Fingerprint wake-up method, electronic device and computer-readable medium

Abstract

The embodiments of the present application disclose a fingerprint wake-up method, an electronic device, and a computer-readable medium. The embodiments of the method include: obtaining the capacitance value of each target sub-area in the fingerprint detection area, and determining the number of target sub-areas whose capacitance value is greater than the corresponding trigger threshold, each target sub-area has a corresponding trigger threshold; when the above number is greater than the target value, waking up the system, recording the first capacitance value of each current target sub-area, and obtaining a fingerprint image of the fingerprint detection area; based on the fingerprint image, generating evaluation information for evaluating whether the wake-up is accurate; based on the evaluation information and the recorded first capacitance value, adjusting the trigger threshold of each target sub-area. This implementation improves the accuracy of fingerprint wake-up and reduces the power consumption of the electronic device.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular to a fingerprint wake-up method, an electronic device, and a computer-readable medium. Background Art

[0002] With the development of computer technology, more and more electronic devices and applications have fingerprint wake-up functions. Generally, when an electronic device detects a finger pressing a fingerprint detection area of ​​the electronic device, the system can be woken up through a hardware interrupt.

[0003] In the prior art, the capacitance value is usually compared with a fixed trigger threshold to detect whether the user presses the fingerprint detection area, thereby deciding whether to wake up the system. However, due to factors such as the module process, environment, and finger state differences, this method is prone to misjudgment, resulting in low fingerprint wake-up accuracy and increased power consumption of electronic devices. Summary of the invention

[0004] The embodiments of the present application propose a fingerprint wake-up method, an electronic device, and a computer-readable medium to solve the technical problems of low accuracy of fingerprint wake-up and high power consumption of the electronic device in the prior art.

[0005] In the first aspect, an embodiment of the present application provides a fingerprint wake-up method, the method comprising: obtaining the capacitance value of each target sub-area in a fingerprint detection area, and determining the number of target sub-areas whose capacitance values ​​are greater than the corresponding trigger threshold, each target sub-area having a corresponding trigger threshold; when the number is greater than the target value, waking up the system, recording the first capacitance value of each current target sub-area, and obtaining a fingerprint image of the fingerprint detection area; based on the fingerprint image, generating evaluation information for evaluating whether the wake-up is accurate; based on the evaluation information and the recorded first capacitance value, adjusting the trigger threshold of each target sub-area.

[0006] In a second aspect, an embodiment of the present application provides an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in aspect...

[0007] In a third aspect, an embodiment of the present application provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0008] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0009] The fingerprint wake-up method, electronic device and computer-readable medium provided in the embodiments of the present application obtain the capacitance value of each target sub-area in the fingerprint detection area, and determine the number of target sub-areas whose capacitance value is greater than the corresponding trigger threshold; then when the number is greater than the target value, wake up the system, record the first capacitance value of each current target sub-area, and obtain the fingerprint image of the fingerprint detection area; then based on the fingerprint image, generate evaluation information for evaluating whether the wake-up is accurate; finally, based on the evaluation information and the recorded first capacitance value, adjust the trigger threshold of each target sub-area. Thus, after each system wake-up, the trigger threshold of each target sub-area can be dynamically adjusted based on the evaluation information that characterizes whether the wake-up is accurate and the capacitance value of each target sub-area when the system is woken up, thereby eliminating the influence of factors such as differences in the process, environment and finger state of the module, improving the accuracy of fingerprint wake-up and reducing the power consumption of the electronic device. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0011] Figure 1 is a flow chart of an embodiment of a fingerprint wake-up method according to the present application;

[0012] Figure 2 is a schematic diagram of a fingerprint detection area according to the present application;

[0013] Figure 3 is a structural schematic diagram of an embodiment of a fingerprint wake-up device according to the present application;

[0014] Figure 4 It is a schematic diagram of the structure of a computer system for implementing an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0016] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0017] Biometric technology has been widely used in various terminal devices or electronic devices. Biometric recognition technology includes but is not limited to fingerprint recognition, palm print recognition, vein recognition, iris recognition, face recognition, liveness recognition, anti-counterfeiting recognition and other technologies. Among them, fingerprint recognition usually includes optical fingerprint recognition, capacitive fingerprint recognition and ultrasonic fingerprint recognition. With the rise of full-screen technology, the fingerprint recognition module can be set in a partial area or the entire area below the display screen to form an under-display optical fingerprint recognition; or, part or all of the optical fingerprint recognition module can be integrated into the display screen of the electronic device to form an in-display optical fingerprint recognition. The above-mentioned display screen can be an organic light emitting diode (OLED) display screen or a liquid crystal display (LCD). The fingerprint recognition method usually includes the steps of acquiring, preprocessing, feature extraction, feature matching and the like of the fingerprint image. Some or all of the above steps can be implemented by a traditional computer vision (CV) algorithm, or by a deep learning algorithm based on artificial intelligence (AI). Fingerprint recognition technology can be applied to portable or mobile terminals such as smartphones, tablets, gaming devices, as well as other electronic devices such as smart door locks, cars, bank ATMs, etc., for fingerprint unlocking, fingerprint payment, fingerprint attendance, identity authentication, etc.

[0018] Electronic devices can be equipped with capacitive fingerprint modules, and use the capacitive fingerprint module to detect whether there is a finger pressing. When it is determined that there is a finger pressing, the system can be awakened by a hardware interrupt. For mobile terminals, due to the influence of battery capacity, there are higher requirements for the power consumption of the entire machine. Therefore, it is necessary to ensure that the finger detection of the capacitive fingerprint module has a high accuracy.

[0019] In the prior art, the capacitance value is usually compared with a fixed trigger threshold to detect whether the user's finger is pressed, so as to decide whether to wake up the system. However, due to factors such as the process, environment and finger state of the module, this method is prone to misjudgment, resulting in low accuracy of fingerprint wake-up and increased power consumption of electronic devices. The embodiments of the present application can solve this problem.

[0020] Please refer to Figure 1, which shows a process 100 of an embodiment of the fingerprint wake-up method according to the present application. The fingerprint wake-up method can be applied to various electronic devices. For example, it can include but is not limited to: smart phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, car computers, PDAs, desktop computers, set-top boxes, smart TVs, wearable devices, etc. The execution subject of the fingerprint wake-up method can be a processor in the above electronic devices, such as Figure 4 The processing device 401 in.

[0021] The fingerprint wake-up method comprises the following steps:

[0022] Step 101, obtaining the capacitance value of each target sub-region in the fingerprint detection region, and determining the number of target sub-regions whose capacitance value is greater than a corresponding trigger threshold, each target sub-region having a corresponding trigger threshold.

[0023] In this embodiment, the electronic device to which the fingerprint wake-up method is applied may be configured with a fingerprint detection area. The fingerprint detection area may be configured with a fingerprint sensor. A large number of tiny capacitors may be arranged on the fingerprint sensor. The capacitor may be configured with a circuit for detecting the capacitance value. Several target sub-areas may be pre-set in the fingerprint detection area. The above-mentioned electronic device may collect the capacitance value of each target sub-area in real time or periodically through the fingerprint sensor. Among them, for each target sub-area, its capacitance value may be the sum of the capacitance values ​​of its pixels.

[0024] In this embodiment, each target sub-region has a corresponding trigger threshold. The trigger thresholds corresponding to different target sub-regions may be the same or different. The initial trigger threshold may be set based on experiments or experience. For each target sub-region, if the capacitance value of the target sub-region is greater than the trigger threshold corresponding to the target sub-region, the target sub-region may be considered to be triggered.

[0025] In this embodiment, after obtaining the capacitance value of each target sub-area each time, the above-mentioned electronic device can compare the capacitance value of each target sub-area with the trigger threshold corresponding to the target sub-area to determine whether the capacitance value is greater than the corresponding trigger threshold, thereby counting the number of target sub-areas whose capacitance values ​​are greater than the corresponding trigger threshold.

[0026] Step 102: When the above number is greater than the target value, wake up the system, record the first capacitance value of each current target sub-area, and obtain the fingerprint image of the fingerprint detection area.

[0027] In this embodiment, if the number of target sub-areas with capacitance values ​​greater than the corresponding trigger threshold is greater than the target value, it means that the user presses the fingerprint detection area, and the system can be awakened at this time. At the same time, the first capacitance value of each current target sub-area (that is, the capacitance value of each target sub-area obtained this time) can be recorded, and the fingerprint image of the fingerprint detection area can also be obtained. Among them, awakening the system can refer to awakening the processing chip, unlocking the target application, etc., which is not specifically limited here.

[0028] Optionally, in order to more accurately evaluate whether the wake-up is accurate, the time for acquiring the fingerprint image, the time for waking up the system, and the time for acquiring the first capacitance value may be the same.

[0029] It should be noted that the above target value may be a value pre-set based on a large number of experiments and data statistics, and its value is not specifically limited here.

[0030] Step 103: Generate evaluation information for evaluating whether the awakening is accurate based on the fingerprint image.

[0031] In this embodiment, the electronic device can detect or identify the fingerprint image, and generate evaluation information for evaluating whether the wake-up is accurate according to the detection result or the identification result. For example, if the fingerprint recognition is passed, the evaluation information for indicating that the wake-up is accurate is generated; on the contrary, if the fingerprint recognition is not passed, the evaluation information for indicating that the wake-up is inaccurate can be generated. The evaluation information can be represented by an identifier, a numerical value or a vector, and the representation method is not specifically limited.

[0032] Step 104: adjusting the trigger threshold of each target sub-area based on the evaluation information and the recorded first capacitance value.

[0033] In this embodiment, the electronic device can adjust the trigger threshold of each target sub-area based on the evaluation information and the recorded first capacitance value in a variety of ways, such as a statistical method or a reinforcement learning method.

[0034] In some optional implementations, the trigger threshold of each target sub-area can be adjusted by reinforcement learning. In practice, the problem discussed in reinforcement learning is how an agent can maximize the reward it can obtain in a complex and uncertain environment. During the reinforcement learning process, the agent can obtain state information in the environment and output an action based on the state information. Then, the environment will output the next state information and the reward for this action based on the current state information and the action. Reinforcement learning can learn an optimal policy that allows the agent to take action according to the current state in a specific environment to obtain the maximum reward. In an embodiment of the present application, the learned strategy is a strategy that outputs actions based on state information (i.e., actions to adjust the trigger threshold).

[0035] Specifically, the first capacitance value of each target sub-region and the comparison result with the corresponding trigger threshold value can be firstly input into the reinforcement learning model as the state information, and the evaluation information can be input into the reinforcement learning model as the reward and punishment signal, and the trigger threshold value of each target sub-region can be adjusted through the reinforcement learning model. Among them, the reinforcement learning model can adjust the calculation strategy of the trigger threshold value based on the reward and punishment signal, so as to calculate the new trigger threshold value of each target sub-region according to the new strategy, and realize the adjustment of the trigger threshold value of each target sub-region.

[0036] Among them, the above comparison result can represent whether the first capacitance value is greater than the corresponding trigger threshold. The above comparison result can be expressed in the form of an identifier. For example, for a certain target sub-area, if the first capacitance value of the target sub-area is greater than the corresponding trigger threshold, its comparison result can be marked as "1"; conversely, if the first capacitance value of the target sub-area is less than or equal to the corresponding trigger threshold, its comparison result can be marked as "0".

[0037] Through reinforcement learning, the trigger threshold of each target sub-area can be dynamically adjusted according to the actual wake-up situation, thereby eliminating the influence of factors such as the module's process, environment, and finger state differences, improving the accuracy of fingerprint wake-up and reducing the power consumption of electronic equipment.

[0038] In some optional implementations, the trigger threshold of each target sub-area can be adjusted based on a statistical method. Specifically, if the evaluation information indicates that the wake-up is accurate, the average capacitance value of each target sub-area can be determined based on the first capacitance value obtained and the historical capacitance value of each target sub-area when the wake-up is accurate in the historical period. Then, based on the average capacitance value of each target sub-area, the trigger threshold of each target sub-area is adjusted. For example, for each target sub-area, the trigger threshold of the target sub-area can be adjusted to a value less than the average capacitance value of the target sub-area. For another example, for each target sub-area, if the average capacitance value of the target sub-area is greater than the trigger threshold of the target sub-area, the trigger threshold of the target sub-area can be increased; if the average capacitance value of the target sub-area is less than the trigger threshold of the target sub-area, the trigger threshold of the target sub-area can be lowered. Each time the wake-up is accurate, the average capacitance value of each target sub-area can be updated based on the first capacitance value when the wake-up is accurate, and then the trigger threshold is updated based on the average capacitance value. As a result, the trigger threshold can be dynamically adjusted according to the actual wake-up situation, eliminating the influence of factors such as module process, environment and finger state differences, improving the accuracy of fingerprint wake-up and reducing the power consumption of electronic equipment.

[0039] The method provided by the above-mentioned embodiment of the present application is to obtain the capacitance value of each target sub-area in the fingerprint detection area in real time; then when the number of target sub-areas whose capacitance value is greater than the corresponding trigger threshold is greater than the target value, wake up the system, record the first capacitance value of each current target sub-area, and obtain the fingerprint image of the fingerprint detection area; then generate evaluation information for evaluating whether the wake-up is accurate based on the fingerprint image; finally, adjust the trigger threshold of each target sub-area based on the evaluation information and the recorded first capacitance value. Thus, after each system wake-up, the trigger threshold of each target sub-area can be dynamically adjusted based on the evaluation information that characterizes whether the wake-up is accurate and the capacitance value of each target sub-area when the system is woken up, thereby eliminating the influence of factors such as differences in the process, environment, and finger state of the module, improving the accuracy of fingerprint wake-up, and reducing the power consumption of electronic equipment.

[0040] In some optional embodiments, the fingerprint detection area may include multiple trigger areas. Each trigger area may include a target sub-area. Each target sub-area may include multiple pixels. Therefore, the above execution can obtain the capacitance value of each pixel in each target sub-area in the fingerprint detection area in real time; then, for each target sub-area, the sum of the capacitance values ​​of each pixel in the target sub-area is used as the capacitance value of the target sub-area.

[0041] As an example, see Figure 2Schematic diagram of the fingerprint detection area shown. In this example, the fingerprint detection area is a rectangular pixel block with a size of 30×300. When selecting the trigger area, 6 horizontal pixel lines with a size of 30×1 can be evenly selected in the rectangular pixel block first. Then, the midpoint of each horizontal pixel line can be used as a dividing point to divide each horizontal pixel line into 2 horizontal pixel lines, forming 12 horizontal pixel lines with a size of 15×1. Each 15×1 horizontal pixel line can be used as a trigger area. The sub-area composed of several pixel points (such as 5 pixel points) close to the middle of the fingerprint detection area in each 15×1 trigger area can be used as a target sub-area. At this time, the size of the target sub-area is 5×1. For each target sub-area, the sum of the capacitance values ​​of each pixel point in the target sub-area can be used as the capacitance value of the target sub-area.

[0042] It is understandable that the larger the target sub-area, the greater the power consumption of the fingerprint sensor, resulting in greater power consumption of the entire device. In the above implementation, the target sub-area only occupies a portion of the trigger area, and the trigger area only occupies a portion of the fingerprint detection area, so the power consumption of the fingerprint sensor during detection can be greatly reduced, thereby reducing the power consumption of the entire electronic device.

[0043] In some optional embodiments, evaluation information can be generated by performing fingerprint recognition on the fingerprint image. Specifically, the above-mentioned electronic device can store a pre-trained fingerprint recognition model. The fingerprint recognition model can be pre-trained using a machine learning algorithm (such as a supervised learning method) and can be used for fingerprint recognition. The above-mentioned electronic device can input the acquired fingerprint image into the fingerprint recognition model to obtain a fingerprint recognition result. The fingerprint recognition result can indicate whether the fingerprint recognition is passed. If the fingerprint recognition result indicates that the recognition is passed, a first evaluation information indicating that the wake-up is accurate can be generated. If the fingerprint recognition result indicates that the recognition is not passed, a second evaluation information indicating that the wake-up is inaccurate can be generated. It should be noted that when the trigger threshold of each target sub-area is adjusted by reinforcement learning, if the fingerprint recognition result indicates that the recognition is passed, the first evaluation information can be input into the reinforcement learning model as a reward signal. If the fingerprint recognition result indicates that the recognition is not passed, the second evaluation information can be input into the reinforcement learning model as a penalty signal.

[0044] In some optional embodiments, before the acquired fingerprint image is input into the fingerprint recognition model for fingerprint recognition, the fingerprint image may be tested for validity to determine whether the fingerprint image is valid. For example, the validity of the fingerprint image may be determined based on the effective area ratio by detecting the effective area ratio in the fingerprint image. The effective area is the area containing the fingerprint pattern. The effective area ratio is the ratio of the effective area to the total area of ​​the fingerprint image. The effective area ratio may be determined with the help of a pre-trained effective area ratio detection model. The effective area ratio detection model may also be pre-trained using a machine learning algorithm (such as a supervised learning method). The electronic device may input the fingerprint image into the effective area ratio detection model to obtain the effective area ratio output by the model. After the validity test, if the fingerprint image is valid, fingerprint recognition may be further performed. If the fingerprint image is detected to be invalid, the second evaluation information may be directly generated without further fingerprint recognition, thereby reducing the amount of data calculation, improving processing efficiency, and saving energy consumption of the electronic device.

[0045] In some optional embodiments, the electronic device may further adjust the trigger threshold in combination with the second capacitance value in the finger lift state. Specifically, after acquiring the fingerprint image of the fingerprint detection area, the capacitance value of each target sub-area may be reacquired, and the number of target sub-areas whose capacitance value is greater than the corresponding trigger threshold may be determined. This operation may be repeated multiple times until the determined number drops below the target value. If the number of target sub-areas whose capacitance value is greater than the corresponding trigger threshold drops below the target value, it means that the user's finger is lifted, and the second capacitance value of each current target sub-area may be recorded at this time. Since the acquisition time of the second capacitance value is different from that of the first capacitance value, the second capacitance value is different from the first capacitance value recorded in step 102. When adjusting the trigger threshold, the trigger threshold of each target sub-area may be adjusted based on the evaluation information, the recorded first capacitance value and the second capacitance value. Here, the method of adjusting the capacitance value may also adopt a reinforcement learning method or a statistical method.

[0046] Taking the reinforcement learning method as an example, the first capacitance value, the second capacitance value and the comparison result with the corresponding trigger threshold of each target sub-region can be firstly input into the reinforcement learning model as the state information, and the evaluation information can be input into the reinforcement learning model as the reward and punishment signal, and the trigger threshold of each target sub-region can be adjusted through the reinforcement learning model. Among them, the reinforcement learning model can adjust the calculation strategy of the trigger threshold based on the reward value, so as to calculate the new trigger threshold of each target sub-region according to the new strategy, and realize the adjustment of the trigger threshold of each target sub-region.

[0047] Taking the adjustment method based on statistics as an example, if the evaluation information indicates that the wake-up is accurate, the average first capacitance value of each target sub-area can be determined based on the first capacitance value obtained and the historical first capacitance value (corresponding to the finger pressing state) of each target sub-area when the wake-up is accurate in the historical period. Then, based on the second capacitance value obtained and the historical second capacitance value (corresponding to the finger lifting state) of each target sub-area when the wake-up is accurate in the historical period, the average second capacitance value of each target sub-area is determined. Then, based on the average first capacitance value and the average historical second capacitance value of each target sub-area, the trigger threshold of each target sub-area is adjusted. For example, for each target sub-area, the trigger threshold of the target sub-area can be adjusted to a value less than the average first capacitance value of the target sub-area and greater than the average second capacitance value of the target sub-area. In addition, each time the wake-up is accurate, the average first capacitance value and the second capacitance value of each target sub-area can be updated based on the first capacitance value and the second capacitance value when the wake-up is accurate, and then the trigger threshold is updated based on the average first capacitance value and the average second capacitance value. Thus, dynamic adjustment of the trigger threshold can also be achieved.

[0048] By adjusting the trigger threshold in combination with the second capacitance value of each target sub-area when the finger is lifted, a relatively reliable reference data can be provided for the dynamic adjustment of the trigger threshold, thereby further improving the accuracy of the adjusted trigger threshold.

[0049] Further references Figure 3 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a fingerprint wake-up device. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0050] like Figure 3 As shown, the fingerprint wake-up device 300 of this embodiment includes: an acquisition unit 301, which is used to acquire the capacitance value of each target sub-area in the fingerprint detection area in real time, and determine the number of target sub-areas whose capacitance values ​​are greater than the corresponding trigger threshold, and each target sub-area has a corresponding trigger threshold; a wake-up unit 302, which is used to wake up the system when the above number is greater than the target value, record the first capacitance value of each current target sub-area, and obtain the fingerprint image of the above fingerprint detection area; a generation unit 303, which is used to generate evaluation information for evaluating whether the wake-up is accurate based on the above fingerprint image; an adjustment unit 304, which is used to adjust the trigger threshold of each target sub-area based on the above evaluation information and the recorded first capacitance value.

[0051] In some optional implementations of the present embodiment, the adjustment unit 304 is further used to: input the first capacitance value of each target sub-area and the comparison result with the corresponding trigger threshold as state information into the reinforcement learning model, input the evaluation information as a reward and punishment signal into the reinforcement learning model, and adjust the trigger threshold of each target sub-area through the reinforcement learning model.

[0052] In some optional implementations of this embodiment, the above-mentioned adjustment unit 304 is further used to: if the above-mentioned evaluation information indicates that the wake-up is accurate, determine the average capacitance value of each target sub-area based on the acquired first capacitance value and the historical capacitance value of each target sub-area when the wake-up is accurate in the historical period; based on the average capacitance value of each target sub-area, adjust the trigger threshold of each target sub-area.

[0053] In some optional implementations of this embodiment, the above-mentioned device also includes: a recording unit, used to record the second capacitance value of each current target sub-area when the above-mentioned number decreases below the target value; and the above-mentioned adjustment unit 304 is further used to: adjust the trigger threshold of each target sub-area based on the above-mentioned evaluation information, the recorded first capacitance value and the second capacitance value.

[0054] In some optional implementations of this embodiment, the above-mentioned generation unit 303 is further used to: input the above-mentioned fingerprint image into a trained fingerprint recognition model to obtain a fingerprint recognition result; if the above-mentioned fingerprint recognition result indicates that the recognition is passed, then generate first evaluation information for indicating that the wake-up is accurate; if the above-mentioned fingerprint recognition result indicates that the recognition is not passed, then generate second evaluation information for indicating that the wake-up is inaccurate.

[0055] In some optional implementations of this embodiment, the above-mentioned generation unit 303 is further used to: detect the effective area ratio in the above-mentioned fingerprint image, and determine the validity of the above-mentioned fingerprint image based on the above-mentioned effective area ratio; if the above-mentioned fingerprint image is invalid, generate second evaluation information for indicating inaccurate wake-up.

[0056] In some optional implementations of the present embodiment, the fingerprint detection area includes multiple trigger areas, each trigger area includes a target sub-area, and each target sub-area includes multiple pixels; the acquisition unit 301 is further used to acquire the capacitance value of each pixel in each target sub-area in the fingerprint detection area in real time; for each target sub-area, the sum of the capacitance values ​​of each pixel in the target sub-area is used as the capacitance value of the target sub-area.

[0057] The device provided by the above-mentioned embodiment of the present application obtains the capacitance value of each target sub-area in the fingerprint detection area in real time; then when the number of target sub-areas whose capacitance value is greater than the corresponding trigger threshold is greater than the target value, the system is awakened, the first capacitance value of each current target sub-area is recorded, and the fingerprint image of the fingerprint detection area is obtained; then, based on the fingerprint image, evaluation information for evaluating whether the awakening is accurate is generated; finally, based on the evaluation information and the recorded first capacitance value, the trigger threshold of each target sub-area is adjusted. Thus, after each system awakening, the trigger threshold of each target sub-area can be dynamically adjusted based on the evaluation information representing whether the awakening is accurate and the capacitance value of each target sub-area when the system is awakened, thereby eliminating the influence of factors such as differences in the process, environment and finger state of the module, improving the accuracy of fingerprint awakening, and reducing the power consumption of electronic equipment.

[0058] Reference below Figure 4 , which shows a schematic structural diagram of an electronic device for implementing some embodiments of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0059] like Figure 4 As shown, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0060] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a disk, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 4 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0061] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of some embodiments of the present application are executed.

[0062] It should be noted that the computer-readable medium described in some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0063] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0064] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the capacitance value of each target sub-area in the fingerprint detection area in real time, and determines the number of target sub-areas whose capacitance value is greater than the corresponding trigger threshold, and each target sub-area has a corresponding trigger threshold; when the number is greater than the target value, wakes up the system, records the first capacitance value of each current target sub-area, and obtains the fingerprint image of the fingerprint detection area; based on the fingerprint image, generates evaluation information for evaluating whether the wake-up is accurate; based on the evaluation information and the recorded first capacitance value, adjusts the trigger threshold of each target sub-area.

[0065] Computer program code for performing the operations of some embodiments of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++; and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, or may be connected to an external computer (e.g., through the Internet using an Internet service provider), including a local area network (LAN) or a wide area network (WAN).

[0066] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0067] The units described in some embodiments of the present application may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes a first determination unit, a second determination unit, a selection unit, and a third determination unit. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0068] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0069] The above description is only some preferred embodiments of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with (but not limited to) the technical features with similar functions disclosed in the embodiments of the present application to form a technical solution.

Claims

1. A fingerprint wake-up method, characterized in that: The method comprises: Obtaining the capacitance value of each target sub-region in the fingerprint detection region, and determining the number of target sub-regions whose capacitance values ​​are greater than a corresponding trigger threshold, each target sub-region having a corresponding trigger threshold; When the number is greater than the target value, waking up the system, recording the first capacitance value of each current target sub-area, and acquiring the fingerprint image of the fingerprint detection area; Based on the fingerprint image, generating evaluation information for evaluating whether the awakening is accurate; Based on the evaluation information and the recorded first capacitance value, a trigger threshold of each target sub-area is adjusted.

2. The method according to claim 1, characterized in that The adjusting the trigger threshold of each target sub-area based on the evaluation information and the recorded first capacitance value includes: The first capacitance value of each target sub-area and the comparison result with the corresponding trigger threshold are input into the reinforcement learning model as state information, and the evaluation information is input into the reinforcement learning model as a reward and punishment signal, and the trigger threshold of each target sub-area is adjusted through the reinforcement learning model.

3. The method according to claim 1, characterized in that The adjusting the trigger threshold of each target sub-area based on the evaluation information and the recorded first capacitance value includes: If the evaluation information indicates that the awakening is accurate, determining an average capacitance value of each target sub-region based on the acquired first capacitance value and a historical capacitance value of each target sub-region when the awakening is accurate in a historical period; Based on the average capacitance value of each target sub-region, the trigger threshold of each target sub-region is adjusted.

4. The method according to any one of claims 1 to 3, characterized in that: After acquiring the fingerprint image of the fingerprint detection area, the method further includes: When the number decreases below the target value, recording the second capacitance value of each current target sub-area; And, adjusting the trigger threshold of each target sub-area based on the evaluation information and the recorded first capacitance value includes: Based on the evaluation information, the recorded first capacitance value and the second capacitance value, a trigger threshold of each target sub-area is adjusted.

5. The method according to any one of claims 1 to 3, characterized in that: The generating, based on the fingerprint image, evaluation information for evaluating whether the awakening is accurate includes: Inputting the fingerprint image into a trained fingerprint recognition model to obtain a fingerprint recognition result; If the fingerprint recognition result indicates that the recognition is successful, generating first evaluation information indicating that the wake-up is accurate; If the fingerprint recognition result indicates that the recognition fails, second evaluation information indicating that the wake-up is inaccurate is generated.

6. The method according to claim 5, characterized in that Before inputting the fingerprint image into the trained fingerprint recognition model, generating evaluation information for evaluating whether the awakening is accurate based on the fingerprint image further includes: Detecting a valid area ratio in the fingerprint image, and determining the validity of the fingerprint image based on the valid area ratio; If the fingerprint image is invalid, second evaluation information indicating that the wake-up is inaccurate is generated.

7. The method according to any one of claims 1 to 3, characterized in that: The fingerprint detection area includes a plurality of trigger areas, each of which includes a target sub-area, and each target sub-area includes a plurality of pixel points; The obtaining of the capacitance value of each target sub-area in the fingerprint detection area includes: Acquire the capacitance value of each pixel in each target sub-area of ​​the fingerprint detection area in real time; For each target sub-region, the sum of the capacitance values ​​of the pixels in the target sub-region is used as the capacitance value of the target sub-region.

8. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images