, and then perform a repetition rate detection based on the first and third fingerprint images to determine their overlapping area; then, based on the overlapping area and the area of ​​the first fingerprint image, determine the ratio between the two, and define the ratio as the measured repetition rate; compare the measured repetition rate with a preset repetition rate threshold; if the measured repetition rate is less than the repetition rate threshold, it indicates that the overlapping part between the first and third fingerprint images is small, 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 to be a successful detection; conversely, if the measured repetition rate is not less than the repetition rate threshold, it indicates that the overlapping part between the first and third fingerprint images is large, and not retaining the first fingerprint image 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 to be a failed detection.

[0046] As an example, in step S24, after determining the motion blur type, the first detection result, and the second detection result corresponding to the first fingerprint image, the fingerprint processing device can determine the image type corresponding to the first fingerprint image based on the specific combination formed by the motion blur type, the first detection result, and the second detection result, and the image type corresponding to that specific combination. In this example, the motion blur type can be any one of a fully blurred type, a half-blurred type, or an unblurred type; the first detection result can be any one of a pass detection or a fail detection; the second detection result can be any one of a pass detection or a fail detection; these three can form any one of 12 combinations. By querying a pre-set combination type mapping table, the corresponding image type can be determined. The combination type mapping table is a data table used to reflect the mapping relationship between different combinations and their corresponding image types.

[0047] In this example, motion blur recognition is performed based on a first fingerprint image and a second fingerprint image. The differences between the two frames are analyzed to determine the changes in fingerprint ridges and thus the type of motion blur reflecting these changes. The first fingerprint image undergoes effective area and image quality detection to assess its suitability as a fingerprint template, thereby determining the first detection result. The first and third fingerprint images are then subjected to repetition rate detection to analyze the overlap of their fingerprint features, further determining the second detection result. By combining the motion blur type of the first fingerprint image, the first detection result, and the second detection result, the corresponding image type is determined. This multi-dimensional approach allows for a comprehensive assessment of the image type, ensuring its suitability as a fingerprint template and guaranteeing a high success rate in fingerprint recognition.

[0048] In one embodiment, step S21, namely, performing motion blur recognition based on the first fingerprint image and the second fingerprint image to determine the motion blur type corresponding to the first fingerprint image, includes: S211: Perform 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; S212: Determine the motion blur type corresponding to the first fingerprint image based on the target motion blur value.

[0049] Among them, the target motion blur value is the specific numerical value used to ultimately evaluate the type of motion blur.

[0050] As an example, in step S211, after acquiring 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 the initial motion blur value. The initial motion blur value can be directly determined as the target motion blur value. Alternatively, depending on the specific situation, a target correction coefficient can be determined, and the initial motion blur value can be corrected using the target correction coefficient to determine the target motion blur value corresponding to the first fingerprint image.

[0051] For example, the motion blur algorithm can be S = std(diff(phase1-phase2)) / signal, where phase1 and phase2 are the first and second fingerprint images, respectively, which can be understood as two frames of fingerprint images with the same configuration acquired at the beginning and end of image acquisition; std is a function used to calculate the standard deviation of the data in the spatial domain; diff is a function used to calculate the difference between the two; signal is the signal quantity of the fingerprint image, used to reflect the fingerprint pressure and signal magnitude; S is the initial motion blur value, which can reflect the degree of motion blur between the two frames of fingerprint images.

[0052] In this example, step S211, which involves performing 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 includes: S2111: Perform motion blur recognition on the first fingerprint image and the second fingerprint image to determine the initial motion blur value corresponding to the first fingerprint image.

[0053] S2112: Perform quality analysis on the first fingerprint image to determine the corresponding quality score. This quality score is a score determined by quality analysis of the first fingerprint image and is used to reflect the fingerprint's contrast and clarity, etc.

[0054] S2113: The initial motion blur value is corrected using a quality score to determine the target motion blur value corresponding to the first fingerprint image.

[0055] Since fingerprint images with high quality scores have better resistance to motion blur, the initial motion blur value can be corrected using the quality score corresponding to the first fingerprint image. This ensures that the corrected target motion blur value can more easily pass the motion blur control, thereby helping to improve fingerprint registration efficiency.

[0056] Furthermore, the system pre-sets two quality thresholds, namely a first quality threshold and a second quality threshold, where the first quality threshold is greater than the second quality threshold. For example, the first quality threshold can be set to 50%, while the second quality threshold can be set to 35%. In step S2113 above, the initial motion blur value is corrected using a quality score to determine the target motion blur value corresponding to the first fingerprint image, specifically including: 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 the 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 means that when the quality score of the first fingerprint image is large, the initial motion blur value is reduced by a factor of 5 to determine the target motion blur value corresponding to the first fingerprint image, so that the first fingerprint image with better image quality is more likely to pass the motion blur control, thereby improving the fingerprint registration efficiency.

[0057] S21132: If the quality score is not greater than the first quality threshold and the quality score is greater than the second quality threshold, then the product of the initial motion blur value corresponding to the first fingerprint image and the 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 indicate 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 is easier to pass the motion blur control, thereby improving the fingerprint registration efficiency.

[0058] 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 thus its image type is determined to be invalid, so that it can be directly deleted in the future, thereby saving computational processing resources.

[0059] In this example, the initial motion blur value is corrected based on the quality score corresponding to the first fingerprint image. The corrected target motion blur value combines the image quality score and the anti-motion blur capability, making it easier for the first fingerprint image with higher image quality to pass the motion blur control. Conversely, the first fingerprint image with lower image quality is more difficult to pass the motion blur control, thus ensuring fingerprint registration efficiency.

[0060] As an example, in step S212, after determining the target motion blur value corresponding to the first fingerprint image, the fingerprint processing device can compare the target motion blur value with different preset blur thresholds to determine the blur threshold range to which it belongs, and determine the motion blur type corresponding to the blur threshold range as the motion blur type corresponding to the first fingerprint image, that is, determine that it belongs to any one of the full blur type, half blur type and non-blur type.

[0061] In this example, the system pre-sets two blur thresholds to define different types of motion blur, designated as the first blur threshold and the second blur threshold. The first blur threshold is used to determine whether the fingerprint pattern is normal; specifically, it can be understood as a blur threshold used to distinguish between non-blurred and semi-blurred types. The second blur threshold is used to determine the degree of fingerprint pattern deformation; specifically, it can be understood as a blur threshold used to distinguish between semi-blurred and fully blurred types. The first blur threshold is smaller than the second blur threshold. For example, the first blur threshold can be set to 20, and the second blur threshold can be set to 50.

[0062] Step S212, namely, determining the motion blur type corresponding to the first fingerprint image based on the target motion blur value, specifically includes: S2121: If the target motion blur value is less than the first blur threshold, then the motion blur type corresponding to the first fingerprint image is determined to be non-blurred. That is, when the target motion blur value corresponding to the first fingerprint image is small, the fingerprint pattern is considered normal, and at this time, the motion blur type is determined to be non-blurred.

[0063] S2122: If the target motion blur value is not less than the first blur threshold and the target motion blur value is less than the second blur threshold, then the motion blur type corresponding to the first fingerprint image is determined to be a semi-blurred type. That is, when the target motion blur value corresponding to the first fingerprint image is moderate, it is determined that the fingerprint pattern is deformed, but the degree of deformation is small and does not meet the standard for determining fingerprint pattern abnormality. At this time, its motion blur type is determined to be a semi-blurred type.

[0064] S2123: If the target motion blur value is not less than the second blur threshold, then the motion blur type corresponding to the first fingerprint image is determined to be the full blur type. That is, the target motion blur value corresponding to the first fingerprint image is large and reaches the standard for identifying fingerprint pattern abnormalities. At this time, its motion blur type is determined to be the full blur type.

[0065] In one embodiment, step S22, which involves performing effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image, includes: S221: Perform effective area and image quality detection on the first fingerprint image to determine the effective area and quality score corresponding to the first fingerprint image; S222: If the effective area is greater than the target area threshold and the quality score is greater than the target quality threshold, then the first detection result corresponding to the first fingerprint image is determined to be a successful detection. 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, then the first detection result corresponding to the first fingerprint image is determined to be a detection failure.

[0066] The effective area is the ratio of the area of ​​the valid pressed region in the fingerprint image to the total area of ​​the fingerprint image. Generally, during fingerprint image acquisition, the fingerprint sensor captures fingerprint features across its corresponding fingerprint recognition area. When the finger does not completely cover the fingerprint recognition area, the acquired fingerprint image includes the area corresponding to the valid pressed region and the background image outside the valid pressed region. The ratio of the area corresponding to the valid pressed region to the total area of ​​the fingerprint image is determined as the effective area. The target area threshold is used to evaluate whether the effective area meets the fingerprint validity standard. The target area threshold can be a pre-set fixed value or a dynamic value determined based on actual conditions.

[0067] The quality score is determined by quality analysis of the first fingerprint image and reflects aspects such as contrast and sharpness. The target quality threshold is used to evaluate whether the quality score meets the fingerprint validity standard. The target quality threshold can be a pre-set fixed value or a dynamic value determined based on actual conditions.

[0068] As an example, in step S221, after acquiring the first fingerprint image, the fingerprint processing device can calculate the effective area of ​​the first fingerprint image, determine the effective area corresponding to the first fingerprint image, compare the effective area with a pre-set or dynamically determined target area threshold, and perform image quality analysis on the first fingerprint image to determine the quality score corresponding to the first fingerprint image. The quality score is then compared with a pre-set or dynamically determined target quality threshold so as to determine the corresponding first detection result based on the two comparison results.

[0069] As an example, in step S222, when the effective area is greater than the target area threshold and the quality score is greater than the target quality threshold, the fingerprint processing device can determine that the effective area of ​​the first fingerprint image is large, that is, the area of ​​the finger in the effective pressing area is large, the probability of acquiring the fingerprint image corresponding to the invalid type is small, and the overall image quality of the acquired first fingerprint image is good. It is determined that the two fingerprint validity standards of effective area and image quality are met at the same time, and therefore, the corresponding first detection result is determined to be a successful detection.

[0070] As an example, in step 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, the fingerprint processing device can determine that the effective area of ​​the first fingerprint image is small, that is, the area of ​​the finger in the effective pressing area is small; or, if the overall image quality of the first fingerprint image is poor, the probability of acquiring a fingerprint image corresponding to an invalid type is high. Therefore, the corresponding first detection result is determined to be a detection failure.

[0071] In this example, based on the comparison results of the effective area and the target area threshold corresponding to the first fingerprint image, and the comparison results of the quality score and the target quality threshold corresponding to the first fingerprint image, it is determined from two evaluation dimensions whether it meets the preset fingerprint validity standard, and the corresponding first detection result is determined, which can ensure the accuracy of the first detection result.

[0072] In one embodiment, before step S22, i.e. before performing effective area and image quality detection on the first fingerprint image and determining the first detection result corresponding to the first fingerprint image, the fingerprint processing method further includes: S221': Perform 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; S222': Determine the target area threshold and target mass threshold based on the target motion ambiguity value; Among them, 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.

[0073] As an example, in step S221', after acquiring the first fingerprint image, the fingerprint processing device can use a pre-set motion blur algorithm to calculate the first and second fingerprint images and determine the output result as the initial motion blur value. This initial motion blur value can be directly determined as the target motion blur value. Alternatively, depending on the specific situation, a target correction coefficient can be determined, and the initial motion blur value can be corrected using the target correction coefficient to determine the target motion blur value corresponding to the first fingerprint image. The processing procedure is the same as in step S211, and will not be described in detail here to avoid repetition.

[0074] As an example, in step S222', after acquiring the target motion blur value corresponding to the first fingerprint image, the fingerprint processing device can dynamically determine the target area threshold corresponding to the target motion blur value based on a mapping table or mapping function between the motion blur value and the area threshold. This allows for subsequent evaluation of whether the effective area of ​​the first fingerprint image meets the fingerprint validity 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). In this case, a smaller target area threshold needs to be set so that the clearer first fingerprint image can more easily pass the effective area control, thereby improving fingerprint registration efficiency and user registration experience. Conversely, the larger the target motion blur value, the more blurred the first fingerprint image (i.e., closer to the fully blurred type). In this case, a larger target area threshold needs to be set so that the more blurred first fingerprint image is more difficult to pass the effective area control, thereby ensuring the validity of the final registered fingerprint template.

[0075] Accordingly, after acquiring the target motion blur value corresponding to the first fingerprint image, the fingerprint processing device can dynamically determine the target quality threshold corresponding to the target motion blur value based on a mapping table or mapping function between the motion blur value and the quality threshold. This allows for subsequent evaluation of whether the quality score of the first fingerprint image meets the fingerprint validity 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). In this case, a smaller target quality threshold needs to be set so that the clearer first fingerprint image is more likely to pass the image quality control, thereby improving fingerprint registration efficiency and user registration experience. Conversely, the larger the target motion blur value, the more blurred the first fingerprint image (i.e., closer to the fully blurred type). In this case, a larger target quality threshold needs to be set so that the more blurred first fingerprint image is more difficult to pass the image quality control, thereby ensuring the validity of the final registered fingerprint template.

[0076] In one embodiment, the third fingerprint image includes a fourth fingerprint image, which is a fingerprint image whose image type was previously determined to be a strong template type at the current time. Step S23, namely, performing a 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: S231: Based on the first fingerprint image and all fourth fingerprint images, perform repetition rate detection to determine the first measured repetition rate; S232: Based on the first fingerprint image and the third fingerprint image closest to the current moment, perform repetition rate detection to determine the second measured repetition rate; 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, then the second detection result corresponding to the first fingerprint image is determined to be a successful detection. 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, then the second detection result corresponding to the first fingerprint image is determined to be a detection failure.

[0077] As an example, in step S231, after acquiring the first fingerprint image, the fingerprint processing device needs to read all fourth fingerprint images from memory. The fourth fingerprint images are fingerprint images whose image type is determined to be a strong template type at the current time, and there is at least one strong fingerprint template. Then, the first fingerprint image and the fourth fingerprint images are subjected to a 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 fourth fingerprint images to determine the first measured repetition rate. Here, the single repetition rate can be calculated by first calculating the overlapping area of ​​the first fingerprint image and a fourth fingerprint image, and then calculating the ratio of this overlapping area to the area of ​​the first fingerprint image, and determining this ratio as the single repetition rate corresponding to the fourth fingerprint image.

[0078] As an example, the first measured repetition rate is the maximum value among the single repetition rates corresponding to all fourth fingerprint images, and the single repetition rate is the repetition rate between the fourth fingerprint image and the first fingerprint image.

[0079] In this example, the fingerprint processing device can calculate the repetition rate between at least one fourth fingerprint image and the first fingerprint image to determine the single repetition rate corresponding to each fourth fingerprint image. For example, it first calculates the overlap area between each fourth fingerprint image and the first fingerprint image, then calculates the ratio of the overlap area to the area of ​​the first fingerprint image, and determines this ratio as the single repetition rate corresponding to the fourth fingerprint image. Then, the fingerprint processing device can compare the single repetition rates corresponding to at least one fourth fingerprint image and determine the maximum value as the first measured repetition rate. The larger the first measured repetition rate, the larger the overlap area between the first fingerprint image and one of the strong fingerprint templates, and the less likely it is to be retained as a registration fingerprint template, so as to avoid the registration fingerprint database containing multiple fingerprint images with a high degree of fingerprint feature repetition, which would affect the recognition success rate of subsequent fingerprint recognition; conversely, the smaller the first measured repetition rate, the more fingerprint features it contains that are different from the fourth fingerprint image with the largest overlap area, and the more likely it is to be retained as a registration fingerprint template to ensure the recognition success rate of subsequent fingerprint recognition.

[0080] As an example, the first measured repetition rate is the repetition rate between the stitched fingerprint image and the first fingerprint image, and the stitched fingerprint image is the fingerprint image obtained by stitching together all the fourth fingerprint images.

[0081] In this example, after reading the fourth fingerprint image, the fingerprint processing device can stitch all the fourth fingerprint images together. For example, it can perform a union operation on the fingerprint features corresponding to all the fourth fingerprint images to obtain a stitched fingerprint image. Then, the fingerprint processing device can calculate the repetition rate between the stitched fingerprint image and the first fingerprint image to determine the corresponding first measured repetition rate. For example, it can calculate the overlap area between the stitched fingerprint image and the first fingerprint image, and then calculate the ratio of the overlap area to the area of ​​the first fingerprint image. This ratio is determined as the first measured repetition rate. The larger the first measured repetition rate, the larger the overlap area between the first fingerprint image and the stitched fingerprint image, and the less likely it is to be retained as a registration fingerprint template. Conversely, the smaller the first measured repetition rate, the more fingerprint features it contains that are different from the stitched fingerprint image, and the more likely it is to be retained as a registration fingerprint template to ensure the success rate of subsequent fingerprint recognition.

[0082] As an example, in step S232, after acquiring the first fingerprint image, the fingerprint processing device can determine the third fingerprint image closest to the current moment from all third fingerprint images (this can be a fingerprint image of a strong template type or a fingerprint image of a weak template type); then, it performs a repetition rate detection between the first fingerprint image and the third fingerprint image closest to the current moment to determine the second measured repetition rate. In this example, the fingerprint processing device can first determine the overlap area between the first fingerprint image and the third fingerprint image closest to the current moment, then calculate the ratio of the overlap area to the total area of ​​the first fingerprint image, and determine this ratio as the second measured repetition rate.

[0083] The first repetition rate threshold and the second repetition rate threshold are two pre-set repetition rate thresholds. These two repetition rate thresholds can be the same or different. For example, both can be specific values ​​between 70% and 90%.

[0084] 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 few fingerprint features repeated between the first fingerprint image and all fourth fingerprint images, and there are few fingerprint features repeated between the first fingerprint image and the third fingerprint image closest to the current time. This indicates that the first fingerprint image contains many fingerprint features that are not present 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 successful detection, allowing it to pass the repetition rate control.

[0085] 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 many repeated fingerprint features between the first fingerprint image and the fourth fingerprint image, or that there are many repeated fingerprint features between the first fingerprint image and the third fingerprint image closest to the current time. This indicates that the first fingerprint image contains fewer fingerprint features that are not present in the third fingerprint image, and should not be retained as a registered fingerprint template. This is to avoid the limited number of fingerprint features that can be identified by a number of valid fingerprint templates, which would affect 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, making it unable to pass the repetition rate control.

[0086] In one embodiment, before step S23, i.e., based on the first fingerprint image and the third fingerprint image, a repetition rate detection is performed to determine the second detection result corresponding to the first fingerprint image, the fingerprint processing method further includes: S231': Obtain the first cumulative number, where the first cumulative number is the number of consecutively acquired first fingerprint images of weak template type; S232': Based on the first cumulative number, determine the first repetition rate threshold and the second repetition rate threshold; Among them, the first repetition rate threshold is negatively correlated with the first cumulative number; the second repetition rate threshold is negatively correlated with the first cumulative number.

[0087] As an example, in step S231', after the fingerprint processing device acquires a first fingerprint image, performs image analysis on the first fingerprint image, and determines the image type corresponding to the first fingerprint image, it needs to update the first cumulative quantity based on the image type corresponding to the first fingerprint image. The first cumulative quantity 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, then N=N+1 is used to update the first cumulative quantity N; if the image type corresponding to the first fingerprint image is not the weak template type, that is, its image type is the weak template type or an invalid type, then N=0 is used to update the first cumulative quantity N, in order to count the number of consecutively acquired fingerprint images corresponding to the weak template type.

[0088] As an example, in step S232', after obtaining 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 the mapping relationship table or 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. That is, the larger the first cumulative number, the more fingerprint images of weak template types are continuously collected. At this time, it is necessary to increase the first repetition rate threshold and the second repetition rate threshold to improve the repetition rate control requirements and avoid the number of fingerprint images of weak template types collected reaching its first number threshold (which is a pre-set number of weak fingerprint templates to be collected, for example, it can be set to 20), which would reduce the processing efficiency of the subsequent determination of the cached target fingerprint images. Conversely, the smaller the first cumulative number, the fewer fingerprint images of weak template types are continuously collected. At this time, it is necessary to decrease the first repetition rate threshold and the second repetition rate threshold to reduce the repetition rate control requirements.

[0089] In one embodiment, step S24, namely 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, includes: S241: If the motion blur type is non-blurred, the first detection result is a successful detection, and the second detection result is a successful detection, then the image type corresponding to the first fingerprint image is determined to be a strong template type. S242: If the motion blur type is non-blurred, the first detection result is a successful detection, and the second detection result is a failed detection, then the image type corresponding to the first fingerprint image is determined to be a weak template type; or, if the motion blur type is semi-blurred, the first detection result is a successful detection, and the second detection result is a successful detection, then the image type corresponding to the first fingerprint image is determined to be a weak template type.

[0090] As an example, in step S241, when the motion blur type corresponding to the first fingerprint image is non-blurred, the first detection result corresponding to the first fingerprint image is a pass, and the second detection result corresponding to the first fingerprint image is a pass, it can be determined that the fingerprint pattern of the first fingerprint image is normal, the validity determined based on the effective area and image quality is high, and the repetition rate with the third fingerprint image is low, meeting the preset conditions for determining it as a strong fingerprint template. Therefore, the image type of the first fingerprint image can be determined as a strong template type. In other words, when the clarity and validity of the first fingerprint image are high, and the repetition rate with other registered fingerprint templates is low, it can be registered as a strong fingerprint template.

[0091] As an example, in step S242, when the motion blur type corresponding to the first fingerprint image is non-blurred, the first detection result corresponding to the first fingerprint image is a pass, and the second detection result corresponding to the first fingerprint image is a fail, it can be determined that the fingerprint pattern of the first fingerprint image is normal, and its effectiveness is high based on the effective area and image quality. However, it has a high repetition rate with the third fingerprint image, which meets the preset conditions for determining it as a weak fingerprint template. Therefore, the image type of the first fingerprint image can be determined as a weak template type. In other words, when the clarity and effectiveness of the first fingerprint image are high, and its repetition rate with other registered fingerprint templates is high, in order to avoid missing fingerprint features in some effective pressing areas, it can be cached as a target fingerprint image so that it can be registered as a weak fingerprint template later.

[0092] Alternatively, if the fingerprint processing device determines that the motion blur type of the first fingerprint image is semi-blurred, the first detection result corresponding to the first fingerprint image is a pass, and the second detection result corresponding to the first fingerprint image is also a pass, then the fingerprint pattern of the first fingerprint image is determined to be deformed but the degree of deformation is small. Based on the effective area and image quality, the determination of effectiveness is high, and the repetition rate with the third fingerprint image is low, satisfying the preset conditions for determining it as a weak fingerprint template. Therefore, the image type of the first fingerprint image can be determined as a weak template type. In other words, when the clarity of the first fingerprint image is moderate, its effectiveness is high, and the repetition rate with other registered fingerprint templates is low, it can be registered as a strong fingerprint template. To avoid missing fingerprint features in some effective pressing areas, it can be used as a target fingerprint image cache for subsequent registration as a weak fingerprint template.

[0093] Furthermore, when the motion blur type, the first detection result, and the second detection result corresponding to the first fingerprint image do not meet the preset conditions for identifying it as a strong template type or a weak template type, the fingerprint processing device determines that the corresponding image type is invalid, so that the first fingerprint image is deleted and no longer processed, which helps to save processing resources, and then repeats the acquisition of the first fingerprint image and subsequent operations.

[0094] In one embodiment, such as Figure 4 As shown, step S3, which is to determine the target fingerprint image based on the first fingerprint image, includes: S31: Obtain the number of images corresponding to the fifth fingerprint image. The fifth fingerprint image is a fingerprint image whose image type was previously determined to be a weak template type at the current moment. S32: If the number of images corresponding to the fifth fingerprint image is less than the first number threshold, then the first fingerprint image and all fifth fingerprint images are determined as the target fingerprint images; S33: If the number of images corresponding to the fifth fingerprint image is not less than the first number threshold, then analyze the first fingerprint image and all fifth fingerprint images to determine the target fingerprint image.

[0095] The first quantity threshold is the number of weak fingerprint templates that need to be collected, which can also be understood as the maximum number of target fingerprint images that can be cached. For example, the first quantity threshold can be set to 20.

[0096] As an example, in step S31, when the fingerprint processing device determines that the image type corresponding to the first fingerprint image is a weak template type, it needs to first determine the number of images corresponding to the fifth fingerprint image pre-cached in memory, compare the number of images with a pre-set first quantity threshold, and determine the target fingerprint image to be cached this time based on the comparison result.

[0097] As an example, in step S32, when the number of images corresponding to the fifth fingerprint image is less than a first quantity threshold, the fingerprint processing device can determine that there are few fifth fingerprint images cached in memory at the current moment, and can directly determine the first fingerprint image as the target fingerprint image to be cached. That is to say, in this case, both the first fingerprint image and all fifth fingerprint images can be determined as target fingerprint images to be cached.

[0098] As an example, in step S33, when the number of images corresponding to the fifth fingerprint image is not less than a first quantity threshold, the fingerprint processing device can determine that there are too many fifth fingerprint images cached in memory at the current moment. Therefore, it is necessary to analyze and process the first fingerprint image and all fifth fingerprint images to delete one of them and retain the remaining fingerprint images as the target fingerprint images to be cached. In other words, in this case, based on the analysis results, it is possible to delete the first fingerprint image or one of the fifth fingerprint images, and determine the remaining fingerprint images as the target fingerprint images.

[0099] For example, when the number of images corresponding to the fifth fingerprint image is not less than the first number threshold, the fingerprint processing device can obtain the cache time of all fifth fingerprint images, delete the fifth fingerprint image with the longest cache time, and determine the remaining fifth fingerprint images and the first fingerprint image as target fingerprint images that need to be cached, so that when the fingerprint registration ends, all target fingerprint images can be registered as weak fingerprint templates.

[0100] In one embodiment, step S33, which involves analyzing the first fingerprint image and all fifth fingerprint images to determine the target fingerprint image, includes: S331: Determine the priority corresponding to the first fingerprint image; S332: If the priority of the first fingerprint image is less than the minimum priority of all fifth fingerprint images, then delete the first fingerprint image and determine all fifth fingerprint images as the target fingerprint images. S333: If the priority of the first fingerprint image is not less than the minimum priority of all fifth fingerprint images, then delete the fifth fingerprint image corresponding to the minimum priority, and determine the first fingerprint image and the remaining fifth fingerprint images as the target fingerprint images.

[0101] As an example, in step S331, the fingerprint processing device may use pre-set priority determination logic to analyze and process the first fingerprint image to determine its priority. This priority can be determined based on at least one of the image attributes such as fingerprint image clarity, effective area, image quality, and repetition rate. Then, the priority of the first fingerprint image is compared with the lowest priority corresponding to all fifth fingerprint images in the cache to determine the fingerprint image to be deleted based on the comparison result. In this example, the fingerprint processing device analyzes and stores the corresponding priority each time it obtains a first fingerprint image for subsequent priority comparisons.

[0102] As an example, in step S332, when the priority of the first fingerprint image is less than the minimum priority of all fifth fingerprint images, the fingerprint processing device determines that the priority of the first fingerprint image is less than the priority of all fifth fingerprint images. Therefore, the first fingerprint image can be deleted, and all fifth fingerprint images can be determined as target fingerprint images to ensure the fingerprint images with higher priority.

[0103] 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 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 protect the fingerprint images with higher priority and ensure that the target fingerprint image with the cache time is closest to the current time.

[0104] In one embodiment, step S331, namely determining the priority corresponding to the first fingerprint image, includes: S3311: Motion blur recognition is performed based on the first fingerprint image and the second fingerprint image to determine the target motion blur value. Based on the target motion blur value, the first index score corresponding to the first fingerprint image is determined. The second fingerprint image is the fingerprint image collected at the previous moment. S3312: Perform effective area and image quality detection on the first fingerprint image, determine the effective area and quality score, and determine the second index score corresponding to the first fingerprint image based on the effective area and quality score; S3313: Perform repetition rate detection based on the first fingerprint image and the third fingerprint image to determine the measured repetition rate. Based on the measured repetition rate, determine the third index score corresponding to the first fingerprint image. The third fingerprint image is a fingerprint image whose image type was determined to be either strong template type or weak template type before the current time. S3314: Determine 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.

[0105] As an example, in step S3311, the fingerprint processing device can use a pre-set motion blur algorithm to analyze the first fingerprint image and the second fingerprint image to determine the target motion blur value corresponding to the first fingerprint image. The processing procedure is similar to step S21, and will not be described in detail here to avoid repetition. 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 it can perform normalization or other processing on the target motion blur value to determine the first index score used for priority evaluation.

[0106] As an example, in step S3312, the fingerprint processing device can calculate the effective area of ​​the first fingerprint image to 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 procedure is similar to step S22, and will not be described in detail here to avoid repetition. After determining the effective area and image quality corresponding to the first fingerprint image, the fingerprint processing device can normalize the effective area and image quality corresponding to the first fingerprint image to determine a second index score used for priority evaluation; or, based on the effective area and image quality corresponding to the first fingerprint image, it can query a pre-set mapping table reflecting the combination of the two and the index score to determine the second index score used for priority evaluation.

[0107] As an example, in step S3313, the fingerprint processing device can perform repetition rate detection on the first fingerprint image and the third fingerprint image to determine their overlapping area; then, based on the overlapping area and the area of ​​the first fingerprint image, it determines the ratio between the two, and the ratio is determined as the measured repetition rate. The processing procedure is similar to step S23, and to avoid repetition, it will not be described in detail here. After determining the measured repetition rate corresponding to the first fingerprint image, the fingerprint processing device can normalize the measured repetition rate to determine a third indicator score used for priority evaluation; or, based on the measured repetition rate, it can query a pre-set mapping table reflecting the repetition rate and indicator scores to determine the third indicator score used for priority evaluation.

[0108] As an example, in step S3314, after determining the first index score, second index score, and third index score corresponding to the first fingerprint image, the fingerprint processing device can directly add the first index score, second index score, and third index score corresponding to the first fingerprint image to determine the priority of the first fingerprint image; alternatively, it can perform weighted processing on the first index score, second index score, and third index score corresponding to the first fingerprint image to determine the priority of the first fingerprint image. During the weighted processing, the weight of each index score can be a fixed value or a dynamic value determined according to the actual situation.

[0109] In this example, motion blur recognition is performed based on the first and second fingerprint images. Based on the determined target motion blur value, a first index score reflecting fingerprint pattern changes is determined. The first fingerprint image undergoes effective area and image quality detection to determine a second index score reflecting whether the fingerprint image meets validity standards, based on the image's inherent quality. The first and third fingerprint images undergo repetition rate detection to determine a third index score reflecting fingerprint feature repetition. Based on the first, second, and third index scores corresponding to the first fingerprint image, their priorities are dynamically determined. This multi-dimensional approach ensures the validity of the subsequently retained target fingerprint image, improving its success rate as a weak fingerprint template for fingerprint recognition.

[0110] In one embodiment, step S3314, namely 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, includes: S33141: Determine the fingerprint acquisition method corresponding to the first fingerprint image; S33142: If the fingerprint acquisition method is a press method, then based on the first weight combination, the first indicator score, the second indicator score and the third indicator score corresponding to the first fingerprint image are weighted to determine the priority of the first fingerprint image; in the first weight combination, the weight of the first indicator score is less than the weight of the third indicator score. S33143: If the fingerprint acquisition method is a sliding method, then based on the second weight combination, the first index score, the second index score and the third index score corresponding to the first fingerprint image are weighted 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.

[0111] As an example, in step S33141, the fingerprint processing device can determine the fingerprint acquisition method corresponding to each first fingerprint image. Specifically, it can be determined based on its built-in configuration information or by analyzing multiple consecutive frames of first fingerprint images. That is, the fingerprint acquisition method can be determined autonomously according to the actual situation.

[0112] The first weight combination refers to the weight combination that weights the indicator scores under the pressing method. The first weight combination includes the weight of the first indicator score, the weight of the second indicator score, and the weight of the third indicator score. The sum of the weights of the three indicator scores is 1.

[0113] As an example, in step S33142, when the fingerprint processing device determines that the fingerprint acquisition method is a press-to-seal method, i.e., the user's finger presses onto the fingerprint sensor and the fingerprint sensor acquires the corresponding first fingerprint image, when evaluating the priority of the first fingerprint image, it needs to perform weighted processing on the first index score, second index score, and third index score corresponding to the first fingerprint image based on the first weight combination to determine the priority of the first fingerprint image. Since the weight of the first index score is less than the weight of the third index score in the first weight combination, the index score of the blurred motion factor in the first fingerprint image acquired by the press-to-seal method is reduced, and the index score of the repetitive factor in the first fingerprint image acquired by the press-to-seal method is increased to adapt to the specific scenario.

[0114] The second weight combination refers to the weight combination that weights the indicator scores under the sliding mode. The second weight combination includes the weight of the first indicator score, the weight of the second indicator score, and the weight of the third indicator score. The sum of the weights of the three indicator scores is 1.

[0115] As an example, in step S33143, when the fingerprint processing device determines that the fingerprint acquisition method is a sliding method, that is, the user's finger slides or drags across the fingerprint sensor and the fingerprint sensor acquires the corresponding first fingerprint image, when evaluating the priority of the first fingerprint image, it needs to perform weighted processing on the first index score, second index score and third index score corresponding to the first fingerprint image based on the second weight combination to determine the priority of the first fingerprint image. Since the weight of the first index score is greater than the weight of the third index score in the first weight combination, the index score of the blurred motion factor in the first fingerprint image acquired by the press method is increased, and the index score of the repetitive factor in the first fingerprint image acquired by the press method is decreased, so as to adapt to the specific scenario.

[0116] In one embodiment, the current status data includes a second cumulative number, which is the number of times a strong fingerprint template has been registered. The registration termination condition includes the second cumulative quantity being greater than the second quantity threshold.

[0117] The second cumulative number is the number of fingerprint templates that have been registered. The second threshold is a threshold used to assess whether to terminate the fingerprint registration process.

[0118] As an example, when the fingerprint processing device registers the first fingerprint image as a strong fingerprint template, it needs to update the second cumulative quantity M corresponding to the strong fingerprint template using M=M+1; then, it compares the second cumulative quantity with the second quantity threshold; if the second cumulative quantity is greater than the second quantity threshold, it is determined that the registration end condition has been met, and the target fingerprint image can be registered as a weak fingerprint template, and fingerprint registration is completed based on all strong fingerprint templates and all weak fingerprint templates; otherwise, if the second cumulative quantity is not greater than the second quantity threshold, it is determined that the registration end condition has not been met, and image acquisition needs to be repeated, that is, step S1 is repeated.

[0119] In one embodiment, the current state data includes the effective area ratio, which is the ratio of the overlapping area of ​​all strong fingerprint templates to the area of ​​the standard fingerprint template. Registration termination conditions include the effective area ratio being greater than the preset ratio threshold.

[0120] The effective area ratio is the ratio of the overlapping area of ​​all strong fingerprint templates after stitching together to the area of ​​the standard fingerprint template. Specifically, after the first fingerprint image is registered as a strong fingerprint template, all strong fingerprint templates are stitched together to determine their corresponding overlapping area. This overlapping area can be understood as the overlapping area of ​​the first fingerprint image and its previously determined stitched fingerprint images. The preset ratio threshold is a pre-set threshold used to evaluate whether to end the fingerprint registration process. The standard fingerprint template is a pre-set fingerprint template, which can be a fingerprint template whose total area is determined through pre-calibration experiments.

[0121] As an example, after registering the first fingerprint image as a strong fingerprint template, the fingerprint processing device needs to determine the overlapping area based on all strong fingerprint templates in the fingerprint template library by stitching together all strong fingerprint templates. Then, it calculates the effective area ratio by comparing the overlapping area with the area of ​​the standard fingerprint template. Next, it compares the effective area ratio with a preset ratio threshold. If the effective area ratio is greater than the preset ratio threshold, it is determined that the registration end condition has been met, and the target fingerprint image can be registered as a weak fingerprint template, completing the fingerprint registration based on all strong fingerprint templates and all weak fingerprint templates. Conversely, if the effective area ratio is not greater than the preset ratio threshold, it is determined that the registration end condition has not been met, and image acquisition needs to be repeated, i.e., step S1 needs to be repeated.

[0122] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0123] In one embodiment, a fingerprint processing apparatus is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the fingerprint processing method described in the above embodiment, for example... Figure 2 As shown in S1-S5, or Figures 3 to 4 As shown, to avoid repetition, it will not be repeated here.

[0124] In one embodiment, a fingerprint processing system is provided, including a fingerprint sensor and the fingerprint processing device described above. The fingerprint processing device is connected to the fingerprint sensor. To avoid repetition, it will not be described again here.

[0125] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A fingerprint processing method, characterized in that, include: Acquire a first fingerprint image, which is the fingerprint image collected at the current moment; Perform 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, then based on the first fingerprint image, a target fingerprint image is determined and the target fingerprint image is cached; If the image type corresponding to the first fingerprint image is a strong template type, then the first fingerprint image is registered as a strong fingerprint template, and the current status data corresponding to the strong fingerprint template is updated. The strong fingerprint template refers to a valid fingerprint image that has passed the screening completely. The valid fingerprint image refers to a fingerprint image that can be stored in the fingerprint template library for fingerprint recognition. When the current state data corresponding to the strong fingerprint template meets the registration end condition, the target fingerprint image is registered as a weak fingerprint template. Fingerprint registration is completed based on the strong fingerprint template and the weak fingerprint template. The weak fingerprint template refers to a portion of the valid fingerprint images that have passed the screening.

2. The fingerprint processing method according to claim 1, characterized in that, The step of performing image analysis on the first fingerprint image to determine the image type corresponding to the first fingerprint image includes: 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. Perform effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image; Based on the first fingerprint image and the third fingerprint image, a repetition rate detection is performed to determine the second detection result corresponding to the first fingerprint image. 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. Based on the motion blur type corresponding to the first fingerprint image, the first detection result, and the second detection result, the image type corresponding to the first fingerprint image is determined.

3. The fingerprint processing method according to claim 2, characterized in that, The step of performing motion blur recognition based on the first fingerprint image and the second fingerprint image to determine the motion blur type corresponding to the first fingerprint image includes: Motion blur recognition is performed on the first fingerprint image and the second fingerprint image to determine the target motion blur value corresponding to the first fingerprint image; Based on the target motion blur value, the motion blur type corresponding to the first fingerprint image is determined.

4. The fingerprint processing method according to claim 2, characterized in that, The step of performing effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image includes: The effective area and image quality of the first fingerprint image are detected to determine the effective area and quality score of the first fingerprint image. If the effective area is greater than the target area threshold and the quality score is greater than the target quality threshold, then the first detection result corresponding to the first fingerprint image is determined to be a successful detection. If the effective area is not greater than the target area threshold, or the quality score is not greater than the target quality threshold, then the first detection result corresponding to the first fingerprint image is determined to be a detection failure.

5. The fingerprint processing method according to claim 4, characterized in that, Before performing effective area and image quality detection on the first fingerprint image to determine the first detection result corresponding to the first fingerprint image, the fingerprint processing method further includes: Motion blur recognition is performed on the first fingerprint image and the second fingerprint image to determine the target motion blur value corresponding to the first fingerprint image; Based on the target motion blur value, the target area threshold and the target mass threshold are determined; 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.

6. The fingerprint processing method according to claim 2, characterized in that, The third fingerprint image includes a fourth fingerprint image, which is a fingerprint image whose image type was previously determined to be a strong template type at the current moment. The step of performing 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: Based on the first fingerprint image and all the fourth fingerprint images, a repetition rate detection is performed to determine the first measured repetition rate; Based on the first fingerprint image and the third fingerprint image closest to the current moment, a repetition rate is detected to determine the second measured repetition rate; 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, then the second detection result corresponding to the first fingerprint image is determined to be a successful detection. 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, then the second detection result corresponding to the first fingerprint image is determined to be a detection failure.

7. The fingerprint processing method according to claim 6, characterized in that, The first measured repetition rate is the maximum value among 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. Alternatively, the first measured repetition rate is the repetition rate between the stitched fingerprint image and the first fingerprint image, where the stitched fingerprint image is the fingerprint image obtained by stitching together all the fourth fingerprint images.

8. The fingerprint processing method according to claim 6, characterized in that, Before performing 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: Obtain a first cumulative number, where the first cumulative number is the number of consecutively acquired first fingerprint images of the weak template type. Based on the first cumulative number, determine the first repetition rate threshold and the second repetition rate threshold; Wherein, the first repetition rate threshold is negatively correlated with the first cumulative quantity; the second repetition rate threshold is negatively correlated with the first cumulative quantity.

9. The fingerprint processing method according to claim 2, characterized in that, The step of determining the image type corresponding to the first fingerprint image based on the motion blur type, the first detection result, and the second detection result includes: If the motion blur type is a non-blurred type, the first detection result is a pass, and the second detection result is a pass, then the image type corresponding to the first fingerprint image is determined to be a strong template type. If the motion blur type is a non-blurred type, the first detection result is a successful detection, and the second detection result is a failed detection, then the image type corresponding to the first fingerprint image is determined to be a weak template type. Alternatively, if the motion blur type is a semi-blurred type, the first detection result is a pass, and the second detection result is a pass, then the image type corresponding to the first fingerprint image is determined to be a weak template type.

10. The fingerprint processing method according to claim 1, characterized in that, The step of determining the target fingerprint image based on the first fingerprint image includes: Obtain the number of images corresponding to the fifth fingerprint image, wherein the fifth fingerprint image is a fingerprint image whose image type was previously determined to be a weak template type at the current moment; If the number of images corresponding to the fifth fingerprint image is less than the first number threshold, then the first fingerprint image and all the fifth fingerprint images are determined as the target fingerprint images; If the number of images corresponding to the fifth fingerprint image is not less than the first number threshold, then the first fingerprint image and all the fifth fingerprint images are analyzed to determine the target fingerprint image.

11. The fingerprint processing method according to claim 10, characterized in that, The step of analyzing the first fingerprint image and all the fifth fingerprint images to determine the target fingerprint image includes: Determine the priority corresponding to the first fingerprint image; If the priority of the first fingerprint image is less than the minimum priority of all the fifth fingerprint images, then the first fingerprint image is deleted and all the fifth fingerprint images are determined as the target fingerprint images. If the priority of the first fingerprint image is not less than the minimum priority of all the fifth fingerprint images, then delete the fifth fingerprint image corresponding to the minimum priority, and determine the first fingerprint image and the remaining fifth fingerprint images as the target fingerprint images.

12. The fingerprint processing method according to claim 11, characterized in that, Determining the priority corresponding to the first fingerprint image includes: Motion blur recognition is performed based on the first fingerprint image and the second fingerprint image to determine the target motion blur value. Based on the target motion blur value, the first index score corresponding to the first fingerprint image is determined. The second fingerprint image is the fingerprint image collected at the previous moment. The first fingerprint image is subjected to effective area and image quality detection to determine the effective area and quality score. Based on the effective area and the quality score, the second index score corresponding to the first fingerprint image is determined. Based on the first fingerprint image and the third fingerprint image, a repetition rate is detected to determine the measured repetition rate. Based on the measured repetition rate, a third index score corresponding to the first fingerprint image is determined. The third fingerprint image is a fingerprint image whose image type was determined to be either a strong template type or a weak template type before the current time. The priority of the first fingerprint image is determined based on the first index score, the second index score, and the third index score corresponding to the first fingerprint image.

13. The fingerprint processing method according to claim 12, characterized in that, The step of determining the priority of the first fingerprint image based on the first index score, second index score, and third index score corresponding to the first fingerprint image includes: Determine the fingerprint acquisition method corresponding to the first fingerprint image; If the fingerprint acquisition method is a press method, then based on the first weight combination, the first indicator score, the second indicator score and the third indicator score corresponding to the first fingerprint image are weighted to determine the priority of the first fingerprint image; in the first weight combination, the weight of the first indicator score is less than the weight of the third indicator score. If the fingerprint acquisition method is a sliding method, then based on the second weight combination, the first indicator score, the second indicator score and the third indicator score corresponding to the first fingerprint image are weighted to determine the priority of the first fingerprint image; in the second weight combination, the weight of the first indicator score is greater than the weight of the third indicator score.

14. The fingerprint processing method according to claim 1, characterized in that, The current status data includes a second cumulative number, which is the number of strong fingerprint templates that have been registered; the registration termination condition includes the second cumulative number being greater than a second number threshold. Alternatively, the current status data may include an effective area ratio, which is the ratio of the overlapping area of ​​all strong fingerprint templates to the area of ​​the standard fingerprint template; the registration termination condition may include the effective area ratio being greater than a preset ratio threshold.

15. 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, When the processor executes the computer program, it implements the fingerprint processing method according to any one of claims 1 to 14.

16. A fingerprint processing system, characterized in that, It includes a fingerprint sensor and the fingerprint processing device of claim 15, wherein the fingerprint processing device is connected to the fingerprint sensor.

Images