Fingerprint identification processing method and device, electronic equipment and storage medium

Abstract

According to the fingerprint identification processing method and device, the electronic equipment and the storage medium provided by the embodiment of the invention, the method comprises the steps of firstly obtaining the target capacitance value information of the fingerprint identification area in the equipment, inputting the target capacitance value information into the pre-trained anomaly identification model to obtain the type of the target abnormal object located in the fingerprint identification area; then, according to the target abnormal object type, target display information is determined in a preset mapping relation, at least one abnormal object type and display information corresponding to each abnormal object type are recorded in the mapping relation, and finally, the target display information is displayed in the fingerprint recognition area. According to the technical scheme, the abnormal object type of the fingerprint identification area is judged by obtaining the capacitance value information in combination with the abnormal identification model, the abnormal object prompt is displayed in the fingerprint identification area, a user is reminded that abnormal objects exist on the surface of the fingerprint identification area, the user can clean the abnormal objects in time, subsequent use of the fingerprint identification area is facilitated, the fingerprint unlocking rate is increased, and the user experience is improved. And the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of capacitive sensing technology, and in particular to a fingerprint recognition processing method, device, electronic device and storage medium. Background Art

[0002] As a biometric technology, fingerprint module has the characteristics of high security and convenience, and is widely used in many fields. Among them, in electronic devices such as smartphones and tablets, the fingerprint recognition area in the fingerprint module is used to provide a convenient, fast and safe unlocking method, and users can quickly unlock electronic devices with fingerprints.

[0003] In the prior art, the design of the fingerprint module light is relatively simple, the function is relatively single, it is just to light up or turn off the light, and the light color is single, lacking flexibility and interactivity. When there is an abnormality on the surface of the fingerprint recognition area in the fingerprint module, it cannot effectively remind the user.

[0004] Therefore, how to efficiently remind users of surface abnormalities in the fingerprint recognition area has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a fingerprint recognition processing method, device, electronic device and storage medium to solve the problems in the prior art regarding how to efficiently remind users of surface abnormalities in a fingerprint recognition area.

[0006] In a first aspect, an embodiment of the present application provides a fingerprint recognition processing method, including:

[0007] Obtain target capacity information of the fingerprint recognition area in the device;

[0008] Inputting the target capacitance value information into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area, wherein the abnormality recognition model is trained based on multiple capacitance value information and abnormal object types corresponding to different capacitance value information;

[0009] According to the target abnormal object type, the target display information is determined in a preset mapping relationship, wherein the mapping relationship records at least one abnormal object type and display information corresponding to each abnormal object type;

[0010] The target display information is displayed in the fingerprint recognition area.

[0011] In one or more embodiments, the step of inputting the target capacitance information into a pre-trained anomaly recognition model to obtain the target anomaly type located in the fingerprint recognition area includes:

[0012] Determine whether the fingerprint recognition area has an abnormality according to the target capacitance information and the initial capacitance information of the fingerprint recognition area, wherein the initial capacitance information is the capacitance information of the fingerprint recognition area when there is no abnormality;

[0013] If an abnormality exists, the target capacitance value information is input into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area.

[0014] In one or more embodiments, the mapping relationship includes at least one of the following:

[0015] If the abnormal object type is water droplets or water mist, the displayed information is the first color or the first stroboscopic information;

[0016] If the abnormal object type is a dirty type, the display information is a second color or a second stroboscopic information;

[0017] If the abnormal object type is a foreign object type, the display information is a third color or a third stroboscopic information.

[0018] In one or more embodiments, before inputting the target capacitance information into a pre-trained abnormality recognition model to obtain the target abnormality type located in the fingerprint recognition area, the method further includes:

[0019] Acquire multiple capacitance value information and abnormal object types corresponding to different capacitance value information;

[0020] According to the multiple capacitance value information and the types of abnormal objects corresponding to different capacitance value information, a preset algorithm model is trained to obtain the abnormality recognition model.

[0021] In one or more embodiments, further comprising:

[0022] In response to an adjustment operation by a user, adjusting at least one abnormal object type in the mapping relationship and display information corresponding to the at least one abnormal object type;

[0023] In one or more embodiments, displaying the target display information in the fingerprint recognition area includes:

[0024] The target display information is sent to a controller corresponding to the fingerprint module in the fingerprint recognition area, so that the controller controls the operation of the display module in the fingerprint module according to the target display information.

[0025] In one or more embodiments, obtaining target capacity information of a fingerprint recognition area in a device includes:

[0026] The target capacitance value information is obtained by the sensor of the fingerprint module in the fingerprint recognition area.

[0027] In a second aspect, an embodiment of the present application provides a fingerprint recognition processing device, including:

[0028] An acquisition module, used to acquire target capacity value information of a fingerprint recognition area in a device;

[0029] A processing module, used for inputting the target capacitance value information into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area, wherein the abnormality recognition model is trained based on multiple capacitance value information and abnormal object types corresponding to different capacitance value information;

[0030] A determination module, configured to determine the target display information in a preset mapping relationship according to the target abnormal object type, wherein the mapping relationship records at least one abnormal object type and display information corresponding to each abnormal object type;

[0031] A display module is used to display the target display information in the fingerprint recognition area.

[0032] In one or more embodiments, the processing module is specifically used to:

[0033] Determine whether the fingerprint recognition area has an abnormality according to the target capacitance information and the initial capacitance information of the fingerprint recognition area, wherein the initial capacitance information is the capacitance information of the fingerprint recognition area when there is no abnormality;

[0034] If an abnormality exists, the target capacitance value information is input into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area.

[0035] In one or more embodiments, the mapping relationship includes at least one of the following:

[0036] If the abnormal object type is water droplets or water mist, the displayed information is the first color or the first stroboscopic information;

[0037] If the abnormal object type is a dirty type, the display information is a second color or a second stroboscopic information;

[0038] If the abnormal object type is a foreign object type, the display information is a third color or a third stroboscopic information.

[0039] In one or more embodiments, before inputting the target capacitance value information into a pre-trained anomaly recognition model to obtain the target anomaly type located in the fingerprint recognition area, the processing module is further configured to:

[0040] Acquire multiple capacitance value information and abnormal object types corresponding to different capacitance value information;

[0041] According to the multiple capacitance value information and the types of abnormal objects corresponding to different capacitance value information, a preset algorithm model is trained to obtain the abnormality recognition model.

[0042] In one or more embodiments, the determining module is further configured to:

[0043] In response to an adjustment operation by a user, adjusting at least one abnormal object type in the mapping relationship and display information corresponding to the at least one abnormal object type;

[0044] In response to a confirmation operation by the user, the adjusted mapping relationship is saved.

[0045] In one or more embodiments, the display module is specifically used for:

[0046] The target display information is sent to a controller corresponding to the fingerprint module in the fingerprint recognition area, so that the controller controls the operation of the display module in the fingerprint module according to the target display information.

[0047] In one or more embodiments, the acquisition module is specifically used to:

[0048] The target capacitance value information is obtained by the sensor of the fingerprint module in the fingerprint recognition area.

[0049] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0050] The memory stores computer-executable instructions;

[0051] The processor executes the computer-executable instructions stored in the memory to implement the fingerprint recognition processing method as described in the first aspect and any one of the above-mentioned methods.

[0052] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the processing method for fingerprint recognition described in the first aspect and any one of the above methods is implemented.

[0053] In a fifth aspect, an embodiment of the present application provides a computer program, wherein the computer program product includes a computer program, wherein the computer program is stored in a computer-readable storage medium, and at least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, the fingerprint recognition processing method described in the first aspect and any one of the above methods can be implemented.

[0054] The fingerprint recognition processing method, device, electronic device and storage medium provided in the embodiment of the present application first obtain the target capacity information of the fingerprint recognition area in the device, input the target capacity information into the pre-trained abnormal recognition model, obtain the target abnormal object type located in the fingerprint recognition area, and then determine the target display information in the preset mapping relationship according to the target abnormal object type. The mapping relationship records at least one abnormal object type and the display information corresponding to each abnormal object type. Finally, the target display information is displayed in the fingerprint recognition area. The technical solution determines the abnormal object type of the fingerprint recognition area by obtaining the capacity information and combining the abnormal recognition model, and displays the abnormal object prompt in the fingerprint recognition area, which can remind the user that there is an abnormal object on the surface of the fingerprint recognition area. The user can clean it up in time to facilitate the subsequent use of the fingerprint recognition area, improve the fingerprint unlocking rate, and enhance the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0056] Figure 1 A schematic diagram of the architecture of a fingerprint recognition processing method provided in an embodiment of the present application;

[0057] Figure 2 Schematic diagram of the fingerprint recognition processing method provided in the embodiment of the present application Figure 1 ;

[0058] Figure 3 Schematic diagram of the fingerprint recognition processing method provided in the embodiment of the present application Figure 2 ;

[0059] Figure 4 A schematic diagram of capacitance information when there is no foreign matter in the fingerprint recognition area provided by an embodiment of the present application;

[0060] Figure 5 A schematic diagram of capacitance information when there are water drops in the fingerprint recognition area provided by an embodiment of the present application;

[0061] Figure 6 Schematic diagram of the fingerprint recognition processing method provided in the embodiment of the present application Figure 3 ;

[0062] Figure 7 A schematic diagram of the structure of a fingerprint recognition processing device provided in an embodiment of the present application;

[0063] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0064] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] Before introducing the embodiments of the present application, the application background of the embodiments of the present application is first explained:

[0067] As a biometric technology, fingerprint module performs identity authentication by scanning and identifying the user's fingerprint. It has the characteristics of high security and convenience, and is widely used in many fields. Among them, in electronic devices such as smartphones and tablets, the fingerprint recognition area in the fingerprint module is used to provide a convenient, fast and safe unlocking method. Users can quickly unlock electronic devices with fingerprints.

[0068] In the prior art, the design of the fingerprint module light is relatively simple, and the function is relatively single, which is to simply turn the light on or off, and the light color is single, lacking flexibility and interactivity. When there is an abnormality on the surface of the fingerprint recognition area in the fingerprint module, the electronic device cannot effectively remind the user, which may lead to fingerprint recognition failure or cause certain safety hazards.

[0069] Therefore, how to efficiently remind users of surface abnormalities in the fingerprint recognition area has become a technical problem that needs to be solved urgently.

[0070] In view of the technical problems existing in the prior art, the inventor of the present application has the following idea: in the prior art, the fingerprint module light function is single, and when there is an abnormality (such as water droplets, dirt, foreign matter, etc.) on the surface of the fingerprint recognition area in the fingerprint module, the user cannot be reminded of the type of abnormal object, which affects the user experience. The above problem can be solved by setting multiple colors or strobing times of the display light corresponding to different types of abnormal objects. Since the capacitance information of different substances when attached to the fingerprint recognition area is different, the algorithm model can be trained using different types of abnormal objects and corresponding capacitance information to obtain an abnormal object recognition model. The capacitance information generated by the current abnormal object attached to the fingerprint recognition area is input into the abnormal object recognition model to directly determine the current abnormal object type, and then according to the mapping relationship between the abnormal object type and the display information, the display information corresponding to the current abnormal object type is output, wherein the display information can be the light color or strobing times of the fingerprint module display light, and finally the display information is displayed by the display light. This method can efficiently remind the user of the current abnormal object type in the fingerprint recognition area, which greatly improves the user experience.

[0071] Based on the above technical concept, firstly, the hardware architecture involved in the embodiment of the present application is described. Figure 1 A schematic diagram of the structure of the fingerprint recognition processing method provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, the architecture includes: a sensor, a microcontroller unit (MCU), a driver, a controller, and a display light.

[0072] In a possible implementation, the MCU obtains the target capacitance value information collected by the sensor through a synchronous serial communication protocol (Serial Peripheral Interface, SPI) interface, and then inputs the target capacitance value information into the abnormal object recognition model in the MCU. The pre-trained algorithm model configured in the MCU can determine the target display information corresponding to the current abnormal object type, and then the MCU transmits the target display information to the controller through the driver, and the controller controls the display light to display the target display information through the general purpose input / output (General Purpose Input / Output, GPIO) interface.

[0073] It is worth noting that the communication method between the controller and the MCU can be Universal Serial Bus (USB) or SPI. The MCU can convert the SPI signal into a USB signal. The driver and the controller can both receive SPI signals and USB signals.

[0074] The above principle is only an example. The detailed technical solution involved in the embodiment of the present application is described below:

[0075] Figure 2 Schematic diagram of the fingerprint recognition processing method provided in the embodiment of the present application Figure 1 ,like Figure 2 As shown, the method may include the following steps:

[0076] Step 11: Obtain target capacity information of the fingerprint recognition area in the device.

[0077] In this step, since the capacitance information of different substances (such as water droplets, dirt, etc.) in the fingerprint recognition area is different, in order to be able to identify the type of abnormal objects on the surface of the fingerprint recognition area, the capacitance information of the fingerprint recognition area can be obtained first and recorded as the target capacitance information.

[0078] In a possible implementation, the device may be a device such as a smart phone, a tablet computer, etc. that applies a fingerprint recognition function. The fingerprint recognition area is an area in the device that is used to verify the fingerprint.

[0079] Optionally, step 11 may be implemented in the following manner: obtaining target capacitance value information through a sensor of a fingerprint module in the fingerprint recognition area.

[0080] In this implementation, the sensor in the fingerprint module collects and obtains the target capacitance value information of the fingerprint recognition area in the device in real time.

[0081] The sensor in the fingerprint module can be an optical sensor, a capacitive sensor, an ultrasonic sensor, or a thermal sensor. The sensor in the fingerprint module can collect capacitance information of the fingerprint recognition area in real time through light, electricity, or sound waves.

[0082] Step 12: Input the target capacitance value information into the pre-trained anomaly recognition model to obtain the target anomaly type located in the fingerprint recognition area.

[0083] Among them, the anomaly recognition model is obtained by training based on multiple capacitance value information and the types of anomalies corresponding to different capacitance value information.

[0084] In this step, the target capacity information obtained above is input into a pre-trained anomaly recognition model, and the anomaly recognition model automatically recognizes the target capacity information to obtain the type of anomaly corresponding to the target capacity information, and then uses the anomaly type as the target anomaly type of the fingerprint recognition area.

[0085] In a possible implementation, the MCU obtains the target capacitance information collected by the sensor through the SPI interface, and then inputs the target capacitance information into the abnormal object recognition model in the MCU. The abnormal object recognition model can determine that the abnormal object type corresponding to the capacitance information of the current abnormal object is water droplets based on the pre-trained capacitance information characteristics corresponding to different abnormal objects. That is, it can be obtained that the current abnormal object type in the fingerprint recognition area is water droplets.

[0086] Step 13: According to the target abnormal object type, the target display information is determined in the preset mapping relationship.

[0087] The mapping relationship records at least one abnormal object type and display information corresponding to each abnormal object type.

[0088] In this step, according to the target abnormal object type obtained above, an abnormal object type consistent with the target abnormal object type is found in the preset mapping relationship, and the display information corresponding to the abnormal object type is used as the target display information corresponding to the target abnormal object type.

[0089] Specifically, the mapping relationship includes at least one of the following:

[0090] 1. If the abnormal object type is water droplets or water mist, the displayed information is the first color or the first stroboscopic information;

[0091] 2. If the abnormal object type is dirty, the displayed information will be the second color or the second strobe information;

[0092] 3. If the abnormal object type is a foreign object type, the displayed information will be the third color or the third strobe information.

[0093] In a possible implementation, the first color may be green, the second color may be yellow, the third color may be purple, the first strobe information may be that the light of the fingerprint module flashes twice continuously, the second strobe information may be that the light of the fingerprint module flashes three times continuously, and the third strobe information may be that the light of the fingerprint module flashes four times continuously.

[0094] In a possible implementation, the abnormality recognition model determines that the current abnormal object type in the fingerprint recognition area is water droplets, and the MCU searches the mapping relationship to find that the display information corresponding to the abnormal object type of water droplets is the first color, thereby determining that the target display information is green.

[0095] Optionally, before step 13, an adjustment operation on the mapping relationship may be performed to enrich the user experience, for example:

[0096] Step 1: In response to the user's adjustment operation, at least one abnormal object type in the mapping relationship is adjusted to display information corresponding to at least one abnormal object type;

[0097] In this implementation, the user can select an abnormal object type in the device, and then select a color or stroboscopic information as the display information of the abnormal object in the device settings. The user can set the mapping relationship between the abnormal object type and the corresponding display information in the device according to his or her usage habits.

[0098] Among them, taking the application for mapping relationship management as an example, the user adjustment operation can be completed through the application. For example, the interface of the application shows "water drop or water mist type", and the corresponding display information is "green". Click the "Modify" control to replace "green" with "blue". In a possible implementation, when the abnormal object type is a water drop or water mist type, the display information can also be set to the second color or the second stroboscopic information; when the abnormal object type is a dirty type, the display information can also be set to the third color or the first stroboscopic information; when the abnormal object type is a foreign object type, the display information can also be set to the first color or the third stroboscopic information.

[0099] That is, the type of abnormal object and its corresponding display information can be adjusted according to user preferences or actual application scenarios.

[0100] Step 2: In response to the user's confirmation operation, save the adjusted mapping relationship.

[0101] In this implementation, based on the adjustment operation of the above mapping relationship, in response to the user's confirmation operation, the adjusted mapping relationship between the abnormal object type and the display information is automatically saved.

[0102] In a possible implementation, the user adjusts the mapping relationship in the interface of the application program and clicks a "confirm modification" control in the interface, and the application program automatically saves the adjusted mapping relationship.

[0103] Step 14: Display the target display information in the fingerprint recognition area.

[0104] In this step, after the target display information obtained above, the target display information corresponding to the current abnormal object type is displayed in the fingerprint recognition area.

[0105] Optionally, step 14 may be implemented as follows: sending the target display information to a controller corresponding to the fingerprint module in the fingerprint recognition area, so that the controller controls the operation of the display module in the fingerprint module according to the target display information.

[0106] In this implementation, the MCU sends the target display information corresponding to the current abnormal object type to the controller through the driver, and the controller controls the display module in the fingerprint module to display according to the target display information.

[0107] Among them, the display module in the fingerprint module can be a display light.

[0108] In a possible implementation, there are dirty abnormal objects on the surface of the fingerprint recognition area, and the MCU obtains the target capacitance information collected by the sensor through the SPI interface, and then inputs the target capacitance information into the abnormal object recognition model in the MCU. The abnormal object recognition model determines that the abnormal object type corresponding to the target capacitance information is a dirty abnormal object type, and then the MCU determines that the target display information corresponding to the dirty abnormal object type is the second color (that is, yellow) according to a preset mapping relationship, and then transmits the target display information to the controller, and the controller controls the display light to show that the target display information is yellow.

[0109] The fingerprint recognition processing method provided in the embodiment of the present application first obtains the target capacity information of the fingerprint recognition area in the device, inputs the target capacity information into the pre-trained abnormal recognition model, obtains the target abnormal object type located in the fingerprint recognition area, and then determines the target display information in the preset mapping relationship according to the target abnormal object type, and the mapping relationship records at least one abnormal object type and the display information corresponding to each abnormal object type, and finally displays the target display information in the fingerprint recognition area. In this technical solution, by obtaining the capacity information and combining the abnormal recognition model to determine the abnormal object type in the fingerprint recognition area, and displaying the abnormal object prompt information in the fingerprint recognition area, the user can be effectively reminded that there are abnormal objects on the surface of the fingerprint recognition area, and the user can clean them in time, which is convenient for the subsequent use of the fingerprint recognition area, improves the fingerprint unlocking rate, and enhances the user experience.

[0110] Based on the above embodiments, Figure 3 Schematic diagram of the fingerprint recognition processing method provided in the embodiment of the present application Figure 2 ,like Figure 3 As shown, step 12 may include the following steps:

[0111] Step 21: Determine whether there is an abnormality in the fingerprint recognition area according to the target capacitance information and the initial capacitance information of the fingerprint recognition area.

[0112] The initial capacitance information is the capacitance information of the fingerprint recognition area when there is no abnormality.

[0113] In this step, when there is no abnormal object in the fingerprint recognition area, the capacitance information of the fingerprint recognition area is obtained as the initial capacitance information, and the capacitance information of the fingerprint recognition area is obtained in real time. If the capacitance information is different from the initial capacitance information, it means that there is an abnormal object on the surface of the fingerprint recognition area.

[0114] In one possible implementation, Figure 4 A schematic diagram of capacitance information when there is no foreign matter in the fingerprint recognition area provided by the embodiment of the present application; and taking the capacitance information when the target capacitance information reflects the capacitance information when there is a water drop in the fingerprint recognition area as an example, Figure 5A schematic diagram of capacitance information when there are water drops in the fingerprint recognition area provided in the embodiment of the present application is provided to illustrate the present embodiment:

[0115] Figure 4 The left picture is the fingerprint recognition area. There is no foreign matter in the fingerprint recognition area. Figure 4 The right picture in the figure is the capacitance data information corresponding to no abnormal objects, that is, the initial capacitance information indicating the fingerprint recognition area.

[0116] Figure 5 The left picture is the fingerprint recognition area. It can be seen that there are water drops in the fingerprint recognition area. The capacitance information on the surface of the fingerprint recognition area is compared with Figure 4 The initial capacitance information in the right figure will change. Figure 5 The right picture in the figure shows the capacitance information collected by the sensor when there are water drops in the fingerprint recognition area. It is not difficult to see that there are abnormal capacitance data in the upper left corner and the right half of the area. Figure 5 Compared with the capacitance information in Figure 4 The initial capacitance value information in is abnormal capacitance value information, indicating that there are abnormal objects in the current fingerprint recognition area.

[0117] Step 22: If an anomaly exists, the target capacitance value information is input into a pre-trained anomaly recognition model to obtain the target anomaly type located in the fingerprint recognition area.

[0118] In this step, if there is an abnormality in the fingerprint recognition area, the target capacitance information obtained above is input into the pre-trained abnormality recognition model. The trained abnormality recognition model can determine the corresponding abnormal object type according to the target capacitance information, thereby obtaining the target abnormal object type located in the fingerprint recognition area.

[0119] The fingerprint recognition processing method provided in the embodiment of the present application determines whether there is an abnormality in the fingerprint recognition area based on the target capacitance information and the initial capacitance information of the fingerprint recognition area. If there is an abnormality, the target capacitance information is input into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area. In this technical solution, the target capacitance information is input into the abnormality recognition model to obtain the target abnormal object type in the fingerprint recognition area. The target abnormal object type can be efficiently obtained through the algorithm model, thereby improving the accuracy of the fingerprint recognition system.

[0120] Based on the above embodiments, Figure 6 Schematic diagram of the fingerprint recognition processing method provided in the embodiment of the present application Figure 3 ,like Figure 6 As shown, the following steps may be included before step 12:

[0121] Step 31: Acquire multiple capacitance value information and abnormal object types corresponding to different capacitance value information.

[0122] In this step, different types of abnormal objects are placed in the fingerprint recognition area, multiple capacitance value information corresponding to the current abnormal object type is obtained, and the abnormal object types corresponding to the multiple capacitance value information are recorded.

[0123] Among them, the multiple capacitance information includes initial capacitance information when there is no abnormal object in the fingerprint recognition area, capacitance information when there are water droplets in the fingerprint recognition area, capacitance information when the fingerprint recognition area is dirty, and capacitance information when there are foreign objects in the fingerprint recognition area.

[0124] In a possible implementation, sensors are used to collect 200 sets of capacitance value information of water droplet anomalies, 200 sets of capacitance value information of dirt anomalies, and 200 sets of capacitance value information of foreign matter anomalies.

[0125] Step 32: According to the plurality of capacitance value information and the abnormal object types corresponding to the different capacitance value information, a preset algorithm model is trained to obtain an abnormality recognition model.

[0126] In this step, the above-collected capacitance information and the abnormal object types corresponding to the recorded multiple capacitance information are input into the algorithm model for training, and the corresponding relationship between different capacitance information and abnormal object types can be obtained, which is convenient for the subsequent use of the algorithm model.

[0127] In a possible implementation, the algorithm model is taken as a convolutional neural network algorithm model as an example for explanation:

[0128] The above 200 sets of capacitance information of water drop anomalies, 200 sets of capacitance information of dirty anomalies, and 200 sets of capacitance information of foreign matter anomalies are divided into 160 sets of training sets and 40 sets of validation sets, and then the 160 sets of training sets are input into the preset algorithm model for pre-training. The convolutional neural network algorithm model will extract the important features of different capacitance information from the capacitance information, so that the algorithm model can better understand the data pattern, and then normalize the capacitance information to eliminate the influence of interference information training. Then the 40 sets of validation sets are input into the convolutional neural network algorithm model to verify the model performance, adjust the hyperparameters to optimize the results, and obtain a high-performance anomaly recognition model.

[0129] It should be understood that the above 200 groups are only examples. In actual training, the more training sets there are, the more accurate the abnormality recognition model obtained will be.

[0130] The fingerprint recognition processing method provided in the embodiment of the present application first obtains multiple capacitance information and the types of abnormal objects corresponding to different capacitance information, and then trains the preset algorithm model according to the types of abnormal objects corresponding to the multiple capacitance information and different capacitance information to obtain an abnormal recognition model. In this technical solution, by training the algorithm model with capacitance information and the corresponding abnormal object types, and adjusting the parameters to optimize the algorithm model, a model that can accurately identify the type of abnormal objects can be obtained, that is, the type of abnormal object corresponding to the capacitance information can be judged according to the capacitance information, thereby improving the efficiency and accuracy of abnormal object recognition and enhancing the user experience.

[0131] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0132] Figure 7 This is a schematic diagram of the structure of the fingerprint recognition processing device provided in the embodiment of the present application. Figure 7 As shown, the device comprises:

[0133] An acquisition module 71 is used to acquire target capacity value information of a fingerprint recognition area in a device;

[0134] The processing module 72 is used to input the target capacitance value information into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area, and the abnormality recognition model is trained based on multiple capacitance value information and abnormal object types corresponding to different capacitance value information;

[0135] A determination module 73, configured to determine target display information in a preset mapping relationship according to the target abnormal object type, wherein the mapping relationship records at least one abnormal object type and display information corresponding to each abnormal object type;

[0136] The display module 74 is used to display target display information in the fingerprint recognition area.

[0137] In a possible implementation, the processing module 72 is specifically configured to:

[0138] Determine whether there is an abnormality in the fingerprint recognition area according to the target capacitance information and the initial capacitance information of the fingerprint recognition area, where the initial capacitance information is the capacitance information of the fingerprint recognition area when there is no abnormality;

[0139] If an anomaly exists, the target capacity information is input into the pre-trained anomaly recognition model to obtain the target anomaly type located in the fingerprint recognition area.

[0140] In a possible implementation, the mapping relationship includes at least one of the following:

[0141] If the abnormal object type is water droplets or water mist, the displayed information is the first color or the first stroboscopic information;

[0142] If the abnormal object type is a dirty type, the displayed information is a second color or a second strobe information;

[0143] If the abnormal object type is a foreign object type, the displayed information will be a third color or a third strobe information.

[0144] In a possible implementation, before inputting the target capacitance information into the pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area, the processing module 72 is further used to:

[0145] Acquire multiple capacitance value information and abnormal object types corresponding to different capacitance value information;

[0146] According to the multiple capacitance value information and the abnormal object types corresponding to the different capacitance value information, the preset algorithm model is trained to obtain the abnormality recognition model.

[0147] In a possible implementation manner, the determination module 73 is further configured to:

[0148] In response to the user's adjustment operation, adjusting at least one abnormal object type in the mapping relationship and display information corresponding to at least one abnormal object type;

[0149] In response to a confirmation operation by the user, the adjusted mapping relationship is saved.

[0150] In a possible implementation, the display module 74 is specifically configured to:

[0151] The target display information is sent to the controller corresponding to the fingerprint module in the fingerprint recognition area, so that the controller controls the operation of the display module in the fingerprint module according to the target display information.

[0152] In a possible implementation, the acquisition module 71 is specifically configured to:

[0153] The target capacitance value information is obtained through the sensor of the fingerprint module in the fingerprint recognition area.

[0154] The fingerprint identification processing device provided in the embodiments of the present application can be used to execute the determination method in any of the above embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0155] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0156] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 8 As shown, the electronic device may include: a processor 81, a memory 82, and computer program instructions stored in the memory 82 and executable on the processor 81. When the processor 81 executes the computer program instructions, the method provided in any of the aforementioned embodiments is implemented.

[0157] Optionally, the above-mentioned components of the electronic device may be connected via a system bus.

[0158] The memory 82 may be a separate storage unit or a storage unit integrated in the processor 81. The number of the processor 81 may be one or more.

[0159] It should be understood that the processor 81 can be a central processing unit (CPU), or other general-purpose processors 81, digital signal processors 81 (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor 81 can be a microprocessor 81 or the processor 81 can also be any conventional processor 81, etc. The steps of the method disclosed in the present application can be directly embodied as being executed by the hardware processor 81, or can be executed by a combination of hardware and software modules in the processor 81.

[0160] The system bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The memory 82 may include a random access memory 82 (Random Access Memory, RAM), and may also include a non-volatile memory 82 (Non-Volatile Memory, NVM), such as at least one disk memory 82.

[0161] All or part of the steps of the above-mentioned method embodiments can be completed by hardware related to program instructions. The above-mentioned program can be stored in a readable memory 82. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the above-mentioned memory 82 (storage medium) includes: read-only memory 82 (Read-Only Memory, ROM), RAM, flash memory 82, hard disk, solid state hard disk, magnetic tape (English: magnetic tape), floppy disk (English: floppy disk), optical disc (English: optical disc) and any combination thereof.

[0162] The electronic device provided in the embodiment of the present application can be used to execute the method provided in any of the above method embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0163] An embodiment of the present application provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed on a computer, the computer executes the above method.

[0164] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0165] Optionally, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0166] An embodiment of the present application also provides a computer program product, which includes a computer program. The computer program is stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, the above method can be implemented.

[0167] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A fingerprint recognition processing method, characterized in that: The method comprises: Obtain target capacity information of the fingerprint recognition area in the device; Inputting the target capacitance value information into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area, wherein the abnormality recognition model is trained based on multiple capacitance value information and abnormal object types corresponding to different capacitance value information; According to the target abnormal object type, the target display information is determined in a preset mapping relationship, wherein the mapping relationship records at least one abnormal object type and display information corresponding to each abnormal object type; The target display information is displayed in the fingerprint recognition area.

2. The method according to claim 1, characterized in that The step of inputting the target capacitance value information into a pre-trained abnormality recognition model to obtain the target abnormality type located in the fingerprint recognition area includes: Determine whether the fingerprint recognition area has an abnormality according to the target capacitance information and the initial capacitance information of the fingerprint recognition area, wherein the initial capacitance information is the capacitance information of the fingerprint recognition area when there is no abnormality; If an abnormality exists, the target capacitance value information is input into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area.

3. The method according to claim 1 or 2, characterized in that: The mapping relationship includes at least one of the following: If the abnormal object type is water droplets or water mist, the displayed information is the first color or the first stroboscopic information; If the abnormal object type is a dirty type, the display information is a second color or a second stroboscopic information; If the abnormal object type is a foreign object type, the display information is a third color or a third stroboscopic information.

4. The method according to claim 1 or 2, characterized in that: Before inputting the target capacitance value information into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area, the method further includes: Acquire multiple capacitance value information and abnormal object types corresponding to different capacitance value information; According to the multiple capacitance value information and the types of abnormal objects corresponding to different capacitance value information, a preset algorithm model is trained to obtain the abnormality recognition model.

5. The method according to claim 1 or 2, characterized in that: The method further comprises: In response to an adjustment operation by a user, adjusting at least one abnormal object type in the mapping relationship and display information corresponding to the at least one abnormal object type; In response to a confirmation operation by the user, the adjusted mapping relationship is saved.

6. The method according to claim 1, characterized in that The displaying of the target display information in the fingerprint recognition area includes: The target display information is sent to a controller corresponding to the fingerprint module in the fingerprint recognition area, so that the controller controls the operation of the display module in the fingerprint module according to the target display information.

7. The method according to claim 1, characterized in that The step of obtaining target capacity information of a fingerprint recognition area in a device includes: The target capacitance value information is obtained by the sensor of the fingerprint module in the fingerprint recognition area.

8. A fingerprint recognition processing device, characterized in that: The device comprises: An acquisition module, used to acquire target capacity value information of a fingerprint recognition area in a device; A processing module, used for inputting the target capacitance value information into a pre-trained abnormality recognition model to obtain the target abnormal object type located in the fingerprint recognition area, wherein the abnormality recognition model is trained based on multiple capacitance value information and abnormal object types corresponding to different capacitance value information; A determination module, configured to determine the target display information in a preset mapping relationship according to the target abnormal object type, wherein the mapping relationship records at least one abnormal object type and display information corresponding to each abnormal object type; A display module is used to display the target display information in the fingerprint recognition area.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 7.

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