Processing method, device and equipment

Abstract

The invention provides a processing method, device and equipment, and is applied to the technical field of information processing. The processing method comprises the steps that a first fingerprint image of a user is acquired in response to the fact that the terminal equipment is in a target mode, the target mode represents that a fingerprint collection area of the terminal equipment has a target medium, and the first fingerprint image represents image data formed by the fact that fingerprints of the user are affected by the target medium; and inputting the first fingerprint image into the intelligent model to obtain a target fingerprint image which can meet the quality requirement of the terminal for fingerprint identification.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing technology, and in particular to a processing method, device and equipment. Background Art

[0002] With the development of information recognition technology, the scenarios of fingerprint entry and fingerprint unlocking of electronic devices based on fingerprint recognition technology are becoming more and more mature. However, in actual use scenarios, electronic devices usually have a greatly reduced fingerprint recognition rate due to fingers in special states (such as wet fingers, etc.), which ultimately affects the user experience. Summary of the invention

[0003] In view of this, the present disclosure provides a processing method, device and equipment.

[0004] According to a first aspect of the present disclosure, a processing method is provided, comprising: the processing method comprises: in response to a terminal device being in a target mode, acquiring a first fingerprint image of a user, the target mode representing that a fingerprint collection area of ​​the terminal device has a target medium, the first fingerprint image representing image data formed by the user's fingerprint being affected by the target medium; inputting the first fingerprint image into an intelligent model to obtain a target fingerprint image, the target fingerprint image being able to meet the quality requirements of the terminal for fingerprint recognition.

[0005] According to an embodiment of the present disclosure, the first fingerprint image is image data of the user's fingerprint formed by a reflection signal of the target medium; and / or the first fingerprint image is image data formed by filtering out the reflection signal of the target medium from the user's fingerprint.

[0006] According to an embodiment of the present disclosure, the method also includes: determining a second fingerprint image based on a first fingerprint image, wherein the first fingerprint image is image data of the user's fingerprint having a reflection signal formed by a target medium, and the second fingerprint image is image data formed by filtering out the reflection signal of the target medium from the user's fingerprint; inputting the first fingerprint image and the second fingerprint image into an intelligent model to determine a target fingerprint image, wherein an effective area of ​​the target fingerprint image for the user's fingerprint is greater than an effective area of ​​the fingerprint image for the user's fingerprint.

[0007] According to an embodiment of the present disclosure, based on the first fingerprint image, determining the second fingerprint image includes: inputting the first fingerprint image into an intelligent model to obtain a second fingerprint image; or obtaining the second fingerprint image of the user, the second fingerprint image being acquired by a fingerprint recognition module of the terminal; wherein obtaining the second fingerprint image of the user includes: determining at least one target area based on the first fingerprint image; determining the second fingerprint image based on the at least one target area, the second fingerprint image containing more valid information about the image of the at least one target area than the first fingerprint image containing valid information about the image of the at least one target area.

[0008] According to an embodiment of the present disclosure, a first fingerprint image and a second fingerprint image are input into an intelligent model to determine a target fingerprint image, including: splicing a first valid area of ​​a user's fingerprint in the first fingerprint image with a second valid area of ​​the user's fingerprint in the second fingerprint image to obtain a third fingerprint image; and inputting the third fingerprint image into the intelligent model to obtain a target fingerprint image.

[0009] According to an embodiment of the present disclosure, the method also includes: in response to a user pressing a fingerprint recognition module of a terminal device, obtaining a target parameter, the target parameter characterizing environmental information of a fingerprint collection area; when the target parameter meets a preset condition, determining that the terminal device is in a target mode, the preset condition characterizing environmental information that the fingerprint collection area has a target medium.

[0010] According to an embodiment of the present disclosure, when the target parameter is a sampled capacitance value of a fingerprint collection area, the method further includes: when the sampled capacitance value is the same as the target capacitance value, determining that the terminal device is in a target mode, and the target capacitance value is capacitance data of the fingerprint collection area having a target medium.

[0011] According to an embodiment of the present disclosure, when the target parameter is image information of a fingerprint collection area, the method further includes: acquiring a first fingerprint image in response to a user pressing a fingerprint recognition module of a terminal device; and determining that the terminal device is in target mode when the first fingerprint image matches a reference fingerprint image, and the reference image represents a preset fingerprint image of a user with a target medium.

[0012] The second aspect of the present disclosure provides a processing device, including: an acquisition module, used to acquire a first fingerprint image of a user in response to a terminal device being in a target mode, the target mode representing that a fingerprint collection area of ​​the terminal device has a target medium, and the first fingerprint image represents image data formed by the user's fingerprint being affected by the target medium; a processing module, used to input the first fingerprint image into an intelligent model to obtain a target fingerprint image, and the target fingerprint image can meet the quality requirements of the terminal for fingerprint recognition.

[0013] The third aspect of the present disclosure provides an electronic device, including: a fingerprint recognition module, which is used to collect fingerprint information of a user when the user presses the fingerprint recognition module, and the fingerprint recognition module corresponds to a fingerprint collection area; a processor, which is used to obtain a first fingerprint image of the user in response to the terminal device being in a target mode, the target mode characterizing that the fingerprint collection area of ​​the terminal device has a target medium, and the first fingerprint image represents image data formed by the user's fingerprint being affected by the target medium; the first fingerprint image is input into an intelligent model to obtain a target fingerprint image, and the target fingerprint image can meet the quality requirements of the terminal for fingerprint recognition.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned processing method.

[0015] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above processing method when executed by a processor.

[0016] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 A flowchart schematically shows a processing method according to an embodiment of the present disclosure;

[0019] Figure 2 One of the schematic diagrams schematically shows a processing method according to an embodiment of the present disclosure;

[0020] Figure 3 A second schematic diagram of a processing method according to an embodiment of the present disclosure is schematically shown, wherein the schematic diagram is a partial schematic diagram of a first fingerprint image;

[0021] Figure 4 The third schematic diagram of the processing method according to the embodiment of the present disclosure is schematically shown, wherein the schematic diagram is a partial schematic diagram of acquiring the second fingerprint image according to the first fingerprint image;

[0022] Figure 5 The schematic diagram shows a processing method according to an embodiment of the present disclosure;

[0023] Figure 6 A block diagram schematically shows a structure of a processing device according to an embodiment of the present disclosure; and

[0024] Figure 7 A block diagram of an electronic device suitable for implementing the processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] The embodiments of the present disclosure provide a processing method, an apparatus and a device. Before introducing the technical solution provided by the embodiments of the present disclosure, the related technologies involved in the present disclosure are first described.

[0030] With the development of information recognition technology, the scenarios of fingerprint entry and fingerprint unlocking based on fingerprint recognition technology for electronic devices (such as mobile phones, tablets, etc.) are becoming more and more mature. However, in actual usage scenarios, electronic devices usually have a greatly reduced fingerprint recognition rate due to special conditions of fingers (such as wet fingers, etc.), which ultimately affects the user experience.

[0031] For example, currently, optical fingerprint recognition is usually difficult to unlock with fingerprints when there are wet hands or water on the screen. For example, when there is water on the finger or the screen, the optical fingerprint is focused on the first layer of water reflection image on the screen during recognition. Therefore, the displayed image is uniformly an almost fully reflected whitish image, and the difference in the valley ridges of the original fingerprint becomes very small and almost invisible. Even if there are partial fingerprints, they are intermittent and cannot form a whole continuous image. Therefore, it has a great impact on the success rate and security of current optical fingerprint recognition, and fingerprint unlocking cannot be performed. For this reason, users are usually required to wipe the moisture off their fingers or the screen before fingerprint unlocking can be performed normally. This results in a poor user experience.

[0032] Below, some terms in this application are explained to facilitate understanding by those skilled in the art.

[0033] The fingerprint recognition module is to realize the recognition of individual fingerprint features through a specific sensing module. At present, fingerprint recognition modules are mainly divided into optical fingerprint modules, capacitive fingerprint modules and radio frequency fingerprint modules. The fingerprint recognition module can be combined with the membrane key dome key setting of the terminal device, and can be set on the front, back or side of the terminal device, etc., which is not limited by the present disclosure. Similarly, the fingerprint recognition module can be combined with the touch screen setting of the terminal device, and can be set below the touch panel of the touch screen.

[0034] Under-screen optical fingerprint recognition technology usually uses organic light-emitting semiconductor (OLED) as the light source for fingerprint recognition. Afterwards, the electronic device can receive reflected light, where the reflected light is generated by the reflection of the light emitted by the OLED based on the peaks (or ridges) and troughs (or grooves) of the fingerprint of the finger. Afterwards, the electronic device can determine the fingerprint image based on the difference in reflected light.

[0035] An embodiment of the present disclosure provides a processing method, comprising: the processing method comprises: in response to a terminal device being in a target mode, acquiring a first fingerprint image of a user, the target mode representing that a fingerprint collection area of ​​the terminal device has a target medium, the first fingerprint image representing image data formed by the user's fingerprint being affected by the target medium; inputting the first fingerprint image into an intelligent model to obtain a target fingerprint image, the target fingerprint image being able to meet the quality requirements of the terminal for fingerprint recognition.

[0036] Terminal equipment, also known as user equipment (UE), is a device that provides voice and / or data connectivity to users, such as handheld devices with wireless connection functions, vehicle-mounted devices, etc. Common terminals include, for example, mobile phones, tablet computers, laptops, PDAs, mobile internet devices (MID), wearable devices such as smart watches, smart bracelets, pedometers, etc.

[0037] The following will be passed Figure 1~Figure 5 The processing method of the embodiment of the present disclosure is described in detail.

[0038] Figure 1 The flowchart of the processing method according to the embodiment of the present disclosure is schematically shown.

[0039] like Figure 1 As shown, the processing method of this embodiment includes operations S210 to S220.

[0040] In operation S110, in response to the terminal device being in a target mode, a first fingerprint image of the user is acquired, the target mode represents that the fingerprint collection area of ​​the terminal device has a target medium, and the first fingerprint image represents image data formed by the user's fingerprint being affected by the target medium.

[0041] In operation S120, the first fingerprint image is input into the intelligent model to obtain a target fingerprint image, which can meet the quality requirements of the terminal for fingerprint recognition.

[0042] Exemplarily, the fingerprint collection area may be a designated area of ​​the terminal device display screen. For example, below the display screen. The fingerprint collection area may also be a designated area of ​​the terminal device body, for example, a designated area on the side of the terminal device, or a designated area on the back. The terminal device collects the fingerprint image of the fingerprint collection area through the fingerprint recognition module.

[0043] The target medium can be an external substance or environment that affects the quality of the fingerprint image. The target medium can affect the acquisition of the fingerprint image and further affect the recognition of the fingerprint image. For example, the target medium can be a substance present on the user's finger or the fingerprint recognition area, such as water, grease, film, etc.

[0044] The target mode may be a state triggered when the fingerprint collection area of ​​the terminal has a target medium. The target mode may be manually triggered by a user or automatically triggered by the terminal, which is not limited in the embodiments of the present disclosure.

[0045] The first fingerprint image may be an image that reflects the user's fingerprint formed under the influence of the target medium. That is, the first fingerprint image is not pure ridges and grooves, but also includes the influencing factors caused by the existence of the target medium. The first fingerprint image may be one or more fingerprint image data that are affected to varying degrees by the target medium. The first fingerprint image may be the original fingerprint image data collected by the fingerprint recognition module, or it may be the fingerprint image data after preprocessing the original fingerprint image data, and the degree of influence of the fingerprint image on the target medium can be reduced after preprocessing. For example, the first fingerprint image may be the image data formed by the user's fingerprint having the reflected signal of the target medium; the first fingerprint image may be the image data formed by the user's fingerprint having the reflected signal of the target medium filtered out; the first fingerprint image may simultaneously include the image data formed by the user's fingerprint having the reflected signal of the target medium and the image data formed by the user's fingerprint having the reflected signal of the target medium filtered out.

[0046] The intelligent model may be a trained deep learning model or other machine learning model, which is designed to optimize, repair or enhance the fingerprint image so that the target fingerprint image is less affected by the target medium than the first fingerprint image. That is, the target fingerprint image is clearer than the first fingerprint image. For example, the resolution of the target fingerprint image may be greater than the first fingerprint image, the contrast of the target fingerprint image may be greater than the first fingerprint image, the ridges and grooves in the target fingerprint image may be more obvious than the first fingerprint image, or the effective fingerprint area of ​​the target fingerprint image may be greater than the first fingerprint image.

[0047] Here, the quality requirements for fingerprint recognition by the terminal device can be set independently according to the actual situation. For example, when the fingerprint collection area has the target medium, the clarity of the collected fingerprint image must be greater than the first preset threshold. Or the degree of prominence of the ridges and grooves in the collected fingerprint image is greater than the second preset threshold. Or the continuous area of ​​the ridges and grooves in the collected fingerprint image is greater than the third preset threshold. The present disclosure does not make specific limitations on this.

[0048] Compared with using the first fingerprint image for fingerprint identification, using the target fingerprint image pair for fingerprint identification has a higher probability of successful identification.

[0049] It can be understood that by enhancing the first fingerprint image affected by the target medium through the intelligent model, the processed target fingerprint image is less affected by the target medium than the first fingerprint image, and the target fingerprint image has a higher clarity than the first fingerprint image. The success rate of fingerprint recognition is improved without increasing hardware costs, and the user experience is improved.

[0050] Figure 2 One of the schematic diagrams of the processing method according to the embodiment of the present disclosure is schematically shown.

[0051] As described above, in one possible implementation, the first fingerprint image is a fingerprint of the user having image data formed by a reflection signal of the target medium.

[0052] In one example, the first fingerprint image is a fingerprint image collected by the fingerprint recognition module when the finger is in the target medium environment. Figure 2 As shown, in optical fingerprint recognition, the first fingerprint image can be an image formed by the reflection of light focused on the first layer of water on the display screen when there is water on the finger.

[0053] The intelligent model enhances the first fingerprint image, which can improve the clarity of the fingerprint in the first fingerprint image and thus improve the success rate of fingerprint unlocking.

[0054] It is understandable that during the fingerprint collection process, if it is affected by the target medium, using the fingerprint image collected by the fingerprint recognition module in the target medium environment can ensure the security of fingerprint recognition.

[0055] As described above, in another achievable manner, the first fingerprint image is image data formed by filtering out the reflection signal of the target medium from the fingerprint of the user.

[0056] In one example, the first fingerprint image is a fingerprint image formed by filtering out the signal of the target medium being fully reflected once and then by the weak signal of the secondary reflection. Figure 2 As shown, the first fingerprint image can be a fingerprint image obtained by filtering out the total reflection signal of water and retaining the secondary reflection signal when the finger is wet and the light is reflected twice through the water.

[0057] The intelligent model enhances the first fingerprint image, which can improve the clarity of the fingerprint in the first fingerprint image, so that the target fingerprint image can meet the quality requirements of the terminal for fingerprint recognition, thereby improving the success rate of fingerprint unlocking.

[0058] It is understandable that in the fingerprint collection process, if it is affected by the target medium, directly filtering out the signal of the total reflection of the target medium and enhancing the fingerprint image formed by the weak signal of the secondary reflection of the fingerprint can improve the efficiency of fingerprint recognition.

[0059] As described above, in another possible implementation, the first fingerprint image includes image data formed by filtering out the reflected signal of the target medium with the user's fingerprint and image data formed by filtering out the reflected signal of the target medium with the user's fingerprint.

[0060] In one example, the first fingerprint image includes a fingerprint image collected by the fingerprint recognition module when the finger is in the target medium environment and a fingerprint image formed by a second reflection weak signal after filtering out the signal of the first total reflection of the target medium. For example, when the target medium is water, the first fingerprint image may include a fingerprint image obtained after the water total reflection signal and a fingerprint image obtained after removing the water total reflection signal.

[0061] The intelligent model can combine and enhance the two fingerprint images, which can further improve the clarity of the fingerprint in the first fingerprint image, so that the target fingerprint image can meet the quality requirements of the terminal for fingerprint recognition, thereby improving the success rate of fingerprint unlocking.

[0062] It is understandable that in the fingerprint collection process, if it is affected by the target medium, by filtering out the signal of the total reflection of the target medium, the fingerprint image formed by the weak signal of the secondary reflection of the fingerprint is fused with the fingerprint image collected by the fingerprint recognition module in the target medium environment, which can not only ensure the security of fingerprint recognition, but also improve the accuracy of fingerprint recognition.

[0063] Figure 3 A second schematic diagram of a processing method according to an embodiment of the present disclosure is schematically shown, wherein the schematic diagram is a partial schematic diagram of a first fingerprint image.

[0064] As described above, the processing method of this embodiment also includes: determining a second fingerprint image based on the first fingerprint image, the first fingerprint image being image data of the user's fingerprint having a reflection signal formed by the target medium, and the second fingerprint image being image data formed by filtering out the reflection signal of the target medium from the user's fingerprint; inputting the first fingerprint image and the second fingerprint image into an intelligent model to determine a target fingerprint image, wherein an effective area of ​​the target fingerprint image for the user's fingerprint is greater than an effective area of ​​the fingerprint image for the user's fingerprint.

[0065] Exemplarily, the first fingerprint image is a fingerprint image of the user's fingerprint collected by the fingerprint recognition module in an environment with the target medium. Here, the first fingerprint image can be formed when the fingerprint recognition module collects the user's wet hand fingerprint. For example, when the user's wet hand is used for fingerprint recognition, the fingerprint recognition module collects the fingerprint image obtained after the signal is fully reflected by water once. When the user's fingerprint is affected by water, the fingerprint image formed by the full reflection of the light signal once is as follows: Figure 3 As shown, the changes in the valleys and ridges of the fingerprint cannot be seen in the image formed after the light in the water area is reflected by the water, while the changes in the valleys and ridges of the fingerprint can be seen in the image formed after the light in the water-free area is reflected by the fingerprint.

[0066] The second fingerprint image is a fingerprint image formed by filtering out the signal of the first total reflection of the target medium and extracting the second reflected weak signal. Here, the second fingerprint image can be obtained by signal processing the first fingerprint image. For example, when the user performs fingerprint recognition with wet hands, the fingerprint recognition module collects the first fingerprint image after the signal is fully reflected by water. The second fingerprint image is obtained by processing the pixels formed by the signal formed by the water reflection in the first fingerprint image. The second fingerprint image can also be formed by the fingerprint recognition module using the secondary reflection of light through the target medium, filtering out the original signal of the total reflection of the target medium, and retaining the second reflected weak signal. For example, when the user performs fingerprint recognition with wet hands, if it is determined that the user's fingerprint is in a water environment, the fingerprint recognition module collects the fingerprint signal of the second reflection after filtering out the original signal of the first total reflection of water.

[0067] The effective area of ​​the user's fingerprint can be the fingerprint area that can be identified in the fingerprint image, that is, it can be the area of ​​the continuous and clear ridges and grooves.

[0068] In one example, during fingerprint recognition with wet hands, the fingerprint recognition module collects a first fingerprint image formed after a signal is fully reflected by water. Based on the first fingerprint image, it is determined to filter out the original signal fully reflected by water and retain a second fingerprint image formed by a fingerprint signal that is reflected twice by water. The first fingerprint image and the second fingerprint image are input into the intelligent model together, and the intelligent model obtains a target fingerprint image by fusing the first fingerprint image and the second fingerprint image, so that the target fingerprint image has a larger area of ​​continuous and clear ridges and grooves than the first fingerprint image and the second fingerprint image.

[0069] It is understandable that in the fingerprint collection process, if it is affected by the target medium, by filtering out the signal of the total reflection of the target medium, the fingerprint image formed by the weak signal of the secondary reflection of the fingerprint is fused with the fingerprint image collected by the fingerprint recognition module in the target medium environment, which can not only ensure the security of fingerprint recognition, but also improve the accuracy of fingerprint recognition.

[0070] As described above, for the operation of: determining the second fingerprint image based on the first fingerprint image, in one achievable manner, the operation may further include: inputting the first fingerprint image into the intelligent model to obtain the second fingerprint image.

[0071] In one example, when the user performs fingerprint recognition with wet hands, the fingerprint recognition module collects a first fingerprint image after the signal is fully reflected by water. The first fingerprint image is input into the intelligent model, and the intelligent model processes the pixels formed by the signal formed by the water reflection in the first fingerprint image to obtain a second fingerprint image. For example, the intelligent model can deblur the image area affected by water in the first fingerprint image and restore the fingerprint features in the image area affected by water. The intelligent model can also perform super-resolution reconstruction on the image area affected by water in the first fingerprint image to improve the details of the fingerprint features in the image area affected by water.

[0072] Among them, the intelligent model can process the image area affected by water in the first fingerprint image, and can also simultaneously process the area in the first fingerprint image that is not affected by water but where the fingerprint features are not obvious, so that the fingerprint features in the processed second fingerprint image will increase or become more obvious.

[0073] As described above, for the operation: determining the second fingerprint image based on the first fingerprint image, in another achievable manner, the operation may further include: acquiring the second fingerprint image of the user, the second fingerprint image being acquired by a fingerprint recognition module of the terminal.

[0074] In one example, when a user performs fingerprint recognition with wet hands, the fingerprint recognition module first collects a first fingerprint image obtained after the signal is fully reflected by water. The fingerprint recognition module collects the fingerprint image a second time, using the secondary reflection of light through water to filter out the original signal of the first total reflection of water, retain the weak signal of the secondary reflection, and obtain the second fingerprint image.

[0075] Figure 4 The third schematic diagram of the processing method according to the embodiment of the present disclosure is schematically shown, wherein the schematic diagram is a partial schematic diagram of collecting the second fingerprint image based on the first fingerprint image.

[0076] For the fingerprint recognition module to collect the fingerprint image for the second time to obtain the second fingerprint image, the following operations may be included: determining at least one target area based on the first fingerprint image; determining the second fingerprint image based on the at least one target area, and the second fingerprint image contains more valid information about the image of the at least one target area than the first fingerprint image contains about the image of the at least one target area.

[0077] For example, the target area may be an area in the first fingerprint image where the fingerprint feature is not obvious. The target area may be one or more. For example, the target area may be an area in the first fingerprint image that is deeply affected by the target medium. Figure 4As shown, when the user collects fingerprint images with wet hands, the images collected in the water area are almost fully reflected and have no fingerprint features. These areas can be used as target areas. The target area can also be an area in the first fingerprint image that is not affected by the target medium but has unclear fingerprint features.

[0078] The effective information contained in the target area may be the number and clarity of fingerprint features in the target area. For example, the fingerprint features of the target area are fuzzy in the first fingerprint image, but clear in the second fingerprint image.

[0079] It should be noted that the signal of the second reflection of light through water is usually weaker than the signal of the first reflection. This weak signal may contain some more delicate information, such as the texture or other details of the fingerprint. By retaining these weak second reflection signals, more delicate and accurate fingerprint features can be captured.

[0080] When it is determined that the terminal has entered the target mode, the area where the fingerprint features are unclear will be determined as the target area based on the first fingerprint image. When the second fingerprint image is collected for the second time, the total water reflection light is filtered and the collected image is calibrated to highlight the fingerprint features of the target area. At this time, the fingerprint features of the target area may become clear compared to the fingerprint features of the target area in the first fingerprint image, and the fingerprint features of the non-target area may become blurred compared to the fingerprint features of the non-target area in the first fingerprint image.

[0081] When calibrating the second captured image, the position of the light source can be adjusted so that the part that was not clearly reflected in the first reflection can show more details in the second reflection. For example, the angle of the light source can be adjusted so that the light can better illuminate the target area in the second reflection.

[0082] In one example, continue to refer to Figure 4 When the user's wet hands are used for fingerprint recognition, the fingerprint recognition module first collects the first fingerprint image after the signal is fully reflected by water. The image collected in the water area is almost fully reflected without fingerprint features. Therefore, the water area is used as the target area for secondary collection of user fingerprints. The fingerprint recognition module collects the fingerprint image for the second time, using the secondary reflection of light through water to filter out the original signal of the first total reflection of water, retain the weak signal of the secondary reflection, and obtain the second fingerprint image in the target area with fingerprint features better than the first fingerprint image.

[0083] Among them, the area of ​​the first target area in the first fingerprint image is smaller than the area of ​​the first target area in the second fingerprint image. Since the area of ​​the fingerprint recognition module for collecting fingerprints is certain, and the user's finger will not move in a short time, the collection area and position of the first fingerprint image and the second fingerprint image are the same. The position of the target area in the first fingerprint image and the second fingerprint image is the same. However, in order to ensure that the fingerprint features (for example, texture) in the first fingerprint image and the second fingerprint image can be better integrated, the range of the first target area in the second fingerprint image is expanded to cover the fingerprint features adjacent to the first target area in the first fingerprint image, so that the first target area of ​​the second fingerprint image and the fingerprint area adjacent to the first target area in the first fingerprint image can be quickly found, so that the fingerprint features adjacent to the first target area in the first fingerprint image can be better corresponded with the fingerprint features in the first target area in the second fingerprint image.

[0084] As described above, in the operation of inputting the first fingerprint image and the second fingerprint image into the intelligent model to determine the target fingerprint image, the operation may further include: splicing the first valid area of ​​the user fingerprint in the first fingerprint image with the second valid area of ​​the user fingerprint in the second fingerprint image to obtain a third fingerprint image; inputting the third fingerprint image into the intelligent model to obtain the target fingerprint image.

[0085] Exemplarily, the effective area may be an area having fingerprint features in the fingerprint image features.

[0086] During the fingerprint recognition process of the user's wet hands, the fingerprint recognition module first collects the first fingerprint image obtained after the signal is fully reflected by water. The image collected in the water-free area has fingerprint features (the first effective area), and the image collected in the water area is almost fully reflected without fingerprint features. Therefore, the water area is used as the target area to collect the user's fingerprint for the second time. The fingerprint recognition module collects the fingerprint image for the second time, and uses the secondary reflection of light through water to filter out the original signal of the first total reflection of water, retain the weak signal of the secondary reflection, and obtain the second fingerprint image in which the fingerprint features in the target area are better than the first fingerprint image. The target area in the second fingerprint image can be used as the second effective area. The first fingerprint image and the second fingerprint image are input into the intelligent model, and the intelligent model splices the fingerprint features in the first effective area and the second effective area to form a third fingerprint image with a continuous fingerprint feature area to meet the area requirements of fingerprint recognition. At the same time, the intelligent model processes the third fingerprint image input to improve the clarity of the fingerprint features in the third fingerprint image, and obtains a target fingerprint image with continuous and clear fingerprint features to better perform subsequent fingerprint recognition.

[0087] In other embodiments, a splicing module may also be used to splice the first valid area of ​​the user's fingerprint in the first fingerprint image with the second valid area of ​​the user's fingerprint in the second fingerprint image. For example, the first fingerprint image and the second fingerprint image are input into the splicing module, and the splicing module splices the fingerprint features in the first valid area and the second valid area to form a third fingerprint image with a continuous fingerprint feature area to meet the area requirements of fingerprint recognition. The third fingerprint image is input into the intelligent model for further processing.

[0088] In some embodiments, the intelligent model can be obtained by the following operations.

[0089] Acquire multiple sample data, the sample data include the user's first fingerprint image, the second fingerprint image and the historical fingerprint image, the first fingerprint image is the image data formed by the user's fingerprint being affected by the target medium, the second fingerprint image is the image data formed by filtering out the reflected signal of the target medium from the user's fingerprint, and the historical fingerprint image represents the image data formed by the user's fingerprint not being affected by the target medium.

[0090] Multiple sample data are input into the intelligent model to be trained to obtain a predicted fingerprint image corresponding to each sample data.

[0091] According to the difference between the predicted fingerprint image and the historical fingerprint image, the parameters of the intelligent model are adjusted until the intelligent model converges.

[0092] There may be one or more target media, for example, water, oil, etc.

[0093] In one example, the first fingerprint image and the second fingerprint image in the multiple sample data may be fingerprint images collected when the hands of multiple users are wet. The first fingerprint image is the first fingerprint image obtained when the fingerprint recognition module collects the signal of the wet hands of user A for the first time after the signal is fully reflected by water once. The second fingerprint image is the second fingerprint image obtained when the fingerprint recognition module collects the fingerprint image of the wet hands of user A for the second time, using the secondary reflection of light through water, filtering out the signal of the original full reflection of water once, and retaining the weak signal of the secondary reflection. The historical fingerprint image is the fingerprint image obtained when the fingerprint recognition module collects the dry hands of user A, and the historical fingerprint image may be the fingerprint data of the user A that can be successfully identified.

[0094] The first fingerprint image and the second fingerprint image of user A are input into the intelligent model to obtain the predicted fingerprint image. According to the difference between the predicted fingerprint image and the historical fingerprint image, the parameters of the neural network are continuously adjusted to find the minimum value of the difference. The neural network converges to obtain a trained intelligent model.

[0095] For different target media, the intelligent model needs to be trained using the first fingerprint image and the second fingerprint image collected by the user when the user is in different target media.

[0096] Here, the difference between the predicted fingerprint image and the historical fingerprint image can be determined by the matching degree between the fingerprint features in the predicted fingerprint image and the historical fingerprint image, and whether the predicted fingerprint image is successfully identified in fingerprint recognition.

[0097] It should be known that convergence usually means that the model parameters are updated to a stable state, in which the model has a good fit to the training data and can generalize well to new, unseen data. Specifically, convergence means that the model's loss function (a function that measures the difference between the model's predictions and the actual values) has found a possible minimum or local minimum.

[0098] In some embodiments, the processing method of this embodiment also includes: in response to the user pressing the fingerprint recognition module of the terminal device, obtaining the target parameter, the target parameter characterizing the environmental information of the fingerprint collection area; when the target parameter meets the preset condition, determining that the terminal device is in the target mode, the preset condition characterizing the environmental information that the fingerprint collection area has the target medium.

[0099] For example, the target parameter can be the environmental information of the fingerprint collection area. When the user presses the fingerprint recognition module, the sensor will collect data related to fingerprint recognition. The "target parameter" here may not only refer to the fingerprint image itself, but also other data related to the collected fingerprint, such as the contact pressure, humidity, temperature and other environmental information of the sensor. This information can reflect the environmental conditions of the fingerprint collection area (i.e., the area where the user's finger contacts the sensor).

[0100] The preset condition may be information about environmental changes caused by the fingerprint collection area when the target medium is present. The preset condition may be obtained by preliminarily collecting fingerprints by placing the user's finger in an environment with different target media.

[0101] For example, when a user presses the fingerprint recognition module of a terminal device, the sensor in the terminal device collects environmental information of the fingerprint collection area. When the collected environmental information is consistent with the environmental information corresponding to the preset target medium, it can be determined that the fingerprint collection area has the target medium and trigger the terminal device to enter the target mode.

[0102] As described above, in one implementable manner, when the target parameter is the sampled capacitance value of the fingerprint collection area, the processing method of this embodiment also includes: when the sampled capacitance value is the same as the target capacitance value, determining that the terminal device is in target mode, and the target capacitance value is the capacitance data of the fingerprint collection area having the target medium.

[0103] Exemplarily, the preset condition may also be the capacitance value of the fingerprint collection area when there is a target medium. Different target media correspond to different capacitance values. The target medium may be water, oil, etc.

[0104] In one example, when a fingerprint contacts a sensor in a fingerprint recognition module, the capacitance value of the sensor changes. For example, during the fingerprint recognition process of a user with wet hands, when the user presses the fingerprint recognition module, the water on the user's finger will affect the capacitance value of the fingerprint collection area. At this time, the fingerprint recognition module detects the sampled capacitance value of the fingerprint collection area and compares it with the preset capacitance values ​​of different target media. If the sampled capacitance value is the same as the capacitance value (target capacitance value) corresponding to the target medium being water, it can be confirmed that the user's fingerprint is in the target medium environment, triggering the terminal device to enter the target mode.

[0105] As described above, in another possible implementation, when the target parameter is the image information of the fingerprint collection area, the processing method of this embodiment also includes: in response to the user pressing the fingerprint recognition module of the terminal device, obtaining a first fingerprint image; when the first fingerprint image matches the reference fingerprint image, determining that the terminal device is in target mode, and the reference image represents a preset fingerprint image of a user with a target medium.

[0106] In an exemplary embodiment, the preset condition may also be an image of the fingerprint collection area when there is a target medium. Different target media correspond to different fingerprint images. The target medium may be water, oil, etc.

[0107] In order to obtain a true reference fingerprint image, a twill calibration head with a target medium can be added to the calibration fixture for calibration, and fingerprint images of the fingerprint in different target medium environments can be obtained as reference fingerprint images.

[0108] Here, the first fingerprint image is image data formed by the user's fingerprint having a reflection signal of the target medium.

[0109] In one example, during the fingerprint recognition process of a user with wet hands, when the user presses the fingerprint recognition module, the fingerprint recognition module collects a first fingerprint image of the fingerprint collection area and matches it with a preset reference fingerprint image of a different target medium. If the first fingerprint image matches the fingerprint image corresponding to the target medium being water (reference fingerprint image), it can be confirmed that the user's fingerprint is in a water environment, triggering the terminal device to enter target mode.

[0110] In order to facilitate the understanding of the processing method of the embodiment of the present disclosure, the following will be combined with Figure 5 Provide a detailed description.

[0111] Figure 5The schematic diagram shows a principle diagram of a processing method according to an embodiment of the present disclosure.

[0112] The processing method of this embodiment includes operation S310 to operation S370'.

[0113] In operation S310, a first fingerprint image is acquired, where the first fingerprint image is image data of a user's fingerprint formed by a reflection signal of a target medium.

[0114] In operation S320, it is determined whether the fingerprint collection area has a target medium according to the first fingerprint image.

[0115] In operation S330, when the fingerprint collection area has the target medium, it is determined that the terminal device is in the target mode.

[0116] In operation S330 ′, in a case where the fingerprint collection area does not have the target medium, it is determined that the terminal device is in the normal mode.

[0117] In operation S340, a second fingerprint image is determined based on the first fingerprint image, where the second fingerprint image is image data formed by filtering out a reflection signal of the target medium from the fingerprint of the user.

[0118] In operation S350, the first fingerprint image and the second fingerprint image are input into the intelligent model to obtain a target fingerprint image.

[0119] In operation S360, it is determined whether the target fingerprint image meets the fingerprint recognition requirement.

[0120] In operation S370, fingerprint recognition is performed if the target fingerprint image meets the fingerprint recognition requirement.

[0121] In operation S370', if the target fingerprint image does not meet the fingerprint recognition requirement, fingerprint recognition is not performed.

[0122] In one example, during the process of user fingerprint recognition, when the user presses the fingerprint recognition module of the terminal device with a wet hand, the fingerprint recognition module collects the first fingerprint image of the fingerprint collection area. The first fingerprint image is obtained after the fingerprint recognition module collects the user's wet hand for the first time after the signal is fully reflected by water. In the case where the first fingerprint image does not match the fingerprint images corresponding to multiple target media (reference fingerprint images), it can be confirmed that the user's fingerprint is not affected by the target medium, triggering the terminal device to enter the normal mode. In the normal mode, fingerprint recognition is performed according to the collected first fingerprint image. The first fingerprint image is matched with the preset reference fingerprint images of different target media. In the case where the first fingerprint image matches the fingerprint image corresponding to the target medium being water (reference fingerprint image), it can be confirmed that the user's fingerprint is in a water environment, triggering the terminal device to enter the target mode. After entering the target mode, the area (target area) where the fingerprint feature is unclear in the first fingerprint image is determined, and according to the target area, the second fingerprint image of the user about the target area is collected for the second time through the fingerprint recognition module. The second fingerprint image is obtained by using the secondary reflection of light through water, filtering out the original signal of the first total reflection of water, and retaining the weak signal of the secondary reflection. The first fingerprint image and the second fingerprint image are input into the intelligent model. The intelligent model splices the first valid area in the first fingerprint image with the second valid area in the second fingerprint image to obtain a third fingerprint image, and processes the third fingerprint image to improve the clarity of the third fingerprint image to obtain a target fingerprint image. Determine whether the target fingerprint image meets the quality requirements of fingerprint recognition. If the target fingerprint image meets the quality requirements of fingerprint recognition, fingerprint recognition is performed. If the target fingerprint image meets the quality requirements of fingerprint recognition, fingerprint recognition is not performed.

[0123] Based on the above processing method, the present disclosure also provides a processing device. Figure 6 The device is described in detail.

[0124] Figure 6 The structural block diagram of a processing device according to an embodiment of the present disclosure is schematically shown.

[0125] like Figure 6 As shown, the processing device 400 of this embodiment includes an acquisition module 410 and a processing module 420 .

[0126] The acquisition module 410 is used to acquire a first fingerprint image of the user in response to the terminal device being in a target mode, the target mode indicating that the fingerprint collection area of ​​the terminal device has a target medium, and the first fingerprint image indicating image data formed by the user's fingerprint being affected by the target medium. In one embodiment, the acquisition module 410 can be used to perform the operation S210 described above, which will not be described in detail here.

[0127] The processing module 420 is used to input the first fingerprint image into the intelligent model to obtain a target fingerprint image, which can meet the quality requirements of the terminal for fingerprint recognition. In one embodiment, the processing module 420 can be used to perform the operation S220 described above, which will not be repeated here.

[0128] According to an embodiment of the present disclosure, any multiple modules in the acquisition module 410 and the processing module 420 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 410 and the processing module 420 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 410 and the processing module 420 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0129] Figure 7 A block diagram of an electronic device suitable for implementing the processing method according to an embodiment of the present disclosure is schematically shown.

[0130] like Figure 7 As shown, the electronic device 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 to a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0131] In RAM503, various programs and data required for the operation of electronic device 500 are stored. Processor 501, ROM502 and RAM503 are connected to each other via bus 504. Processor 501 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM502 and / or RAM503. It should be noted that the program can also be stored in one or more memories other than ROM502 and RAM503. Processor 501 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in one or more memories.

[0132] According to an embodiment of the present disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The electronic device 500 may further include one or more of the following components connected to the I / O interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 504 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 505 including a network interface card such as a LAN card, a modem, etc. The communication portion 505 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage portion 508 as needed.

[0133] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0134] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM502 and / or RAM503 described above and / or one or more memories other than ROM502 and RAM503.

[0135] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the processing method provided by the embodiment of the present disclosure.

[0136] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 501. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0137] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 505, and / or installed from a removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0138] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 505, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0139] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0140] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a 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 box can also occur in a different order from the order marked in the accompanying drawings. For example, two 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 box in the block diagram or flow chart, and the combination of the boxes in the block diagram 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.

[0141] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0142] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A processing method comprising: In response to the terminal device being in a target mode, acquiring a first fingerprint image of the user, wherein the target mode indicates that the fingerprint collection area of ​​the terminal device has a target medium, and the first fingerprint image indicates image data formed by the fingerprint of the user being affected by the target medium; The first fingerprint image is input into an intelligent model to obtain a target fingerprint image, and the target fingerprint image can meet the quality requirements of the terminal for fingerprint recognition.

2. The method according to claim 1, wherein the first fingerprint image is image data of the user's fingerprint formed by a reflection signal of a target medium; and / or The first fingerprint image is image data formed by filtering out the reflection signal of the target medium from the fingerprint of the user.

3. The method according to claim 1 or 2, further comprising: Based on the first fingerprint image, determining a second fingerprint image, wherein the first fingerprint image is image data of the user's fingerprint formed by a reflection signal of a target medium, and the second fingerprint image is image data formed by filtering out the reflection signal of the target medium from the user's fingerprint; The first fingerprint image and the second fingerprint image are input into the intelligent model to determine a target fingerprint image, wherein the effective area of ​​the target fingerprint image for the user fingerprint is larger than the effective area of ​​the fingerprint image for the user fingerprint.

4. The method according to claim 3, determining a second fingerprint image based on the first fingerprint image, comprising: Inputting the first fingerprint image into the intelligent model to obtain the second fingerprint image; or Acquire a second fingerprint image of the user, where the second fingerprint image is collected by a fingerprint recognition module of the terminal; The step of obtaining the second fingerprint image of the user includes: Determining at least one target area according to the first fingerprint image; A second fingerprint image is determined based on the at least one target area, wherein the second fingerprint image contains more valid information about the image of the at least one target area than the first fingerprint image contains about the image of the at least one target area.

5. The method according to claim 3, inputting the first fingerprint image and the second fingerprint image into the intelligent model to determine the target fingerprint image, comprising: splicing a first valid area of ​​the user's fingerprint in the first fingerprint image with a second valid area of ​​the user's fingerprint in the second fingerprint image to obtain a third fingerprint image; The third fingerprint image is input into the intelligent model to obtain a target fingerprint image.

6. The method according to claim 1, further comprising: In response to the user pressing the fingerprint recognition module of the terminal device, a target parameter is acquired, where the target parameter represents environmental information of the fingerprint collection area; In the case where the target parameter meets a preset condition, it is determined that the terminal device is in the target mode, and the preset condition indicates that the fingerprint collection area has environmental information of the target medium.

7. The method according to claim 6, when the target parameter is a sampling capacitance value of the fingerprint collection area, the method further comprises: In the case where the sampled capacitance value is the same as the target capacitance value, it is determined that the terminal device is in the target mode, and the target capacitance value is capacitance data of the target medium in the fingerprint collection area.

8. The method according to claim 6, wherein when the target parameter is image information of the fingerprint collection area, the method further comprises: In response to the user pressing the fingerprint recognition module of the terminal device, acquiring a first fingerprint image; In the case where the first fingerprint image matches the reference fingerprint image, it is determined that the terminal device is in target mode, and the reference image represents a preset fingerprint image of a user having the target medium.

9. A processing device comprising: an acquisition module, configured to acquire a first fingerprint image of a user in response to the terminal device being in a target mode, wherein the target mode indicates that a fingerprint collection area of ​​the terminal device has a target medium, and the first fingerprint image indicates image data formed by the fingerprint of the user being affected by the target medium; The processing module is used to input the first fingerprint image into the intelligent model to obtain a target fingerprint image, and the target fingerprint image can meet the quality requirements of the terminal for fingerprint recognition.

10. An electronic device, comprising: A fingerprint recognition module, used to collect fingerprint information of the user when the user presses the fingerprint recognition module, and the fingerprint recognition module corresponds to the fingerprint collection area; The processor is configured to obtain a first fingerprint image of a user in response to the terminal device being in a target mode, wherein the target mode indicates that a fingerprint collection area of ​​the terminal device has a target medium, and the first fingerprint image indicates image data formed by the fingerprint of the user being affected by the target medium; The first fingerprint image is input into the intelligent model to obtain a target fingerprint image, and the target fingerprint image can meet the quality requirements of the terminal for fingerprint recognition.

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