Predicted fingerprint image generation method, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a predicted fingerprint image generation method, electronic equipment, a storage medium and a program product, and the method comprises the steps: carrying out the preprocessing of an original fingerprint image on the basis of obtaining the original fingerprint image of any target object, so as to achieve the obtaining of an original fingerprint vector from the processed original fingerprint image; a fingerprint prediction model corresponding to the target object is further obtained, so that the fingerprint prediction model outputs a predicted fingerprint vector according to an input original fingerprint vector, a preset time interval and a pre-generated target fingerprint change coefficient; a corresponding predicted fingerprint image is generated according to the predicted fingerprint vector; the problem of poor accuracy of the predicted fingerprint image generated based on the scheme in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method for generating a predicted fingerprint image, an electronic device, a storage medium, and a program product. Background Art

[0002] With the development of fingerprint recognition technology, the identity of a person can be determined by identifying a fingerprint image. Since there is a certain time difference between the acquisition time of the fingerprint image and the actual usage time, it is necessary to estimate the predicted fingerprint image at the current usage time based on the original acquired fingerprint image.

[0003] In the prior art, a predicted fingerprint image is generated by performing calculations on the original fingerprint image based on a preset fingerprint aging formula.

[0004] However, the predicted fingerprint images generated by the existing solutions have the problem of poor accuracy. Summary of the Invention

[0005] The embodiments of the present application provide a predicted fingerprint image generation method, electronic device, storage medium, and program product to solve the problem of poor accuracy of predicted fingerprint images generated based on solutions in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for generating a predicted fingerprint image, comprising: obtaining an original fingerprint image of any target object; preprocessing the original fingerprint image to obtain a processed original fingerprint image; obtaining an original fingerprint vector from the processed original fingerprint image; obtaining a fingerprint prediction model corresponding to the target object; inputting the original fingerprint vector, a preset time interval, and a pre-generated target fingerprint variation coefficient into the fingerprint prediction model to output a predicted fingerprint vector; and generating a predicted fingerprint image based on the predicted fingerprint vector.

[0007] In a possible implementation, the process of pre-generating the target fingerprint variation coefficient includes: determining one or more occupational information of the target object according to the preset time interval; and obtaining the target fingerprint variation coefficient according to the one or more occupational information.

[0008] In a possible implementation, obtaining the target fingerprint variation coefficient based on the one or more occupational information includes: obtaining the corresponding occupational engagement duration and the corresponding fingerprint variation coefficient based on the one or more occupational information; for each piece of occupational information, generating an actual fingerprint variation coefficient based on the corresponding occupational engagement duration and the corresponding fingerprint variation coefficient; and obtaining the target fingerprint variation coefficient based on the actual fingerprint variation coefficient corresponding to each piece of occupational information.

[0009] In a possible implementation, obtaining the fingerprint prediction model corresponding to the target object includes: determining the target age of the target object based on the acquisition time of the original fingerprint image; and determining the fingerprint prediction model from a plurality of candidate fingerprint prediction models based on the age range corresponding to the target age.

[0010] In a possible embodiment, the original fingerprint image includes a first original fingerprint image and a second original fingerprint image, and the first original fingerprint image and the second original fingerprint image are two fingerprint images of the target object at different positions in the same time interval; the preprocessing of the original fingerprint image to obtain a processed original fingerprint image includes: performing noise reduction processing on the first original fingerprint image to generate a first processed image; performing noise reduction processing on the second original fingerprint image to generate a second processed image; obtaining a common image area, a first non-public image area corresponding to the first processed image, and a second non-public image area corresponding to the second processed image based on the image area of the first processed image and the image area of the second processed image; if the area of the first non-public image area is smaller than that of the second non-public image area, If the area of the common image area is greater than or equal to the area of the second non-public image area, then the area image corresponding to the first non-public image area is corrected according to the area image of the common image area corresponding to the first processed image and the area image of the common image area corresponding to the second processed image to generate a first corrected image; and the processed original fingerprint image is obtained according to the first corrected image and the second processed image. If the area of the first non-public image area is greater than or equal to the area of the second non-public image area, then the area image corresponding to the second non-public image area is corrected according to the area image of the common image area corresponding to the first processed image and the area image of the common image area corresponding to the second processed image to generate a second corrected image; and the processed original fingerprint image is obtained according to the second corrected image and the first processed image.

[0011] In a possible embodiment, the correcting the regional image corresponding to the first non-public image area according to the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a first corrected image includes: generating a first tensor group according to the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image; correcting the regional image corresponding to the first non-public image area according to the first tensor group to generate the first corrected image; correcting the regional image corresponding to the second non-public image area according to the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a second corrected image includes: generating a second tensor group according to the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image; correcting the regional image corresponding to the second non-public image area according to the second tensor group to generate the second corrected image.

[0012] In a second aspect, an embodiment of the present application provides a device for generating a predicted fingerprint image, comprising:

[0013] An acquisition module, used to acquire an original fingerprint image of any target object;

[0014] a processing module configured to preprocess the original fingerprint image to obtain a processed original fingerprint image; obtain an original fingerprint vector from the processed original fingerprint image; obtain a fingerprint prediction model corresponding to the target object; and input the original fingerprint vector, a preset time interval, and a pre-generated target fingerprint variation coefficient into the fingerprint prediction model to output a predicted fingerprint vector;

[0015] A generating module is used to generate a predicted fingerprint image according to the predicted fingerprint vector.

[0016] In a possible embodiment, during the pre-generation process of the target fingerprint variation coefficient, the predicted fingerprint image generation device is specifically used to: determine one or more occupational information of the target object according to the preset time interval; and obtain the target fingerprint variation coefficient based on the one or more occupational information.

[0017] In a possible embodiment, when the predicted fingerprint image generation device obtains the target fingerprint variation coefficient based on the one or more occupational information, it is specifically used to: obtain the corresponding occupational engagement duration and the corresponding fingerprint variation coefficient based on the one or more occupational information; for each occupational information, generate the actual fingerprint variation coefficient based on the corresponding occupational engagement duration and the corresponding fingerprint variation coefficient; obtain the target fingerprint variation coefficient based on the actual fingerprint variation coefficient corresponding to each occupational information.

[0018] In a possible embodiment, when the processing module obtains the fingerprint prediction model corresponding to the target object, it is specifically used to: determine the target age of the target object based on the acquisition time of the original fingerprint image; and determine the fingerprint prediction model from multiple candidate fingerprint prediction models based on the age range corresponding to the target age.

[0019] In a possible embodiment, the original fingerprint image includes a first original fingerprint image and a second original fingerprint image, and the first original fingerprint image and the second original fingerprint image are two fingerprint images of the target object at different positions in the same time interval; when the processing module pre-processes the original fingerprint image to obtain the processed original fingerprint image, it is specifically used to: perform noise reduction processing on the first original fingerprint image to generate a first processed image; perform noise reduction processing on the second original fingerprint image to generate a second processed image; obtain a common image area, a first non-public image area corresponding to the first processed image, and a second non-public image area corresponding to the second processed image based on the image area of the first processed image and the image area of the second processed image; if the area of the first non-public image area is smaller than the area of the If the area of the second non-public image area is greater than or equal to the area of the second non-public image area, then the area image corresponding to the first non-public image area is corrected according to the area image of the public image area corresponding to the first processed image and the area image of the public image area corresponding to the second processed image to generate a first corrected image; and the processed original fingerprint image is obtained according to the first corrected image and the second processed image. If the area of the first non-public image area is greater than or equal to the area of the second non-public image area, then the area image corresponding to the second non-public image area is corrected according to the area image of the public image area corresponding to the first processed image and the area image of the public image area corresponding to the second processed image to generate a second corrected image; and the processed original fingerprint image is obtained according to the second corrected image and the first processed image.

[0020] In a possible embodiment, when the processing module corrects the regional image corresponding to the first non-public image area based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a first corrected image, the processing module is specifically used to: generate a first tensor group based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image; correct the regional image corresponding to the first non-public image area based on the first tensor group to generate the first corrected image; when the processing module corrects the regional image corresponding to the second non-public image area based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a second corrected image, the processing module is specifically used to: generate a second tensor group based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image; correct the regional image corresponding to the second non-public image area based on the second tensor group to generate the second corrected image.

[0021] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0022] The memory stores computer-executable instructions;

[0023] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0024] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0025] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0026] The predicted fingerprint image generation method, electronic device, storage medium and program product provided in the embodiments of the present application, based on obtaining the original fingerprint image of any target object, pre-processes the original fingerprint image to obtain the original fingerprint vector from the processed original fingerprint image; further obtains a fingerprint prediction model corresponding to the target object, so that the fingerprint prediction model outputs a predicted fingerprint vector based on the input original fingerprint vector, a preset time interval and a pre-generated target fingerprint variation coefficient; and then generates a corresponding predicted fingerprint image based on the predicted fingerprint vector. This solves the problem of poor accuracy of predicted fingerprint images generated based on solutions based on existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] 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.

[0028] Figure 1 A schematic diagram of a scenario for the method for generating a predicted fingerprint image provided in this application;

[0029] Figure 2 A flowchart of a method for generating a predicted fingerprint image according to one embodiment of the present application;

[0030] Figure 3 A flowchart of a method for generating a predicted fingerprint image provided by another embodiment of the present application;

[0031] Figure 4 A schematic diagram of the structure of a device for generating a predicted fingerprint image according to one embodiment of the present application;

[0032] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application.

[0033] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0034] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0035] In the technical solution of this application, the user personal information involved and the collection, storage, use, processing, transmission, provision and disclosure of data are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0036] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0037] With the advancement of fingerprint recognition technology, the identification of a person's personal identity can be determined by identifying a fingerprint image. Due to the time difference between fingerprint image acquisition and actual usage, it is necessary to estimate a predicted fingerprint image based on the original fingerprint image. In existing technologies, this method typically calculates the original fingerprint image based on a preset fingerprint aging formula to generate a predicted fingerprint image. This is then used to determine the person's personal identity. However, the predicted fingerprint images generated by existing solutions suffer from poor accuracy.

[0038] The following explains the application scenarios of the embodiments of the present application:

[0039] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0040] Figure 1 A schematic diagram of a scenario of the method for generating a predicted fingerprint image provided in this application, such as Figure 1 As shown, the specific application scenario of the present application is to generate a predicted fingerprint image of the target object at a preset age based on the fingerprint image of the target object at a certain age. The execution subject of the method provided in the embodiment of the present application can be an electronic control unit, a terminal device or a server. The terminal device is used as the execution subject for explanation. For example, the terminal device obtains the original fingerprint image of the target object at the age of 12 according to the predicted fingerprint image generation method provided in the embodiment of the present application. By calling the fingerprint prediction model, according to the preset time interval of 18 years and the pre-generated target fingerprint change coefficient, the corresponding predicted fingerprint vector can be output, and then the predicted fingerprint image of the target object at the age of 30 can be obtained according to the predicted fingerprint vector.

[0041] Figure 2 A flowchart of a method for generating a predicted fingerprint image is provided in one embodiment of the present application. Figure 2 As shown, the execution subject of the predicted fingerprint image generation method provided in this embodiment can be an electronic control unit, a terminal device, or a server. For example, this embodiment uses the terminal device as the execution subject of the method of this embodiment. The predicted fingerprint image generation method provided in this embodiment includes the following steps:

[0042] Step S101: Acquire an original fingerprint image of any target object.

[0043] Step S102: pre-process the original fingerprint image to obtain a processed original fingerprint image.

[0044] Exemplarily, the terminal device receives an original fingerprint image of any target object input by a user, and then pre-processes the original fingerprint image, for example, binarizes the original fingerprint image to convert the color original fingerprint image or the grayscale original fingerprint image into a black and white original fingerprint image, thereby obtaining a processed original fingerprint image, that is, the processed original fingerprint image is a black and white original fingerprint image; wherein the algorithm used for the binarization processing is any binarization algorithm that can convert a color or grayscale original fingerprint image into a black and white original fingerprint image.

[0045] Step S103: Obtaining an original fingerprint vector from the processed original fingerprint image.

[0046] Exemplarily, after obtaining the processed original fingerprint image, the processed original fingerprint image is encoded to obtain a corresponding original fingerprint vector.

[0047] Step S104: Obtain a fingerprint prediction model corresponding to the target object.

[0048] Exemplarily, a corresponding fingerprint prediction model is obtained according to the object group to which the target object belongs; specifically, for example, there are differences in the fingerprint pattern characteristics of different object groups, and then when training the fingerprint prediction model, multiple fingerprint prediction models are trained according to different object groups respectively, and then the object group to which the target object belongs is determined based on the object characteristics of the target object, and then the fingerprint prediction model corresponding to the object group is determined, that is, the fingerprint prediction model corresponding to the target object is obtained.

[0049] In a possible implementation, the specific implementation of step S104 includes:

[0050] Step S1041: Determine the target age of the target object according to the acquisition time of the original fingerprint image.

[0051] Step S1042: Determine a fingerprint prediction model from a plurality of candidate fingerprint prediction models according to the age range corresponding to the target age.

[0052] For example, based on the acquisition time of the original fingerprint image and the target object's birth time, the target age of the target object is determined, that is, the age of the target object at the time the original fingerprint image was acquired; and then, based on the age range corresponding to the target age, a corresponding fingerprint prediction model can be determined from multiple candidate fingerprint prediction models. Specifically, for example, the candidate fingerprint prediction models include a first age range fingerprint prediction model, a second age range fingerprint prediction model, a third age range fingerprint prediction model, and a fourth age range fingerprint prediction model. Then, based on the target age of the target object determined by the acquisition time of the original fingerprint image, the age range to which the target age belongs is determined. For example, if the target age belongs to the second age range, then based on the second age range, a corresponding prediction model is selected from the first age range fingerprint prediction model, the second age range fingerprint prediction model, the third age range fingerprint prediction model, and the fourth age range fingerprint prediction model, and the corresponding fingerprint prediction model can be determined. That is, the determined fingerprint prediction model is the second age range fingerprint prediction model.

[0053] In the steps of this embodiment, the degree of change of fingerprints of people of different age groups after experiencing the same time span is different. For example, the degree of change between the fingerprint of target object A at the age of 10 and the fingerprint at the age of 30 is change_1, and the degree of change between the fingerprint of target object B at the age of 20 and the fingerprint at the age of 40 is change_2. Although the time span is 20 years, the degree of change change_1 is different from the degree of change change_2. Therefore, the fingerprint prediction model determined based on the age range to which the target age of the target object belongs can accurately generate a predicted fingerprint image in the subsequent steps because it takes into account the influence of the age difference factor.

[0054] Step S105: inputting the original fingerprint vector, the preset time interval and the pre-generated target fingerprint variation coefficient into the fingerprint prediction model to output a predicted fingerprint vector.

[0055] Exemplarily, after obtaining the original fingerprint vector and the fingerprint prediction model, the preset time interval input by the user is obtained, and the target fingerprint change coefficient is generated based on the preset time interval and the object information of the target object; then the fingerprint prediction model can output the corresponding predicted fingerprint vector based on the input original fingerprint vector, the preset time interval and the pre-generated target fingerprint change coefficient.

[0056] In one possible implementation, the process of pre-generating the target fingerprint variation coefficient includes: obtaining first geographic location information and acquisition time when the original fingerprint image is acquired based on the object information of the target object, and then determining corresponding first humidity information based on the first geographic location information and acquisition time; then determining second geographic location information where the target object is currently located (or may be located) based on a preset time interval and the object information of the target object, determining an estimated time based on the preset time interval, and then determining corresponding second humidity information based on the second geographic location information and the estimated time; and then generating the target fingerprint variation coefficient based on the first humidity information and the second humidity information. It can be understood that In the same time interval, there are differences between the fingerprint patterns of the finger fingerprint of the same target object in dry conditions and in wet conditions. Therefore, for example, when the difference between the first humidity information and the second humidity information is less than the preset threshold, the target fingerprint change coefficient is num_1, that is, when the fingerprint prediction model generates a predicted fingerprint vector, the target fingerprint change coefficient num_1 is used to calculate the influence of humidity on the texture of the finger fingerprint; when the difference between the first humidity information and the second humidity information is greater than the preset threshold, the target fingerprint change coefficient is num_2, that is, when the fingerprint prediction model generates a predicted fingerprint vector, the target fingerprint change coefficient num_2 is used to calculate the influence of humidity on the texture of the finger fingerprint.

[0057] In another possible implementation, the process of pre-generating the target fingerprint variation coefficient includes:

[0058] Step S1001: determining one or more occupation information of a target object according to a preset time interval.

[0059] Step S1002: Obtain the target fingerprint variation coefficient based on one or more occupation information.

[0060] For example, within a preset time interval, the target object may engage in one or more occupations. Due to different occupational environments and occupational contents, the degree of wear and tear on the fingerprints of the fingers is different. Therefore, according to the preset time interval, the occupational information corresponding to the one or more occupations engaged in by the target object is determined; and then, based on the determined one or more occupational information, the fingerprint variation coefficient of each occupational information is obtained; further, based on the fingerprint variation coefficient corresponding to each occupational information, the target fingerprint variation coefficient can be calculated. Specifically, for example, the preset time interval is 20 years, and the occupations of the target object are determined to include occupation job_1, occupation job_2 and occupation job_3. The occupation information corresponding to occupation job_1 is info_1, the occupation information corresponding to occupation job_2 is info_2, and the occupation information corresponding to occupation job_3 is info_3; then, the corresponding fingerprint change coefficient data_1 is obtained based on the occupation information info_1, the corresponding fingerprint change coefficient data_2 is obtained based on the occupation information info_2, and the corresponding fingerprint change coefficient data_3 is obtained based on the occupation information info_3; further, based on the fingerprint change coefficient data_1, the fingerprint change coefficient data_2, and the fingerprint change coefficient data_3, the target fingerprint change coefficient is calculated, for example, by addition calculation.

[0061] Furthermore, in another possible implementation, the specific implementation steps of step S1002 include:

[0062] Step S10021: According to one or more occupation information, obtain the corresponding occupation engagement time and the corresponding fingerprint variation coefficient.

[0063] Step S10022: for each occupation information, generate an actual fingerprint variation coefficient according to the corresponding occupation duration and the corresponding fingerprint variation coefficient.

[0064] Step S10023: Obtain the target fingerprint variation coefficient according to the actual fingerprint variation coefficient corresponding to each occupation information.

[0065] For example, due to different occupational environments and occupational contents, the degree of wear and tear on finger fingerprints is different. At the same time, the length of time each occupation is engaged in also affects the degree of wear and tear. Therefore, based on one or more occupational information determined for the target object, the corresponding occupational engagement length and the corresponding fingerprint change coefficient are determined respectively; then, according to the occupational engagement length of each occupation by the target object and the corresponding fingerprint change coefficient, the actual fingerprint change coefficient corresponding to each occupational information is calculated, and then the target fingerprint change coefficient is calculated.

[0066] Specifically, for example, the preset time interval is 20 years, and the occupations of the target object are determined to include occupations job_1, occupation job_2, and occupation job_3. The occupation information corresponding to occupation job_1 is info_1, the occupation information corresponding to occupation job_2 is info_2, and the occupation information corresponding to occupation job_3 is info_3. Then, according to the occupation information info_1, the corresponding fingerprint variation coefficient data_1 is obtained, and the corresponding occupation duration is time_1. According to the occupation information info_2, the corresponding fingerprint variation coefficient data_2 is obtained, and the corresponding occupation duration is time_2. According to the occupation information info_3, the corresponding fingerprint variation coefficient data_3 is obtained, and the corresponding occupation duration is time_ 3; then, based on the occupational engagement time_1 and the fingerprint variation coefficient data_1, the actual fingerprint variation coefficient r_data_1 corresponding to the occupational information info_1 is calculated; based on the occupational engagement time_2 and the fingerprint variation coefficient data_2, the actual fingerprint variation coefficient r_data_2 corresponding to the occupational information info_2 is calculated; based on the occupational engagement time_3 and the fingerprint variation coefficient data_3, the actual fingerprint variation coefficient r_data_3 corresponding to the occupational information info_3 is calculated; further, based on the actual fingerprint variation coefficient r_data_1, the actual fingerprint variation coefficient r_data_2, and the actual fingerprint variation coefficient r_data_3, the target fingerprint variation coefficient is calculated, for example, by summing up.

[0067] In the steps of this embodiment, by obtaining one or more occupations engaged in by the target object within a preset time interval, the impact of each occupation on the fingerprint is determined, and then the target fingerprint variation coefficient is generated based on all the occupations engaged in by the target object within the preset time interval, so that the fingerprint prediction model can output an accurate predicted fingerprint vector based on the original fingerprint vector, the preset time interval and the pre-generated target fingerprint variation coefficient.

[0068] Step S106: Generate a predicted fingerprint image based on the predicted fingerprint vector.

[0069] For example, after obtaining the predicted fingerprint vector output by the fingerprint prediction model, the terminal device may encode the predicted fingerprint vector to generate a corresponding predicted fingerprint image.

[0070] Among them, the model training process of the fingerprint prediction model includes: obtaining a first fingerprint vector corresponding to the first fingerprint image of the training object, a second fingerprint vector corresponding to the second fingerprint image of the training object, and a pre-generated target fingerprint change coefficient, wherein the time difference between the acquisition time of the first fingerprint image and the acquisition time of the second fingerprint image is N years; then the initial fingerprint prediction model outputs a test fingerprint vector based on the input time difference N years, the first fingerprint vector, and the pre-generated target fingerprint change coefficient; then calculating the spatial distance between the test fingerprint vector and the second fingerprint vector, and using the calculated spatial distance as the model loss, optimizing the initial fingerprint prediction model to obtain the fingerprint prediction model corresponding to the target object obtained in the embodiment of the present application.

[0071] For example, in one possible implementation, the core architecture of the fingerprint prediction model is implemented based on the network model ResNet-101.

[0072] Optionally, the data set used in the model training process of the fingerprint prediction model is determined from the national standard fingerprint database.

[0073] In this embodiment, based on obtaining the original fingerprint image of any target object, the original fingerprint image is preprocessed to obtain the original fingerprint vector from the processed original fingerprint image; further, a fingerprint prediction model corresponding to the target object is obtained to enable the fingerprint prediction model to output a predicted fingerprint vector based on the input original fingerprint vector, a preset time interval and a pre-generated target fingerprint variation coefficient; and then, a corresponding predicted fingerprint image is generated based on the predicted fingerprint vector. This solves the problem of poor accuracy of the predicted fingerprint image generated based on the solution of the existing technology.

[0074] Figure 3 A flowchart of a method for generating a predicted fingerprint image is provided in another embodiment of the present application. Figure 3 As shown, the predicted fingerprint image generation method provided by this embodiment is Figure 2 Based on the predicted fingerprint image generation method provided in the illustrated embodiment, step S102 is further refined. The predicted fingerprint image generation method provided in this embodiment includes the following steps:

[0075] Step S201: obtaining an original fingerprint image of any target object, where the original fingerprint image includes a first original fingerprint image and a second original fingerprint image. The first original fingerprint image and the second original fingerprint image are two fingerprint images of the target object at different positions in the same time interval.

[0076] Step S202: performing noise reduction processing on the first original fingerprint image to generate a first processed image.

[0077] Step S203: performing noise reduction processing on the second original fingerprint image to generate a second processed image.

[0078] Exemplarily, the terminal device performs noise reduction processing on the first original fingerprint image and the second original fingerprint image respectively to reduce the noise in the image, thereby obtaining a first processed image and a second processed image; wherein the noise reduction processing includes noise reduction using a median filter and noise reduction using a mean filter.

[0079] Step S204 , obtaining a common image area, a first non-common image area corresponding to the first processed image, and a second non-common image area corresponding to the second processed image according to the image area of the first processed image and the image area of the second processed image.

[0080] Step S205 : determining the size relationship between the area of the first non-public image region and the area of the second non-public image region.

[0081] Exemplarily, after obtaining the first processed image and the second processed image, the image areas of the first processed image and the second processed image are divided based on the image features in the image areas of the first processed image and the image features in the image areas of the second processed image, thereby obtaining a common image area corresponding to both the first processed image and the second processed image, a first non-public image area corresponding to the first processed image, and a second non-public image area corresponding to the second processed image. Furthermore, the area of the first non-public image area is calculated to obtain the area of the first non-public image area, and the area of the second non-public image area is calculated to obtain the area of the second non-public image area, thereby determining the size relationship between the area of the first non-public image area and the area of the second non-public image area.

[0082] Step S206: If the area of the first non-public image area is smaller than the area of the second non-public image area, the area image corresponding to the first non-public image area is corrected according to the area image of the public image area corresponding to the first processed image and the area image of the public image area corresponding to the second processed image to generate a first corrected image.

[0083] Step S207: Obtain a processed original fingerprint image according to the first corrected image and the second processed image.

[0084] Exemplarily, if the area of the first non-public image area is smaller than the area of the second non-public image area, the second processed image is used as a reference image, that is, the regional image of the public image area corresponding to the second processed image is used as a reference image, and then according to the degree of distortion of the regional image of the public image area corresponding to the first processed image relative to the regional image of the public image area corresponding to the second processed image, the regional image corresponding to the first non-public image area is corrected to generate a first corrected image; further, based on the first corrected image and the second processed image, image fusion and splicing are performed to obtain the processed original fingerprint image.

[0085] In a possible implementation, the specific implementation of step S206 includes:

[0086] Step S2061 : generating a first tensor group according to the regional image of the common image area corresponding to the first processed image and the regional image of the common image area corresponding to the second processed image.

[0087] Step S2062 : Correcting the regional image corresponding to the first non-public image region according to the first tensor group to generate a first corrected image.

[0088] Exemplarily, based on the fact that the area of the first non-public image area is smaller than the area of the second non-public image area, the regional image of the public image area corresponding to the second processed image is used as a reference image, and then for the same fingerprint feature point, the longitudinal distortion value and the lateral distortion value of the pixel position of the regional image of the public image area corresponding to the first processed image relative to the pixel position of the regional image of the public image area corresponding to the second processed image are calculated, and then a first tensor group is generated according to the longitudinal distortion value and the lateral distortion value corresponding to each fingerprint feature point; further, according to the change trend between each distortion coefficient in the first tensor group, a first predicted tensor group corresponding to the first non-public image area is generated, and then according to the first predicted tensor group, the pixel position in the regional image corresponding to the first non-public image area is corrected to generate a first corrected image.

[0089] Step S208: If the area of the first non-public image area is greater than or equal to the area of the second non-public image area, the area image corresponding to the second non-public image area is corrected according to the area image of the public image area corresponding to the first processed image and the area image of the public image area corresponding to the second processed image to generate a second corrected image.

[0090] Step S209: Obtain a processed original fingerprint image according to the second corrected image and the first processed image.

[0091] Exemplarily, if the area of the first non-public image area is greater than or equal to the area of the second non-public image area, the first processed image is used as a reference image, that is, the regional image of the public image area corresponding to the first processed image is used as a reference image, and then according to the degree of distortion of the regional image of the public image area corresponding to the second processed image relative to the regional image of the public image area corresponding to the first processed image, the regional image corresponding to the second non-public image area is corrected to generate a second corrected image; further, image fusion and splicing are performed based on the second corrected image and the first processed image to obtain the processed original fingerprint image.

[0092] In a possible implementation, the specific implementation of step S208 includes:

[0093] Step S2081 : generating a second tensor group according to the regional image of the common image area corresponding to the first processed image and the regional image of the common image area corresponding to the second processed image.

[0094] Step S2082: Correct the regional image corresponding to the second non-public image region according to the second tensor group to generate a second corrected image.

[0095] Exemplarily, based on the fact that the area of the first non-public image area is greater than or equal to the area of the second non-public image area, the regional image of the public image area corresponding to the first processed image is used as a reference image, and then for the same fingerprint feature point, the longitudinal distortion value and the lateral distortion value of the pixel position of the regional image of the public image area corresponding to the second processed image relative to the pixel position of the regional image of the public image area corresponding to the first processed image are calculated, and then a second tensor group is generated based on the longitudinal distortion value and the lateral distortion value corresponding to each fingerprint feature point; further, based on the change trend between each distortion coefficient in the second tensor group, a second predicted tensor group corresponding to the second non-public image area is generated, and then based on the second predicted tensor group, the pixel position in the regional image corresponding to the second non-public image area is corrected to generate a second corrected image.

[0096] In the steps of this embodiment, two fingerprint images of the target object at different positions in the same time interval are subjected to distortion correction and splicing to obtain a fused fingerprint image, that is, a processed original fingerprint image. By means of distortion correction and image splicing, the image information of the fingerprint image is expanded, which provides rich data for generating a predicted fingerprint image in the subsequent steps and improves the prediction accuracy of the generated predicted fingerprint image.

[0097] Step S210: Obtain an original fingerprint vector from the processed original fingerprint image.

[0098] Step S211: Obtain a fingerprint prediction model corresponding to the target object.

[0099] Step S212: inputting the original fingerprint vector, the preset time interval and the pre-generated target fingerprint variation coefficient into the fingerprint prediction model to output a predicted fingerprint vector.

[0100] Step S213: Generate a predicted fingerprint image based on the predicted fingerprint vector.

[0101] In this embodiment, the implementation of steps S210 to S213 is the same as that of the present application. Figure 2 The implementation methods of steps S103 to S106 in the illustrated embodiment are the same and will not be described in detail here.

[0102] Figure 4 A schematic diagram of the structure of a predicted fingerprint image generation device provided in one embodiment of the present application is shown as follows: Figure 4 As shown, the predicted fingerprint image generation device 3 provided in this embodiment includes:

[0103] An acquisition module 31 is used to acquire an original fingerprint image of any target object;

[0104] The processing module 32 is configured to pre-process the original fingerprint image to obtain a processed original fingerprint image; obtain an original fingerprint vector from the processed original fingerprint image; obtain a fingerprint prediction model corresponding to the target object; and input the original fingerprint vector, a preset time interval, and a pre-generated target fingerprint variation coefficient into the fingerprint prediction model to output a predicted fingerprint vector.

[0105] The generating module 33 is configured to generate a predicted fingerprint image according to the predicted fingerprint vector.

[0106] In a possible embodiment, during the pre-generation process of the target fingerprint variation coefficient, the predicted fingerprint image generation device 3 is specifically used to: determine one or more occupational information of the target object according to a preset time interval; and obtain the target fingerprint variation coefficient according to the one or more occupational information.

[0107] In a possible embodiment, when the predicted fingerprint image generation device 3 obtains the target fingerprint variation coefficient based on one or more occupational information, it is specifically used to: obtain the corresponding occupational engagement time and the corresponding fingerprint variation coefficient based on one or more occupational information; for each occupational information, generate the actual fingerprint variation coefficient based on the corresponding occupational engagement time and the corresponding fingerprint variation coefficient; obtain the target fingerprint variation coefficient based on the actual fingerprint variation coefficient corresponding to each occupational information.

[0108] In a possible implementation, when the processing module 32 obtains the fingerprint prediction model corresponding to the target object, it is specifically used to: determine the target age of the target object based on the acquisition time of the original fingerprint image; and determine the fingerprint prediction model from multiple candidate fingerprint prediction models based on the age range corresponding to the target age.

[0109] In a possible embodiment, the original fingerprint image includes a first original fingerprint image and a second original fingerprint image, and the first original fingerprint image and the second original fingerprint image are two fingerprint images of the target object at different positions in the same time interval; when the processing module 32 pre-processes the original fingerprint image to obtain the processed original fingerprint image, it is specifically used to: perform noise reduction processing on the first original fingerprint image to generate a first processed image; perform noise reduction processing on the second original fingerprint image to generate a second processed image; obtain a common image area, a first non-common image area corresponding to the first processed image, and a second non-common image area corresponding to the second processed image based on the image area of the first processed image and the image area of the second processed image; if the area of the first non-common image area is smaller than If the area of the first non-public image area is greater than or equal to the area of the second non-public image area, then the area image corresponding to the first non-public image area is corrected according to the area image of the public image area corresponding to the first processed image and the area image of the public image area corresponding to the second processed image to generate a first corrected image; based on the first corrected image and the second processed image, a processed original fingerprint image is obtained; if the area of the first non-public image area is greater than or equal to the area of the second non-public image area, then the area image corresponding to the second non-public image area is corrected according to the area image of the public image area corresponding to the first processed image and the area image of the public image area corresponding to the second processed image to generate a second corrected image; based on the second corrected image and the first processed image, a processed original fingerprint image is obtained.

[0110] In a possible embodiment, when the processing module 32 corrects the regional image corresponding to the first non-public image area based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a first corrected image, it is specifically used to: generate a first tensor group based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image; correct the regional image corresponding to the first non-public image area based on the first tensor group to generate a first corrected image; when the processing module 32 corrects the regional image corresponding to the second non-public image area based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a second corrected image, it is specifically used to: generate a second tensor group based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image; correct the regional image corresponding to the second non-public image area based on the second tensor group to generate a second corrected image.

[0111] The acquisition module 31, the processing module 32 and the generation module 33 are connected in sequence. The predicted fingerprint image generation device 3 provided in this embodiment can perform the following steps: Figure 2-Figure 3 The technical solutions of any of the method embodiments shown have similar implementation principles and technical effects, which will not be described in detail here.

[0112] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0113] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 performs the above method.

[0114] The specific implementation process of the processor 501 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0115] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0116] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0117] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0118] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0119] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0120] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0121] An exemplary 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 an integral part 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 a device as discrete components.

[0122] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0123] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0125] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0126] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0127] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for generating a predicted fingerprint image, characterized in that: include: Obtain the original fingerprint image of any target object; Preprocessing the original fingerprint image to obtain a processed original fingerprint image; Obtaining an original fingerprint vector from the processed original fingerprint image; Obtaining a fingerprint prediction model corresponding to the target object; Inputting the original fingerprint vector, a preset time interval and a pre-generated target fingerprint variation coefficient into the fingerprint prediction model to output a predicted fingerprint vector; A predicted fingerprint image is generated according to the predicted fingerprint vector.

2. The method according to claim 1, characterized in that The pre-generation process of the target fingerprint variation coefficient includes: Determining one or more occupation information of the target object according to the preset time interval; The target fingerprint variation coefficient is obtained according to the one or more occupation information.

3. The method according to claim 2, characterized in that Obtaining the target fingerprint variation coefficient according to the one or more occupation information includes: According to the one or more occupation information, obtaining the corresponding occupation engagement time and the corresponding fingerprint variation coefficient; For each occupation information, the actual fingerprint variation coefficient is generated according to the corresponding occupation duration and the corresponding fingerprint variation coefficient; The target fingerprint variation coefficient is obtained according to the actual fingerprint variation coefficient corresponding to each occupation information.

4. The method according to claim 1, wherein The obtaining of the fingerprint prediction model corresponding to the target object includes: determining the target age of the target object according to the acquisition time of the original fingerprint image; The fingerprint prediction model is determined from a plurality of candidate fingerprint prediction models according to an age range corresponding to the target age.

5. The method according to any one of claims 1 to 4, characterized in that The original fingerprint image includes a first original fingerprint image and a second original fingerprint image, wherein the first original fingerprint image and the second original fingerprint image are two fingerprint images of the target object at different positions in the same time interval; The preprocessing of the original fingerprint image to obtain a processed original fingerprint image includes: Performing noise reduction processing on the first original fingerprint image to generate a first processed image; performing noise reduction processing on the second original fingerprint image to generate a second processed image; obtaining, according to the image area of the first processed image and the image area of the second processed image, a common image area, a first non-common image area corresponding to the first processed image, and a second non-common image area corresponding to the second processed image; If the area of the first non-public image region is smaller than the area of the second non-public image region, correcting the area image corresponding to the first non-public image region based on the area image of the public image region corresponding to the first processed image and the area image of the public image region corresponding to the second processed image to generate a first corrected image; Obtaining the processed original fingerprint image according to the first corrected image and the second processed image; If the area of the first non-public image region is greater than or equal to the area of the second non-public image region, correcting the area image corresponding to the second non-public image region based on the area image of the public image region corresponding to the first processed image and the area image of the public image region corresponding to the second processed image to generate a second corrected image; The processed original fingerprint image is obtained according to the second corrected image and the first processed image.

6. The method according to claim 5, characterized in that Correcting the regional image corresponding to the first non-public image area based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a first corrected image includes: generating a first tensor group according to a regional image of a common image region corresponding to the first processed image and a regional image of a common image region corresponding to the second processed image; Correcting the regional image corresponding to the first non-public image region according to the first tensor group to generate the first corrected image; Correcting the regional image corresponding to the second non-public image area based on the regional image of the public image area corresponding to the first processed image and the regional image of the public image area corresponding to the second processed image to generate a second corrected image includes: generating a second tensor group according to a regional image of a common image region corresponding to the first processed image and a regional image of a common image region corresponding to the second processed image; Correcting the regional image corresponding to the second non-public image region according to the second tensor group to generate the second corrected image.

7. A predicted fingerprint image generation device, characterized in that: include: An acquisition module, used to acquire an original fingerprint image of any target object; A processing module, configured to pre-process the original fingerprint image to obtain a processed original fingerprint image; Obtaining an original fingerprint vector from the processed original fingerprint image; Obtaining a fingerprint prediction model corresponding to the target object; inputting the original fingerprint vector, a preset time interval, and a pre-generated target fingerprint variation coefficient into the fingerprint prediction model to output a predicted fingerprint vector; A generating module is used to generate a predicted fingerprint image according to the predicted fingerprint vector.

8. 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 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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