Fingerprint processing method and device and electronic equipment

Abstract

The embodiment of the invention provides a fingerprint processing method and device and electronic equipment, and the method comprises the steps: collecting fingerprint data according to a first frame rate through a fingerprint sensor in a fingerprint template registration process, and enabling each frame of fingerprint data to comprise a plurality of sub-data of different phases; comparing the difference between the two sub-data of different phases acquired based on the same configuration to obtain the motion ambiguity of the frame of fingerprint data; and performing fingerprint template registration according to the motion ambiguity of the frame of fingerprint data. By adopting the embodiment of the invention, the finger can be in continuous contact with the fingerprint acquisition area and move in the fingerprint template registration process, and is not limited to repeated pressing and hand raising modes, so that the user input flexibility in the fingerprint template registration can be improved, the fingerprint template registration efficiency can be improved, and the quality of the registered fingerprint template can be ensured.

Description

[0001] This disclosure is a divisional application of the invention patent entitled "Fingerprint Processing Method, Apparatus and Electronic Device", filed on September 30, 2024, with application number 2024113763245. Technical Field

[0002] This disclosure relates to the field of fingerprint recognition technology, specifically to a fingerprint processing method, apparatus, and electronic device. Background Technology

[0003] Ultrasonic fingerprint recognition systems acquire fingerprint images by emitting and receiving ultrasonic signals. They then combine this with the differences in acoustic impedance between the screen, the finger, and the air to distinguish the valleys and ridges on the fingerprint, thus obtaining fingerprint features for identification. In application, the fingerprint pattern must first be collected as a template for recognition; therefore, the fingerprint template has a significant impact on identification.

[0004] In related technologies, fingerprint template registration involves repeatedly pressing and releasing the finger to acquire the fingerprint position. During this process, the user is required to repeatedly place their finger on the fingerprint sensor, and after each placement, they are instructed to lift their finger and adjust its position so that other parts of the finger are in contact with the sensor when placed back on it. This repetitive pressing and lifting method is inefficient, resulting in a lengthy fingerprint template registration process. Furthermore, if the finger moves during the pressing process, the movement can distort the fingerprint image, causing the registered fingerprint template to differ significantly from the actual fingerprint, thus affecting the final fingerprint recognition performance. Summary of the Invention

[0005] In view of the above problems, this disclosure provides a fingerprint processing method, apparatus and electronic device to at least partially solve the above technical problems.

[0006] In a first aspect, embodiments of this disclosure provide a fingerprint processing method, comprising: during fingerprint template registration, acquiring fingerprint data using a fingerprint sensor at a first frame rate, wherein each frame of fingerprint data includes multiple sub-data of different phases, at least two of which are acquired based on the same configuration; comparing the differences between two sub-data of different phases acquired based on the same configuration to obtain the motion ambiguity of the frame of fingerprint data; and registering a fingerprint template based on the motion ambiguity of the frame of fingerprint data.

[0007] Optionally, comparing the differences between the two sub-data sets of different phases acquired based on the same configuration to obtain the motion ambiguity of the fingerprint data frame includes: determining a difference map between the two sub-data sets of different phases acquired based on the same configuration; determining the dispersion of the difference map in the spatial domain; and determining the motion ambiguity of the fingerprint data frame based on the dispersion.

[0008] Optionally, the method further includes: determining candidate fingerprint images for registration as fingerprint templates based on the frame of fingerprint data; the above-mentioned determination of motion blur of the frame of fingerprint data based on dispersion includes: determining the signal quantity of the candidate fingerprint image; normalizing the dispersion based on the signal quantity to obtain the motion blur of the frame of fingerprint data.

[0009] Optionally, the method further includes: determining candidate fingerprint images for registration as fingerprint templates based on the frame of fingerprint data; the above comparison of the differences between two sub-data of different phases acquired based on the same configuration to obtain the motion blur of the frame of fingerprint data further includes: determining the image quality score of the candidate fingerprint image; adjusting the motion blur based on the image quality score, wherein the motion blur is negatively correlated with the image quality score.

[0010] Optionally, adjusting motion blur based on image quality score includes: comparing the image quality score with at least one quality score threshold to obtain an image quality score range corresponding to the image quality score; and adjusting the motion blur according to a ratio corresponding to the image quality score range.

[0011] Optionally, if the image quality score is greater than or equal to a first score threshold, the motion blur is reduced by a first ratio; if the image quality score is less than the first score threshold but greater than or equal to a second score threshold, the motion blur is reduced by a second ratio, where the first ratio is greater than the second ratio; if the image quality score is less than the second score threshold, the motion blur remains unchanged.

[0012] Optionally, the above-mentioned fingerprint template registration based on the motion blur of the fingerprint data frame includes: classifying the fingerprint data frame according to at least one motion blur threshold to obtain the motion blur type of the fingerprint data frame, the motion blur type including non-blurred type and fully blurred type; and registering the fingerprint template based on the motion blur type of the fingerprint data frame.

[0013] Optionally, the above-mentioned classification of the fingerprint data frame based on at least one motion blur threshold to obtain the motion blur type of the fingerprint data frame includes: if the motion blur is less than or equal to a first motion blur threshold, determining that the motion blur type of the fingerprint data frame is a non-blurred type; if the motion blur is greater than the first motion blur threshold and less than or equal to a second motion blur threshold, determining that the motion blur type of the fingerprint data frame is a semi-blurred type; if the motion blur is greater than the second motion blur threshold, determining that the motion blur type of the fingerprint data frame is a fully blurred type.

[0014] Optionally, the above further includes: determining candidate fingerprint images for registration as fingerprint templates based on the fingerprint data frame; the above-mentioned registration of fingerprint templates based on the motion blur type of the fingerprint data frame includes: the fingerprint image type is semi-blurred or unblurred, and determining whether to register the candidate fingerprint image as a fingerprint template based on the image quality score and / or effective area of ​​the candidate fingerprint image.

[0015] Optionally, the above-mentioned determination of whether to register a candidate fingerprint image as a fingerprint template based on the image quality score and / or effective area of ​​the candidate fingerprint image includes: detecting whether the effective area of ​​the candidate fingerprint image is greater than an area threshold; if the effective area of ​​the candidate fingerprint image is greater than the area threshold, detecting whether the image quality score of the candidate fingerprint image is greater than a third score threshold; if the image quality score of the candidate fingerprint image is greater than the third score threshold, registering the candidate fingerprint image as a fingerprint template.

[0016] Optionally, the above-mentioned fingerprint template registration based on the motion blur type of the fingerprint data frame includes: if the motion blur type of the fingerprint data frame is unblurred, registering a first type of fingerprint template based on the fingerprint data frame; if the motion blur type of the fingerprint data frame is semi-blurred, registering a second type of fingerprint template based on the fingerprint data frame.

[0017] Optionally, the second type of fingerprint template is used to assist the first type of fingerprint template in fingerprint matching.

[0018] Optionally, the above method further includes: determining whether the number of registered first-type fingerprint templates has reached a threshold; if the number of registered first-type fingerprint templates has reached the threshold, ending the fingerprint template registration; if the number of registered first-type fingerprint templates has not reached the threshold, continuing the above steps of collecting fingerprint data by the fingerprint sensor at the first frame rate.

[0019] Optionally, the above method further includes: during the fingerprint recognition process, collecting fingerprint data by a fingerprint sensor at a second frame rate, wherein the first frame rate is greater than the second frame rate.

[0020] Optionally, the fingerprint template registration process includes a process in which the finger continuously contacts and moves with the fingerprint acquisition area, the movement being used to change the fingerprint position in contact with the fingerprint acquisition area.

[0021] Optionally, the fingerprint sensor described above includes an ultrasonic fingerprint sensor.

[0022] Secondly, embodiments of this disclosure also provide a fingerprint processing apparatus, comprising: an acquisition module, configured to acquire fingerprint data at a first frame rate via a fingerprint sensor during fingerprint template registration, wherein each frame of fingerprint data includes multiple sub-data of different phases, at least two of which are acquired based on the same configuration; a registration module, configured to compare the differences between two sub-data of different phases acquired based on the same configuration to obtain the motion ambiguity of the frame of fingerprint data; and to register a fingerprint template based on the motion ambiguity of the frame of fingerprint data.

[0023] Optionally, the registration module is used to determine the difference map of two sub-data of different phases acquired based on the same configuration, determine the dispersion of the difference map in the spatial domain, and determine the motion blur of the fingerprint data of the frame based on the dispersion.

[0024] Optionally, the registration module is further configured to determine candidate fingerprint images for registration as fingerprint templates based on the fingerprint data frame; and the registration module is configured to determine the signal quantity of the candidate fingerprint images; and normalize the discreteness based on the signal quantity to obtain the motion blur of the fingerprint data frame.

[0025] Optionally, the registration module is further configured to determine candidate fingerprint images for registration as fingerprint templates based on the frame of fingerprint data; and the registration module is configured to determine the image quality score of the candidate fingerprint images; and adjust the motion blur based on the image quality score, wherein the motion blur is negatively correlated with the image quality score.

[0026] Optionally, a registration module is used to compare the image quality score with at least one quality score threshold to obtain an image quality score range corresponding to the image quality score; and to adjust the motion blur according to the proportion corresponding to the image quality score range.

[0027] Optionally, the registration module is used to classify the frame of fingerprint data according to at least one motion blur threshold to obtain the motion blur type of the frame of fingerprint data, the motion blur type including non-blurred type and fully blurred type; and to register the fingerprint template according to the motion blur type of the frame of fingerprint data.

[0028] Optionally, the registration module is used to determine the motion blur type of the fingerprint data frame as non-blurred if the motion blur is less than or equal to a first motion blur threshold; to determine the motion blur type of the fingerprint data frame as semi-blurred if the motion blur is greater than the first motion blur threshold and less than or equal to a second motion blur threshold; and to determine the motion blur type of the fingerprint data frame as fully blurred if the motion blur is greater than the second motion blur threshold.

[0029] Optionally, the registration module is further configured to determine candidate fingerprint images for registration as fingerprint templates based on the fingerprint data of the frame; and the registration module is configured to determine whether to register the candidate fingerprint image as a fingerprint template based on the image quality score and / or effective area of ​​the candidate fingerprint image if the fingerprint image type is semi-fuzzy or non-fuzzy.

[0030] Optionally, the registration module is used to register a first type of fingerprint template based on the fingerprint data frame if the motion blur type of the fingerprint data frame is non-blurred; and to register a second type of fingerprint template based on the fingerprint data frame if the motion blur type of the fingerprint data frame is semi-blurred.

[0031] Thirdly, embodiments of this disclosure also provide an electronic device, including: a fingerprint sensor; and the fingerprint processing device described above.

[0032] Fourthly, embodiments of this disclosure also provide an electronic device, including: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the methods described above in embodiments of this disclosure.

[0033] Fifthly, embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods described above in embodiments of this disclosure.

[0034] The fingerprint processing method, apparatus, and electronic device provided in this disclosure, during the fingerprint template registration process, each frame of fingerprint data collected includes multiple sub-data of different phases. At least two of these sub-data of different phases are collected based on the same configuration. By comparing the differences between the two sub-data of different phases collected based on the same configuration, the motion ambiguity of the fingerprint frame is obtained. Fingerprint template registration is performed based on the motion ambiguity of the fingerprint frame. This can at least partially avoid registering fingerprint images that are distorted due to finger movement as fingerprint templates. It allows the finger to continuously contact and move with the fingerprint collection area during the fingerprint template registration process, rather than being limited to a repeated press-and-release method. This can improve the user input flexibility during fingerprint template registration, improve the efficiency of fingerprint template registration, and ensure the quality of the registered fingerprint templates.

[0035] These or other aspects of this disclosure will become more apparent in the following description of embodiments. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1A A schematic diagram of an electronic device applicable according to exemplary embodiments of the present disclosure is shown.

[0038] Figure 1B A schematic diagram of another electronic device that may be applied according to exemplary embodiments of the present disclosure is shown.

[0039] Figure 1C A system block diagram of an electronic device to which exemplary embodiments of the present disclosure may be applied is shown.

[0040] Figure 2 A flowchart of a fingerprint processing method according to an exemplary embodiment of the present disclosure is shown.

[0041] Figure 3 A flowchart of a motion ambiguity determination method according to an exemplary embodiment of the present disclosure is shown.

[0042] Figure 4 A flowchart is shown of a method for registering a fingerprint template based on motion ambiguity according to an exemplary embodiment of the present disclosure.

[0043] Figure 5 A structural block diagram of a fingerprint processing apparatus according to an exemplary embodiment of the present disclosure is shown.

[0044] Figure 6 A structural block diagram of an ultrasonic fingerprint processing system according to an exemplary embodiment of the present disclosure is shown.

[0045] Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0046] Embodiments of this disclosure are described in detail below, with examples of embodiments shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this disclosure, and should not be construed as limiting this disclosure.

[0047] To enable those skilled in the art to better understand the solutions disclosed herein, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0048] In this disclosure, it should be noted that relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0049] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0050] In the description of embodiments in this disclosure, terms such as "example" or "for example" are used to indicate exemplification, illustration, or description. Any embodiment or design described as "example" or "for example" in this disclosure is not to be construed as being more preferred or having more advantages than another embodiment or design. The use of terms such as "example" or "for example" is intended to present relative concepts in a clear manner.

[0051] Furthermore, in this disclosure, "multiple" refers to two or more. Therefore, in this disclosure, "multiple" can also be understood as "at least two." "At least one" can be understood as one or more, such as one, two, or more. For example, including at least one means including one, two, or more, and is not limited to which ones are included. For instance, including at least one of A, B, and C could mean including A, B, C, A and B, A and C, B and C, or A and B and C.

[0052] It should be noted that in this embodiment of the disclosure, "and / or" describes the relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. In addition, the character " / ", unless otherwise specified, generally indicates that the associated objects before and after it are in an "or" relationship.

[0053] Figure 1A and Figure 1B The illustration shows schematic diagrams of electronic devices in which various schemes described herein may be implemented according to exemplary embodiments of the present disclosure, such as... Figure 1A and Figure 1B As shown, the electronic device 100 may include a device body 101 and a fingerprint sensor 102. The fingerprint sensor 102 can acquire fingerprint images for fingerprint recognition. The fingerprint sensor 102 may include, but is not limited to, capacitive fingerprint sensors, optical fingerprint sensors, ultrasonic fingerprint sensors, etc., and the specific location of the fingerprint sensor 102 in the electronic device 100 may be set on the side, back, front, or below the display screen of the device body 101, depending on the actual product design requirements.

[0054] In some embodiments, the electronic device 100 may be a portable electronic device, such as a smartphone, tablet computer, laptop computer, personal digital assistant, etc. In other embodiments, the electronic device 100 may also be a smart wearable device. This disclosure does not limit the type of electronic device 100.

[0055] In some embodiments, the fingerprint sensor 102 may be specifically disposed on the side of the device body 101 of the electronic device 100; as smartphones or other portable electronic devices develop towards thinner or foldable designs, the thickness of the electronic device 100 becomes smaller and smaller, resulting in the fingerprint sensor 102 disposed on the side of the device body 101 becoming narrower and narrower.

[0056] Please see Figure 1A In a typical embodiment, the device body 101 includes a display screen 10 and a mid-frame 20. The display screen 10 is located on the front of the device body 101, used to display images and provide a human-computer interaction interface for the user. The mid-frame 20 is generally located between the display screen 10 and the rear shell of the electronic device, used to support the display screen 10 and to carry various functional components inside the device body 101, such as the motherboard, battery, camera, speaker, microphone, various sensing units, etc. In a specific embodiment, the mid-frame 20 includes a border surrounding the device body 101. The border may include multiple sides and carry a power button, volume buttons, or other function buttons. A fingerprint sensor 102 may be disposed on one side of the border and has a sensing area 108. In a specific embodiment, the fingerprint sensor 102 may be a fingerprint recognition chip or a fingerprint module with a fingerprint recognition chip. It may be integrated above the power button or volume buttons on the side of the border, embedded in a predetermined area on the side of the border, or attached to the inner surface of the side of the border, to allow the user to input their fingerprint to realize the side fingerprint function of the electronic device 100.

[0057] In some embodiments, the fingerprint sensor 102 may be specifically disposed below the display screen of the electronic device 100, with the display screen located on the front of the device body 101, for displaying images and providing a human-computer interaction interface for the user. Compared to the fingerprint sensor 102 being disposed in an area outside the display screen on the front of the device body, the fingerprint sensor 102 being disposed below the display screen of the electronic device 100 can improve the screen-to-body ratio of the electronic device. The fingerprint sensor 102 utilizes ultrasonic, optical, or other penetrating technologies inside the screen, which can penetrate various materials to emit ultrasonic or optical signals to the outer surface of the display screen, and receive the reflected signals reflected by the finger, thereby realizing fingerprint image acquisition for fingerprint recognition.

[0058] Please see Figure 1B As another typical embodiment, with Figure 1A The difference in this embodiment is that the fingerprint sensor 102 is located below the display screen 10, i.e., inside the display screen 10. The display screen 10 consists of a cover glass 11, a touchpad 12, and a display panel 13, arranged from top to bottom. The fingerprint sensor 102 can be located below the display panel 13. The fingerprint sensor 102 has a sensing area 108, and the area on the display screen 10 corresponding to the sensing area 108 is the fingerprint collection area. A visual prompt can typically be displayed on the screen 10 to inform the user of the location of the fingerprint collection area. The fingerprint sensor 102 can emit signals using ultrasonic, optical, or other penetrating technologies, allowing the signals to penetrate the cover glass 11, touchpad 12, and display panel 13. This signal can be reflected by a finger located on the outer surface of the cover glass 11, forming a reflected signal. The reflected signal penetrates the cover glass 11, touchpad 12, and display panel 13 to reach the sensing area 108 of the fingerprint sensor 102, whereby the fingerprint sensor 102 generates a fingerprint image based on the reflected signal.

[0059] Please refer to the following: Figure 1C The fingerprint sensor 102 includes a sensing array 103, an output module 104, an interface module 105, and a driving module 106. The sensing array 103 is used to couple with the user's finger when the user presses the fingerprint sensor 102 to input a fingerprint, thereby acquiring the user's fingerprint information. Specifically, it includes multiple arrayed sensing electrodes. The area where the sensing array 103 is located, or its effective fingerprint acquisition area, is the sensing area 108 of the fingerprint sensor 102. The driving module 106 and the output module 104 are connected to the sensing array 103 and the interface module 105, respectively. The driving module 106 drives the sensing array 103 to perform fingerprint scanning to acquire the user's fingerprint information. The output module 104 generates corresponding fingerprint data based on the fingerprint information acquired by the sensing array 103 and outputs the fingerprint data to the control system 120 through the interface module 105. The interface module 105 can specifically be a Serial Peripheral Interface (SPI).

[0060] Continue reading Figure 1C The control system 120 may include one or more general-purpose single-chip or multi-chip processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or combinations thereof. According to some examples, the control system 120 may include dedicated components for controlling the fingerprint sensor 102. In some implementations, the functionality of the control system 120 may be partitioned among one or more controllers or processors, such as between a dedicated sensor controller and an application processor of the electronic device. Reference Figure 1C The control system 120 may include an application processor 121 of the electronic device. The application processor 121 may be a central processing unit (CPU) or other processing unit or control unit with processing capabilities inside the electronic device 100, such as a microcontroller (MCU). It is connected to the interface module 105 and includes a fingerprint processing device. It is mainly used to control the working state of the fingerprint sensor 102, process the fingerprint data output by the fingerprint sensor 102, and perform fingerprint template registration and fingerprint matching verification to determine whether the currently acquired fingerprint image is a legitimate fingerprint. Based on the determination result, it unlocks the electronic device 100 or performs other functions related to fingerprint recognition.

[0061] Fingerprint recognition typically includes a fingerprint template registration phase and a fingerprint verification phase. In the fingerprint template registration phase, a fingerprint template is created by capturing the user's input fingerprint. In the fingerprint verification phase, a query fingerprint image is obtained by capturing the user's input fingerprint and matching it against the registered fingerprint templates to verify whether the query fingerprint image is a valid fingerprint. To reduce the false rejection rate, multiple templates corresponding to the finger are obtained during the fingerprint template registration phase. Please refer to [link to relevant documentation]. Figure 1A The narrowness of the fingerprint sensor 102 results in a small fingerprint area that it can capture; that is, a single fingerprint image represents a small portion of the fingerprint on the finger. Since the user's fingerprint position on the fingerprint sensor 102 varies, multiple fingerprint images need to be captured for fingerprint template registration to obtain multiple templates, thereby reducing the false rejection rate of fingerprint recognition. Please refer to [link to relevant documentation]. Figure 1B The under-display fingerprint sensor 102 can have a large sensing area. Due to factors such as user pressing habits, the position of the fingerprint applied to the fingerprint sensor 102 is also quite varied. Therefore, it is necessary to collect fingerprint images multiple times to register fingerprint templates and obtain multiple templates in order to reduce the false rejection rate of fingerprint recognition.

[0062] To obtain multiple fingerprint templates, the fingerprint template registration process requires the user to repeatedly place their finger on the fingerprint sensor. After each placement, the user is asked to lift their finger and adjust its position so that other parts of the finger contact the sensor when placed back on it, thus registering multiple fingerprint templates for multiple positions of the finger. This repeated pressing and lifting method is inefficient, resulting in a lengthy fingerprint template registration process. Furthermore, if the finger moves during the pressing process, the movement can distort the fingerprint image, causing the registered fingerprint template to differ significantly from the actual fingerprint, thus affecting the final fingerprint recognition performance.

[0063] This disclosure provides a fingerprint processing method that can be applied to, for example... Figure 1A , Figure 1B The electronic device 100 shown is designed to improve the fingerprint template registration experience.

[0064] Figure 2 A flowchart of a fingerprint processing method according to an exemplary embodiment of the present disclosure is shown, such as... Figure 2 As shown, the fingerprint processing method of this disclosure embodiment can be applied to the fingerprint template registration stage, and can at least partially avoid registering fingerprint images that are distorted due to finger movement as fingerprint templates. This allows the finger to continuously contact and move with the fingerprint acquisition area during the fingerprint template registration process, rather than being limited to repeated pressing and lifting of the hand. This can improve the user input flexibility in fingerprint template registration, improve the efficiency of fingerprint template registration, and ensure the quality of the registered fingerprint templates. Specifically, it includes the following steps.

[0065] In step S201, during the fingerprint template registration process, fingerprint data is collected by the fingerprint sensor at a first frame rate. Each frame of fingerprint data includes multiple sub-data of different phases, and at least two of these multiple sub-data of different phases are collected based on the same configuration.

[0066] In the embodiments of this disclosure, in the electronic device 100, the fingerprint sensor 102 can activate the fingerprint acquisition function upon detecting a user's finger contact or according to the instruction of the application processor 121 of the electronic device 100, and acquire the fingerprint information input by the user by pressing the fingerprint sensor 102 through its sensing array 103, and generate a fingerprint image based on the fingerprint information input by the user.

[0067] For example, when the fingerprint sensor 102 is a capacitive fingerprint sensor, the multiple sensing electrodes of the sensing array 103 will form different coupling capacitances with the ridges and valleys of the user's finger. By driving the sensing array 103 to detect the capacitance signals formed by the ridges and valleys with the sensing electrodes, the fingerprint sensor 102 can collect the fingerprint information of the part of the finger pressed by the user in the sensing area 108, and generate a fingerprint image based on the fingerprint information. Specifically, the fingerprint image is a digital image formed by integrating the fingerprint information of the corresponding position of the finger collected by all the sensing electrodes of the sensing array 103. The fingerprint sensor 102 can also perform some processing on the fingerprint image it generates and temporarily store the fingerprint image internally, waiting for the application processor 121 of the electronic device 100 to acquire it.

[0068] For example, when the fingerprint sensor 102 is an ultrasonic fingerprint sensor, the ultrasonic transmitter of the fingerprint sensor 102 emits ultrasonic signals. The ultrasonic signals pass through the skin surface and are reflected by the ridges and valleys of the fingerprint. The ultrasonic energy reflected by the ridges is greater, while the energy reflected by the valleys is less. The ultrasonic receiver of the fingerprint sensor 102 receives the echo signals and converts them into electrical signals indicating the reflected ultrasonic energy. This allows the fingerprint information of the part of the finger pressed by the user in the sensing area 108 to be collected, and a fingerprint image can be generated based on this fingerprint information. In some implementations, the ultrasonic transmitter of the fingerprint sensor 102 may include a piezoelectric transmitter layer. Ultrasonic waves can be generated by applying a voltage to the piezoelectric transmitter layer to cause it to expand or contract, depending on the applied signal. Referring to 1B, the ultrasonic waves generated by the piezoelectric transmitter layer penetrate the display panel 13, touchpad 12, and cover glass 11, etc., and are reflected by the ridges and valleys of the fingerprint. The ultrasonic receiver of the fingerprint sensor 102 may include a piezoelectric receiver layer and a pixel circuit array, each pixel circuit being configured to convert charge generated in the piezoelectric receiver layer adjacent to the pixel circuit into an electrical signal. Each pixel circuit may include a pixel input electrode that couples the piezoelectric receiver layer to the pixel circuit.

[0069] In embodiments of this disclosure, in the electronic device 100, the application processor 121 can respond to a user operation to enter a fingerprint template registration process to obtain multiple fingerprint templates. When entering the fingerprint template registration process, the application processor 121 can connect to the interface module 105 and send instructions to the fingerprint sensor 102 through the interface module 105, causing the fingerprint sensor to collect fingerprint data at a first frame rate for fingerprint template registration. Each frame of fingerprint data can be transmitted to the application processor 121 through the interface module 105.

[0070] In embodiments of this disclosure, in the electronic device 100, the application processor 121 can also display visual prompts on the display screen 10 during the fingerprint template registration process. These visual prompts may include instructions on how to register the fingerprint template. As a typical implementation, the user may be prompted to press their finger on the fingerprint sensor 102 and move their finger while maintaining the pressure, so that different fingerprint positions on the finger are pressed against the fingerprint sensor 102, thereby allowing the fingerprint sensor 102 to acquire fingerprint images from multiple fingerprint positions. It should be understood that embodiments of this disclosure are not limited to a continuous pressing and moving method; it may also be a combination of continuous pressing and moving and pressing-and-releasing, or simply repeated pressing-and-releasing. (See reference...) Figure 1B As shown, the visual prompt may also include a prompt indicating the location of the fingerprint acquisition area. During the fingerprint template registration process, the visual prompt may also include registered fingerprint locations and unregistered fingerprint locations, so that the user can move their finger to allow the fingerprint sensor 102 to acquire a fingerprint image of the unregistered fingerprint location.

[0071] In embodiments of this disclosure, in electronic device 100, application processor 121 can control fingerprint sensor 102 to collect fingerprint data at a first frame rate during fingerprint template registration. For clarity, please refer to [link to documentation]. Figure 1B When a finger presses and moves on the fingerprint collection area of ​​the display screen 10, in order to improve the overall registration experience and reduce registration time, the application processor 121 can set a higher first frame rate. The first frame rate during fingerprint template registration is higher than the second frame rate during fingerprint recognition. As a typical example, the application processor 121 can set the first frame rate during fingerprint template registration to approximately 60Hz to 100Hz, and the second frame rate during fingerprint recognition to 10Hz. Furthermore, the size of the first and second frame rates can be set according to the actual product design requirements.

[0072] In the embodiments of this disclosure, the difference between two sub-data points of different phases acquired based on the same configuration in each frame of fingerprint data can reflect the movement state of the finger when the fingerprint data frame was acquired. The finger movement state affects whether the fingerprint image is distorted and the degree of distortion. Therefore, step S202 can be executed to compare the difference between two sub-data points of different phases acquired based on the same configuration to obtain the motion blur of the fingerprint data frame. Motion blur refers to phenomena such as ridge deformation, distortion, and trailing that occur during finger movement, which may cause the acquired fingerprint ridges to mismatch with the actual fingerprint ridges. Motion blur can measure the probability and degree to which finger movement causes this phenomenon.

[0073] Furthermore, in embodiments of this disclosure, a final fingerprint image can be generated based on at least a portion of sub-data from multiple different phases in a frame of fingerprint data, serving as a candidate fingerprint image for registration as a fingerprint template during the fingerprint template registration process. In the electronic device 100, the application processor 121 can perform processes such as fusion on the multiple sub-data from different phases to obtain the final fingerprint image.

[0074] In one implementation, in the electronic device 100, the application processor 121 can control the fingerprint sensor 102 to acquire sub-data of at least two phases. Specifically, when the fingerprint sensor 102 is an ultrasonic sensor, by adjusting the time interval of the ultrasonic waves emitted by the fingerprint sensor 102, the phase of the received reflected wave can be controlled to obtain multiple sub-data of multiple phases at multiple time points. For example, sub-data of phase P1 is obtained at time t1 of frame F1, sub-data of phase P2 is obtained at time t2, sub-data of phase P3 is obtained at time t3, and so on, until the tth time... i Phase P is obtained at any time i From the sub-data, we obtain i sub-data for i phases at i time points. Further, at time t... i+1 At time P, obtain phase P i+1 Sub-data. t1 and t i+1 The longest time interval can better reflect the state of finger movement, phase P1 and phase P i+1 The sub-data is collected based on the same configuration. If from t1 to t... i+1 If the fingers did not move significantly during this period, then the two sub-data points are essentially the same. If the difference is between t1 and t2... i+1 If the finger moves during this period, the difference between the two sub-data points is positively correlated with the finger's movement state. In step S202, phase P1 and phase P can be... i+1 The sub-data are compared to obtain the motion blur of a frame of fingerprint data.

[0075] As an example, in electronic device 100, a fingerprint image of any phase can be generated as follows: Specifically, fingerprint sensor 102 is controlled to emit an ultrasonic signal corresponding to that phase. The receiver array of fingerprint sensor 102 detects the reflected signal formed by the finger and converts it into an electrical signal. Each receiver in the receiver array of fingerprint sensor 102 corresponds to a specific spatial location, recording the reflected signal at that location. Application processor 121 (or a separately configured analog-to-digital converter, etc.) can convert the electrical signal into a digital signal, and further convert the digital signal into a processable digital format. Since ultrasonic waves propagate at different speeds in different media, the received signals will have different phase shifts. Application processor 121 can perform phase correction on these signals to ensure the correct phase relationship between the signals. For each receiver, the corrected signal is integrated over time to enhance the signal and reduce noise. The integration can be a simple time summation or a weighted integration, where the weights may be related to the phase or amplitude of the signal. Using the integrated signal, a fingerprint image at each receiver location can be reconstructed. This typically involves inverse projection algorithms or beamforming techniques to project the signal back onto the finger surface, forming a two-dimensional or three-dimensional fingerprint image. In the embodiments of this disclosure, the phase offset used for phase correction and the integration time used for integration are substantially the same when generating the first fingerprint image and the second fingerprint image with a predetermined phase, so that the processing methods of the two are substantially the same.

[0076] Continuing with the above implementation method, the above t1 to t i The obtained multi-phase sub-data are fused to highlight fingerprint features and suppress noise. During fingerprint template registration, candidate fingerprint images for registration as fingerprint templates are obtained through fusion. The fusion method can be a simple summation, weighted average, or a more complex image processing algorithm, which is not limited in this disclosure.

[0077] In the above implementation, theoretically, the more phases, the more accurate the final fingerprint image generated from the multi-phase fingerprint image. However, more phases mean a longer time to generate one frame of fingerprint data, which affects the frame rate. Considering that sliding registration has a significant impact on the frame rate, some performance can be sacrificed by using fewer phases. As a typical implementation, one frame of fingerprint data includes fingerprint images from two phases, phase 0 and phase 1, plus one fingerprint image from phase 2 with the same configuration as phase 0, for a total of three phases at three time points. It should be understood that in specific implementations, the number of phases can be set according to the actual product design needs to meet the design requirements related to frame rate and fingerprint image quality.

[0078] Step S202: Compare the differences between two sub-data sets with different phases acquired based on the same configuration to obtain the motion blur of the fingerprint data in that frame.

[0079] In embodiments of this disclosure, in the electronic device 100, after the application processor 121 acquires a frame of fingerprint data generated by the fingerprint sensor 102, it can compare the differences between two sub-data points of different phases acquired based on the same configuration within that frame of fingerprint data to obtain the motion ambiguity of that frame of fingerprint data. The sub-data points contained in each frame of fingerprint data have a temporal order, and two sub-data points with a longer time interval can be selected for comparison to better reflect the finger movement state. For example, the earliest and latest sub-data points in each frame of fingerprint data are selected for comparison to obtain the motion ambiguity of that frame of fingerprint data.

[0080] As one implementation method, comparing the differences between two sub-data sets acquired based on the same configuration but with different phases to obtain the motion blur of the fingerprint data frame includes: determining a difference map between the two sub-data sets acquired based on the same configuration but with different phases; determining the spatial dispersion of the difference map; and determining the motion blur of the fingerprint data frame based on the dispersion. The spatial dispersion of the difference map can be the statistical variance, statistical standard deviation, etc., of each pixel in the difference map.

[0081] The aforementioned dispersion is influenced by factors such as finger pressure and the degree of distinction between fingerprint ridges and valleys. For example, different finger pressures from the same user may result in different calculated dispersions; similarly, different fingers (from the same user or different users) may have different degrees of distinction between fingerprint ridges and valleys, leading to different calculated dispersions. Directly using dispersion as motion ambiguity makes it difficult to standardize fingerprint template registration based on motion ambiguity; a single standard cannot accommodate different pressure levels and degrees of distinction between fingerprint ridges and valleys. Considering that finger pressure and the degree of distinction between fingerprint ridges and valleys result in different signal quantities in the fingerprint image, a further implementation method involves determining the motion ambiguity of a fingerprint frame based on dispersion. This includes: determining the signal quantity of a candidate fingerprint image; and normalizing the dispersion based on this signal quantity to obtain the motion ambiguity of the fingerprint frame. The normalized dispersion, as the motion ambiguity, is largely independent of finger pressure and the degree of distinction between fingerprint ridges and valleys, facilitating the setting of standards for fingerprint template registration based on motion ambiguity.

[0082] The quality of candidate fingerprint images generated from fingerprint data is affected by factors such as the pressure applied by the finger and the degree of distinction between fingerprint ridges and valleys. If the fingerprint ridges and valleys are clearly defined and the pressure applied is appropriate, the quality of the candidate fingerprint image will still be high even if the finger movement is significant (e.g., a large sliding range). When the finger movement is significant, the motion ambiguity obtained in step S201 is usually high, which may lead to the rejection of high-quality candidate fingerprint images, thereby reducing fingerprint template registration efficiency and increasing fingerprint template registration time. Therefore, as a further implementation, the image quality score of the candidate fingerprint image can be determined; the motion ambiguity can be adjusted according to the image quality score, wherein the motion ambiguity is negatively correlated with the image quality score. The adjusted motion ambiguity is negatively correlated with the image quality score, which can avoid rejecting candidate fingerprint images with high image quality scores when registering fingerprint templates based on motion ambiguity, thereby improving fingerprint template registration efficiency and reducing fingerprint template registration time.

[0083] In the above implementation, for users with good fingerprint conditions (distinct ridges and valleys), even if finger movement is significant during registration (correspondingly, higher motion blur before adjustment), candidate fingerprint images with high image quality scores can still be obtained. The adjusted motion blur combines image quality score and motion blur, avoiding the discarding of candidate fingerprint images with high image quality scores. This allows for rapid finger swiping to quickly acquire high-quality fingerprint images of the effective fingerprint position, thus quickly completing fingerprint template registration. For users with poor fingerprint conditions (indistinct ridges and valleys), fingerprint template registration can be performed by swiping the finger more slowly.

[0084] As a further typical implementation, the above-described adjustment of motion blur based on image quality score includes: comparing the image quality score with at least one quality score threshold to obtain an image quality score interval corresponding to the image quality score; and adjusting the motion blur according to a proportion corresponding to the image quality score interval. For example, a first score threshold and a second score threshold are set to divide the image quality score into three intervals. If the image quality score is greater than or equal to the first score threshold, the motion blur is reduced according to a first proportion; if the image quality score is less than the first score threshold but greater than or equal to the second score threshold, the motion blur is reduced according to a second proportion, where the first proportion is greater than the second proportion; if the image quality score is less than the second score threshold, the motion blur remains unchanged. It should be understood that embodiments of this disclosure can set more or fewer score thresholds; more score thresholds result in more precise adjustment of motion blur based on the image quality score.

[0085] The typical process of obtaining motion ambiguity described above can be achieved through... Figure 3 Summarize the flowchart shown. Figure 3A flowchart of a motion ambiguity determination method according to an exemplary embodiment of the present disclosure is shown, such as... Figure 3 As shown, the method for obtaining motion ambiguity in this embodiment of the present disclosure includes steps S301 to S309.

[0086] Step S301: Determine the difference map between two sub-data of different phases acquired based on the same configuration in a frame of fingerprint data.

[0087] For example, a frame of fingerprint data includes two phases, phase 0 and phase 1, plus one phase 2 configured with phase 0, for a total of three phases. The difference map between the fingerprint image of phase 0 and the fingerprint image of phase 2 in this frame of fingerprint data is determined.

[0088] Step S302: Determine the spatial dispersion of the difference map. Specifically, the spatial dispersion of the difference map can be the statistical variance, statistical standard deviation, etc., of each pixel in the difference map.

[0089] Step S303: Generate candidate fingerprint images for registration as fingerprint templates based on the fingerprint data of the frame.

[0090] Step S304: Determine the signal quantity and image quality score of the candidate fingerprint image.

[0091] Step S305: Normalize the discreteness based on the signal quantity to obtain the normalized discreteness and obtain the initial motion ambiguity.

[0092] Step S306: Compare the image quality score with a first quality score threshold and a second quality score threshold. If the image quality score is greater than or equal to the first score threshold, proceed to step S307; if the image quality score is less than the first score threshold but greater than or equal to the second score threshold, proceed to step S308; if the image quality score is less than the second score threshold, proceed to step S309.

[0093] Step S307: Reduce the initial motion blur according to the first ratio to obtain the motion blur.

[0094] Step S308: Reduce the initial motion blur according to the second ratio to obtain the motion blur.

[0095] The first proportion is greater than the second proportion.

[0096] Step S309: Keep the initial motion blur unchanged and obtain the motion blur. That is, if the image quality score is less than the second score threshold, adjust the ratio to 1.

[0097] For example, the first quality score threshold is 50, and the second quality score threshold is 35. The initial motion blur is denoted as S0, and the motion blur is denoted as S. If the image quality score is greater than or equal to 50, the initial motion blur is reduced by a factor of 5, then S = S0 / 5; if the image quality score is less than 50 but greater than or equal to 35, the initial motion blur is reduced by a factor of 2, i.e., S = S0 / 2; if the image quality score is less than 35, the initial motion blur remains unchanged, i.e., S = S0.

[0098] pass Figure 3 Motion ambiguity is obtained in the manner shown. This dispersion is normalized based on the signal quantity of the candidate fingerprint images. The normalized dispersion serves as the initial motion ambiguity, and its magnitude is largely independent of finger pressure and the distinction between fingerprint ridges and valleys, facilitating the setting of standards for fingerprint template registration based on motion ambiguity. The initial motion ambiguity is adjusted based on the image quality score of the candidate fingerprint images. The adjusted motion ambiguity combines the image quality score and motion ambiguity, avoiding the rejection of candidate fingerprint images with high image quality scores, increasing the probability of high-quality candidate fingerprint images passing motion ambiguity judgment, and reducing fingerprint template registration time.

[0099] Step S203: Register the fingerprint template based on the motion ambiguity of the fingerprint data frame.

[0100] In embodiments of this disclosure, the motion ambiguity of the fingerprint data frame can be used to determine whether to register the candidate fingerprint image corresponding to the fingerprint data as a template. In some embodiments, it can be further determined what type of template to register the candidate fingerprint image as.

[0101] As one implementation, fingerprint template registration based on the motion ambiguity of the fingerprint data frame specifically includes: classifying the fingerprint data according to at least one motion ambiguity threshold to obtain the motion ambiguity type of the fingerprint data frame; and registering a fingerprint template based on the motion ambiguity type of the fingerprint data frame. The motion ambiguity type can include a non-ambiguous type and a fully ambiguous type. In some implementations, if the motion ambiguity type is non-ambiguous, the corresponding candidate fingerprint image is registered as a fingerprint template; if the motion ambiguity type is fully ambiguous, the corresponding fingerprint data is discarded, i.e., the candidate fingerprint image is not registered as a fingerprint template. In some implementations, the motion ambiguity type can include a non-ambiguous type, a semi-ambiguous type, and a fully ambiguous type. If the motion ambiguity type is non-ambiguous, the corresponding candidate fingerprint image is registered as a first type of fingerprint template; if the motion ambiguity type is semi-ambiguous, the corresponding candidate fingerprint image is registered as a second type of fingerprint template; if the motion ambiguity type is fully ambiguous, the corresponding fingerprint data is discarded, i.e., the candidate fingerprint image is not registered as a fingerprint template. The second type of fingerprint template is used to assist the first type of fingerprint template in fingerprint matching. Specifically, the first type of fingerprint template can be a strong fingerprint template, and the second type of fingerprint template can be a weak fingerprint template. A strong fingerprint template can be used alone for fingerprint matching, while a weak fingerprint template can be used to assist fingerprint matching, but not as a standalone fingerprint matching tool.

[0102] As one implementation method, the above-mentioned classification of fingerprint data based on at least one motion ambiguity threshold to obtain the motion ambiguity type of the fingerprint data may specifically include: if the motion ambiguity is less than or equal to a first motion ambiguity threshold, the motion ambiguity type of the fingerprint data is determined to be non-ambiguous, that is, the fingerprint pattern is basically not blurred due to finger movement, and the fingerprint pattern is basically normal; if the motion ambiguity is greater than the first motion ambiguity threshold and less than or equal to a second motion ambiguity threshold, the motion ambiguity type of the fingerprint data is determined to be semi-ambiguous, that is, the fingerprint pattern is slightly deformed due to finger movement; if the motion ambiguity is greater than the second motion ambiguity threshold, the motion ambiguity type of the fingerprint data is determined to be fully ambiguous, that is, the fingerprint pattern is abnormal due to finger movement.

[0103] In embodiments of this disclosure, before registering a candidate fingerprint image as a fingerprint template, it can be determined whether to register the candidate fingerprint image as a fingerprint template based on the image quality score and / or effective area of ​​the candidate fingerprint image. As one implementation, determining whether to register a candidate fingerprint image as a fingerprint template based on the image quality score and / or effective area of ​​the candidate fingerprint image can specifically include: detecting whether the effective area of ​​the candidate fingerprint image is greater than an area threshold; if the effective area of ​​the candidate fingerprint image is greater than the area threshold, detecting whether the image quality score of the candidate fingerprint image is greater than a third score threshold; if the image quality score of the candidate fingerprint image is greater than the third score threshold, registering the candidate fingerprint image as a fingerprint template. For example, when the motion blur type is non-blurred, the corresponding candidate fingerprint image is registered as a first-type fingerprint template; when the motion blur type is semi-blurred, the corresponding candidate fingerprint image is registered as a second-type fingerprint template.

[0104] In the embodiments of this disclosure, it is determined whether the number of registered first-type fingerprint templates has reached a threshold; if the number of registered first-type fingerprint templates has reached the threshold, the fingerprint template registration ends; if the number of registered first-type fingerprint templates has not reached the threshold, fingerprint data is collected by the fingerprint sensor at a first frame rate to continue fingerprint template registration.

[0105] The typical process of registering fingerprint templates based on the motion ambiguity of each frame of fingerprint data described above can be achieved through... Figure 4 Summarize the flowchart shown. Figure 4 A flowchart illustrating a method for registering a fingerprint template based on motion ambiguity according to an exemplary embodiment of the present disclosure is shown, such as... Figure 4 As shown, the method for registering fingerprint templates based on the motion ambiguity of each frame of fingerprint data in this embodiment includes steps S401 to S407.

[0106] Step S401: Compare the motion blur of each frame of fingerprint data with a first motion blur threshold and a second motion blur threshold. The motion blur can be determined based on embodiments of this disclosure, for example using... Figure 3 The method shown is fixed and will not be elaborated upon here.

[0107] If the motion blur is less than or equal to the first motion blur threshold, the motion blur type of the fingerprint data frame is determined to be non-blurred, and the process proceeds to step S402; if the motion blur is greater than the first motion blur threshold and less than or equal to the second motion blur threshold, the motion blur type of the fingerprint data frame is determined to be semi-blurred, and the process proceeds to step S403; if the motion blur is greater than the second motion blur threshold, the motion blur type of the fingerprint data frame is determined to be fully blurred, and the fingerprint data frame is not registered as a fingerprint template. The fingerprint data is then collected by the fingerprint sensor at the first frame rate to continue fingerprint template registration.

[0108] Step S402: Based on the image quality score and effective area of ​​the candidate fingerprint image corresponding to the fingerprint data frame, determine whether to register the candidate fingerprint image as a fingerprint template. If yes, proceed to step S404. Otherwise, do not register it as a fingerprint template, and continue to collect fingerprint data through the fingerprint sensor at the first frame rate to continue fingerprint template registration.

[0109] Step S403: Based on the image quality score and effective area of ​​the candidate fingerprint image corresponding to the fingerprint data frame, determine whether to register the candidate fingerprint image as a fingerprint template. If yes, proceed to step S405. Otherwise, do not register it as a fingerprint template, and continue to collect fingerprint data through the fingerprint sensor at the first frame rate to continue fingerprint template registration.

[0110] Step S404: Register the candidate fingerprint image corresponding to the fingerprint data frame as a strong template.

[0111] Step S405: Register the candidate fingerprint image corresponding to the fingerprint data frame as a weak template.

[0112] Step S406: Determine whether the number of registered strong templates has reached the threshold. If the number of registered strong templates has reached the threshold, proceed to step S407; if the number of registered strong templates has not reached the threshold, continue to collect fingerprint data through the fingerprint sensor at the first frame rate to continue fingerprint template registration.

[0113] Step S407: Package the fingerprint template to complete the registration.

[0114] The fingerprint processing method of this disclosure employs finger swiping and other motion methods during fingerprint template registration. By increasing the frame rate of fingerprint image acquisition, the registration experience is improved, and registration time is reduced. To ensure fingerprint image quality during finger movement, the finger movement state during data acquisition is determined by comparing the differences between two sub-data points of different phases acquired based on the same configuration within a single fingerprint data frame. This allows for the filtering of fingerprint data frames with no movement or minimal movement, improving the registration experience without affecting the image quality of the template and thus maintaining the recognition success rate. When the finger moves across the fingerprint position on the screen, the acquisition frame rate is higher than that used during the fingerprint recognition process to further improve the overall registration experience and reduce registration time. Considering that finger movement speed varies greatly among users, it is inevitable that deformed fingerprint signals will be acquired. Furthermore, the inconsistency between the registered template area and the actual fingerprint pattern can severely impact recognition efficiency. Therefore, selecting useful signals that are essentially identical to the actual fingerprint and processing these effective signals significantly improves recognition accuracy.

[0115] Embodiments of this disclosure also provide a fingerprint processing device, such as... Figure 5 As shown, an embodiment of this disclosure provides a fingerprint processing apparatus that may include a data acquisition module 501 and a registration module 502. The data acquisition module 501 is used to acquire fingerprint data via a fingerprint sensor at a first frame rate during fingerprint template registration. Each frame of fingerprint data includes multiple sub-data of different phases, at least two of which are acquired based on the same configuration. The registration module 502 is used to compare the differences between two sub-data of different phases acquired based on the same configuration to obtain the motion ambiguity of the fingerprint frame; and to register the fingerprint template based on the motion ambiguity of the fingerprint frame.

[0116] In some implementations, the registration module 502 can be specifically used to determine the difference map of two sub-data of different phases acquired based on the same configuration, determine the dispersion of the difference map in the spatial domain, and determine the motion blur of the fingerprint data of the frame based on the dispersion.

[0117] In some implementations, the registration module 502 is further configured to determine candidate fingerprint images for registration as fingerprint templates based on the frame of fingerprint data. More specifically, the registration module 502 may be configured to: determine the signal quantity of the candidate fingerprint image; and normalize the dispersion based on the signal quantity to obtain the motion blur of the frame of fingerprint data.

[0118] In some embodiments, the registration module 502 is further configured to determine candidate fingerprint images for registration as fingerprint templates based on the frame of fingerprint data. Further, the registration module 502 may specifically be configured to: determine the image quality score of the candidate fingerprint images; and adjust the motion blur based on the image quality score, wherein the motion blur is negatively correlated with the image quality score.

[0119] In one implementation, the registration module 502 can be specifically used to: compare the image quality score with at least one quality score threshold to obtain an image quality score range corresponding to the image quality score; and adjust the motion blur according to the ratio corresponding to the image quality score range.

[0120] In one implementation, the registration module 502 can be specifically used to: classify fingerprint data according to at least one motion ambiguity threshold to obtain the motion ambiguity type of the fingerprint data frame, the motion ambiguity type including non-ambiguous type and fully ambiguous type; and register fingerprint templates according to the motion ambiguity type of the fingerprint data frame.

[0121] In one implementation, the registration module 502 can be specifically used to: if the motion ambiguity is less than or equal to a first motion ambiguity threshold, determine that the motion ambiguity type of the fingerprint data frame is a non-ambiguous type; if the motion ambiguity is greater than the first motion ambiguity threshold and less than or equal to a second motion ambiguity threshold, determine that the motion ambiguity type of the fingerprint data frame is a semi-ambiguous type; if the motion ambiguity is greater than the second motion ambiguity threshold, determine that the motion ambiguity type of the fingerprint data frame is a fully ambiguous type.

[0122] In one implementation, the registration module 502 is further configured to determine candidate fingerprint images for registration as fingerprint templates based on the fingerprint data frame. More specifically, the registration module 502 may be configured to: if the fingerprint image is of a semi-fuzzy or unfuzzy type, determine whether to register the candidate fingerprint image as a fingerprint template based on the image quality score and / or effective area of ​​the candidate fingerprint image.

[0123] Furthermore, the registration module 502 can be specifically used to: register a first type of fingerprint template based on the fingerprint data if the motion blur type of the fingerprint data frame is non-blurred; and register a second type of fingerprint template based on the fingerprint data frame if the motion blur type of the fingerprint data is semi-blurred.

[0124] In some embodiments, the fingerprint processing device can be with Figure 1A , Figure 1B and Figure 1C The fingerprint sensor 102 shown forms a fingerprint recognition system within the electronic device 100, wherein the fingerprint processing device may specifically be... Figure 1B and 1C The fingerprint processing device shown can be configured in the application processor 121 (e.g., a central processing unit CPU) of the electronic device 100 to execute the main steps of the fingerprint processing methods described in the above embodiments. In other alternative embodiments, the fingerprint processing device can also be implemented using other processing units or control units with image processing capabilities (e.g., microcontrollers MCUs).

[0125] In some embodiments, the fingerprint processing device may be provided by, for example, Figure 6 The modules shown are used to implement this. For example... Figure 6As shown, the fingerprint system 600 includes the following modules: an ultrasonic fingerprint sensor 601, a controller 602, a data processor 603, an analog-to-digital converter 604, and an algorithm processor 605. Each module is controlled by the controller 602. The controller 602 controls the ultrasonic fingerprint sensor 601 to generate and receive signals. The ultrasonic fingerprint sensor 601 performs analog-to-digital conversion. The data processor 603 rearranges and packages the data. The converted data is then sent to the algorithm processor 605 for algorithm processing to complete fingerprint template registration and recognition. The algorithm processor 605 can execute the main steps of the fingerprint processing methods described in the above embodiments. The controller 602, data processor 603, analog-to-digital converter 604, and algorithm processor 605 can serve as... Figure 1C One implementation of the control system 120 shown is described. The functions of the fingerprint processing device are implemented by a controller 602, a data processor 603, an analog-to-digital converter 604, and an algorithm processor 605.

[0126] This invention implements sliding fingerprint template registration in an ultrasonic fingerprint system. Benefiting from the high frame rate of the ultrasonic fingerprint sensor 601, the ultrasonic fingerprint template registration process, unlike traditional finger-press registration, can utilize finger-sliding motion. By increasing the ultrasonic signal frame rate, the fingerprint signal is acquired, improving the registration experience and reducing registration time. To ensure fingerprint image quality during movement, the differences between fingerprint images at different time points are compared to determine the finger movement state during data acquisition. This allows for the filtering of image signals without significant movement, improving the registration experience without affecting the template image quality and thus maintaining the recognition success rate. Compared to other schemes that use press-and-release template registration, the ultrasonic signal frame rate is higher, allowing for the acquisition of more data in a shorter time, even during movement. This makes it easier to obtain images without motion blur, effectively completing the fingerprint position signal acquisition in a shorter time, thus improving the fingerprint template registration experience and efficiency.

[0127] Embodiments of this disclosure also provide an electronic device 100, including a device body 101 and the aforementioned fingerprint sensor 102 disposed on the device body 101. In some embodiments, the electronic device 100 may be a portable electronic device, such as a smartphone, tablet computer, laptop computer, personal digital assistant, etc. Alternatively, the electronic device 100 may also be a smart wearable device, and embodiments of this disclosure do not limit this to that.

[0128] The electronic device 100 provided in the embodiments of this disclosure may further include: an application processor 121; and a memory storing a program, wherein the program includes instructions that, when executed by the application processor 121, cause the application processor 121 to perform the methods of the above embodiments, for example... Figures 2 to 4 The method shown.

[0129] Embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the application processor 121 of the electronic device 100 to execute the methods of the above embodiments, for example... Figures 2 to 4 The method shown.

[0130] refer to Figure 7 This is a structural block diagram of an electronic device 700 provided in an embodiment of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device 700 may include a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the electronic device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0131] Multiple components in electronic device 700 are connected to I / O interface 705, including: input unit 706, output unit 707, storage unit 708, and communication unit 709. Input unit 706 can be any type of device capable of inputting information to electronic device 700. Input unit 706 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 707 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 708 may include, but is not limited to, disk and optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0132] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described in this disclosure. For example, in some embodiments, the fingerprint processing method of the embodiments of this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. In some embodiments, the computing unit 701 can be configured as the method of this embodiment by any other suitable means (e.g., by means of firmware).

[0133] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0134] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0135] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0136] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0137] The above are merely preferred embodiments of this disclosure and are not intended to limit this disclosure in any way. Although this disclosure has been disclosed above with reference to preferred embodiments, it is not intended to limit this disclosure. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the technical solution of this disclosure. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this disclosure without departing from the content of the technical solution of this disclosure shall still fall within the scope of the technical solution of this disclosure.

Claims

1. A method of fingerprint processing, characterized by, Comprising: In the fingerprint template registration process, the fingerprint data is collected by the fingerprint sensor according to a first frame rate, each frame of fingerprint data includes a plurality of different phase sub-data, at least part of the plurality of different phase sub-data is fused to generate a fingerprint image, and the fingerprint image is a candidate fingerprint image registered as a fingerprint template; In the fingerprint verification process, the query fingerprint image is collected by the fingerprint sensor according to a second frame rate, and the query fingerprint image is matched with the fingerprint image registered as the fingerprint template to verify whether the query fingerprint image belongs to a legal fingerprint; Wherein, the first frame rate in the fingerprint template registration process is higher than the second frame rate in the fingerprint verification process.

2. The method of claim 1, wherein, At least two phases of the plurality of different phase sub-data fused to generate a frame of fingerprint image are collected based on the same configuration.

3. The method of claim 2, wherein, The plurality of different phase sub-data fused to generate a frame of fingerprint image includes: first phase sub-data, second phase sub-data and third phase sub-data collected in sequence, and the first phase sub-data and the third phase sub-data are collected based on the same configuration.

4. The method of claim 2, wherein, The plurality of different phase sub-data fused to generate a frame of fingerprint image, wherein the sub-data collected at the earliest time and the sub-data collected at the latest time are collected based on the same configuration.

5. The method of any one of claims 2 to 4, wherein, The method further comprises: comparing the difference between the two different phase sub-data collected based on the same configuration to obtain the motion blur degree of the frame fingerprint data, and performing fingerprint template registration according to the motion blur degree of the frame fingerprint data.

6. The method of claim 5, wherein, The comparison of the difference between the two different phase sub-data collected based on the same configuration to obtain the motion blur degree of the frame fingerprint data comprises: determining the difference map between the two different phase sub-data collected based on the same configuration; determining the dispersion of the difference map in the spatial domain; determining the motion blur degree of the frame fingerprint data according to the dispersion.

7. The method of claim 6, wherein, Further comprising: determining a candidate fingerprint image for registration as a fingerprint template according to the frame fingerprint data; The determination of the motion blur degree of the frame fingerprint data according to the dispersion comprises: determining the signal amount of the candidate fingerprint image; normalizing the dispersion according to the signal amount to obtain the motion blur degree of the frame fingerprint data.

8. The method of claim 6, wherein, Further comprising: determining a candidate fingerprint image for registration as a fingerprint template according to the frame fingerprint data; The comparison of the difference between the two different phase sub-data collected based on the same configuration to obtain the motion blur degree of the frame fingerprint data further comprises: determining an image quality score of the candidate fingerprint image; adjusting the motion blur degree according to the image quality score, wherein the motion blur degree is negatively correlated with the image quality score.

9. The method of claim 8, wherein, The adjustment of the motion blur degree according to the image quality score comprises: comparing the image quality score with at least one quality score threshold to obtain an image quality score interval corresponding to the image quality score; adjusting the motion blur degree according to the proportion corresponding to the image quality score interval.

10. The method of claim 9, wherein, if the image quality score is greater than or equal to a first score threshold, reducing the motion blur degree according to a first ratio; if the image quality score is less than the first score threshold and greater than or equal to a second score threshold, reducing the motion blur degree according to a second ratio, the first ratio being greater than the second ratio; if the image quality score is less than the second score threshold, keeping the motion blur degree unchanged.

11. The method of claim 10, wherein, The fingerprint template registration according to the motion blur degree of the frame fingerprint data comprises: classifying the frame fingerprint data according to at least one motion blur degree threshold to obtain a motion blur type of the frame fingerprint data, the motion blur type comprising a non-blur type and a full-blur type; performing fingerprint template registration according to the motion blur type of the frame fingerprint data.

12. The method of claim 11, wherein, The classifying the frame fingerprint data according to at least one motion blur degree threshold to obtain a motion blur type of the frame fingerprint data comprises: if the motion blur degree is less than or equal to a first motion blur degree threshold, determining the motion blur type of the frame fingerprint data as the non-blur type; if the motion blur degree is greater than the first motion blur degree threshold and less than or equal to a second motion blur degree threshold, determining the motion blur type of the frame fingerprint data as a half-blur type; if the motion blur degree is greater than the second motion blur degree threshold, determining the motion blur type of the frame fingerprint data as the full-blur type.

13. The method of claim 12, wherein, Further comprising: determining a candidate fingerprint image for registration as a fingerprint template according to the frame fingerprint data; The fingerprint template registration according to the motion blur type of the frame fingerprint data comprises: if the type of the fingerprint image is the half-blur type or the non-blur type, judging whether to register the candidate fingerprint image as a fingerprint template according to an image quality score and / or an effective area of the candidate fingerprint image.

14. The method of claim 13, wherein, The judging whether to register the candidate fingerprint image as a fingerprint template according to the image quality score and / or the effective area of the candidate fingerprint image comprises: detecting whether the effective area of the candidate fingerprint image is greater than an area threshold; if the effective area of the candidate fingerprint image is greater than the area threshold, detecting whether an image quality score of the candidate fingerprint image is greater than a third score threshold; if the image quality score of the candidate fingerprint image is greater than the third score threshold, registering the candidate fingerprint image as a fingerprint template.

15. The method of any one of claims 11 to 14, wherein, The fingerprint template registration according to the motion blur type of the frame fingerprint data comprises: if the motion blur type of the frame fingerprint data is the non-blur type, performing a first type of fingerprint template registration according to the frame fingerprint data; if the motion blur type of the frame fingerprint data is the half-blur type, performing a second type of fingerprint template registration according to the frame fingerprint data.

16. The method of claim 15, wherein, The second type of fingerprint template is used to assist the first type of fingerprint template in fingerprint matching.

17. The method of claim 15, wherein, Further comprising: judging whether the registered first type of fingerprint template reaches a quantity threshold; if the registered first type of fingerprint template reaches the quantity threshold, ending the fingerprint template registration; if the registered first type of fingerprint template does not reach the quantity threshold, continuing the step of collecting fingerprint data according to the first frame rate through the fingerprint sensor.

18. The method of claim 1, wherein, The fingerprint template enrollment process includes a process in which a finger is in continuous contact with the fingerprint capture area and is moved to change the fingerprint position of the finger in contact with the fingerprint capture area.

19. The method of claim 1, wherein, The fingerprint sensor includes an ultrasonic fingerprint sensor.

20. An electronic device, comprising: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 19.

21. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are for causing a computer to perform the method according to any one of claims 1 to 19.

Images