TWI818496B - Fingerprint recognition method, fingerprint module, and electronic device - Google Patents

Abstract

A fingerprint recognition method, a fingerprint module, and an electronic device are provided. The method includes: acquiring at least one first template fingerprint image of a user; performing degradation processing on the first template fingerprint image to obtain at least one second template fingerprint image; acquiring the fingerprint image to be recognized, verifying the fingerprint image to be recognized based on the at least one first template fingerprint image and at least one second template fingerprint image.

Description

Fingerprint identification methods, fingerprint modules and electronic devices

The present application relates to the field of fingerprint identification technology, and in particular to a fingerprint identification method, fingerprint module and electronic device.

Nowadays, fingerprint verification is a commonly used user identity verification method for smart electronic devices such as smartphones and personal computers. It can be used in application scenarios such as unlocking, payment, and login of electronic devices. The success rate of fingerprint recognition is an important indicator to measure the quality of fingerprint recognition algorithms. In fingerprint recognition scenarios, fingerprint modules are easily affected by noise and external environments. For example, users’ fingers are often stained with dust, water, stains, etc. Foreign objects cause poor quality of the collected fingerprint images, thereby reducing the success rate of fingerprint matching. A more commonly used method is to enhance the collected fingerprint images. However, while image enhancement reduces the impact of noise interference, it will also damage the details of the fingerprints in the image, thereby reducing the success rate of fingerprint matching.

In view of this, it is necessary to provide a fingerprint identification method, fingerprint module and electronic equipment to solve the problem that the above fingerprint module is easily affected by noise and external environment, resulting in poor quality of collected fingerprint images, thereby reducing the success rate of fingerprint matching. technical issues.

This application provides a fingerprint identification method, which method includes: Obtain at least one first template fingerprint image of the user; perform degradation processing on the first template fingerprint image to obtain at least a second template fingerprint image; obtain the fingerprint image to be identified, based on the at least one first template fingerprint image The template fingerprint image and the at least one second template fingerprint image verify the fingerprint image to be recognized.

Optionally, the obtaining at least one first template fingerprint image of the user includes: collecting the fingerprint of at least one finger of the user when entering the template fingerprint, and generating the at least one first template fingerprint image.

Optionally, obtaining at least one first template fingerprint image of the user includes: during fingerprint verification, collecting the fingerprint of at least one finger of the user and generating a fingerprint image of the at least one finger; Perform identification to determine whether the fingerprint image matches the at least one first template fingerprint image; if it is determined that the fingerprint image matches the at least one template fingerprint image, use the fingerprint image as the The first template fingerprint image.

Optionally, performing degradation processing on the first template fingerprint image includes: performing degradation processing on the first template fingerprint image based on a fuzzy degradation model, and calculating each image in the first template fingerprint image. The primitive average value of a preset number of primitive points around the primitive point is used to replace the primitive value of the primitive point with the primitive average value.

Optionally, performing degradation processing on the first template fingerprint image includes: performing degradation processing on the first template fingerprint image based on a random noise degradation model, setting a numerical range of noise, and converting the numerical value into Noise within the range is randomly superimposed on the first template fingerprint image.

Optionally, performing degradation processing on the first template fingerprint image includes: The first template fingerprint image is input into a trained neural network model, and the first template fingerprint image is degraded through the trained neural network model.

Optionally, performing degradation processing on the first template fingerprint image includes: performing degradation processing on the first template fingerprint image using each image degradation model to obtain a preset number of second template fingerprint images. picture.

Optionally, the verification of the fingerprint image to be identified based on the at least one first template fingerprint image and the at least one second template fingerprint image includes: determining the fingerprint image to be identified. Whether it matches at least one of the at least one first template fingerprint image and the at least one second template fingerprint image; if it is determined that the fingerprint image to be identified matches the at least one first template fingerprint image. The template fingerprint image matches at least one of the at least one second template fingerprint image, and it is determined that the fingerprint image to be identified passes the verification; or if it is determined that the fingerprint image to be identified matches the fingerprint image to be identified, If all the template fingerprint images in the at least one first template fingerprint image and the at least one second template fingerprint image do not match, it is determined that the verification of the fingerprint image to be identified fails.

This application also provides a fingerprint module for performing the above fingerprint identification method.

This application also provides an electronic device, including: a processor; and a memory. A plurality of program modules are stored in the memory. The plurality of program modules are loaded by the processor and execute the above-mentioned fingerprint recognition. method.

The fingerprint identification method, fingerprint module and electronic device of the present application perform degradation processing on the template fingerprint image in the electronic device to expand the template fingerprint image and at the same time compare the fingerprint image with poor quality. In this way, in fingerprint recognition In the process, even if it is affected by noise and external environment As a result, the quality of the collected fingerprint images is poor, but the matching can still be successful, thereby improving the success rate of fingerprint matching and improving user experience.

1: Electronic equipment

10: Processor

20:Memory

30:Computer program

40:Fingerprint module

2:Server

S201-S203: Steps

Figure 1 is a schematic diagram of the application environment architecture of the fingerprint identification method provided by the preferred embodiment of the present application.

Figure 2 is a flow chart of the fingerprint identification method provided by the preferred embodiment of the present application.

Figure 3 is a schematic diagram of a first template fingerprint image and a second template fingerprint image that has been subjected to blur degradation processing provided by the preferred embodiment of the present application.

FIG. 4 is a schematic diagram of the first template fingerprint image and the second template fingerprint image that have undergone random noise degradation processing provided by the preferred embodiment of the present application.

Figure 5 is a schematic structural diagram of an electronic device provided by a preferred embodiment of the present application.

In order to more clearly understand the above objects, features and advantages of the present application, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, as long as there is no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

Many specific details are set forth in the following description to facilitate a full understanding of the present application. The described embodiments are only some, rather than all, of the embodiments of the present application. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing specific embodiments only and is not intended to limit the application.

Please refer to FIG. 1 , which is a schematic diagram of the application environment architecture of the fingerprint identification method provided by the preferred embodiment of the present application.

The fingerprint identification method in this application is applied to an electronic device 1. The electronic device 1 can establish a communication connection with at least one server 2 through a network. The network may be a wired network or a wireless network, such as radio, wireless fidelity (Wireless Fidelity, WIFI), cellular, satellite, broadcast, etc. The cellular network can be a 4G network or a 5G network.

The electronic device 1 may be an electronic device installed with a fingerprint recognition program, such as a smart phone, a personal computer, a server, etc., wherein the server may be a single server, a server cluster, etc. The server 2 may be a single server, a server cluster, etc.

Please refer to Figure 2, which is a flow chart of a fingerprint identification method provided in a preferred embodiment of the present application. According to different needs, the order of steps in the flow chart can be changed, and some steps can be omitted.

S201. Obtain at least one first template fingerprint image of the user.

In one embodiment, obtaining at least one first template fingerprint image of the user includes: collecting the fingerprint of at least one finger of the user when entering the template fingerprint, and generating the at least one first template fingerprint image.

For example, when a user uses a newly purchased electronic device, he or she needs to enter a template fingerprint after turning it on, or when the user needs to add a finger that can be used for fingerprint verification, the user can use the finger to touch the fingerprint module, and the fingerprint module collects at least one of the user's fingerprints. a fingerprint of a finger, and generate the at least one first fingerprint image.

In one embodiment, obtaining at least one first template fingerprint image of the user further includes: during fingerprint verification, collecting the fingerprint of at least one finger of the user, generating a fingerprint image of the at least one finger, and analyzing the fingerprint image. to identify the fingerprint image and determine whether the fingerprint image matches the at least one The first template fingerprint image matches. If it is determined that the fingerprint image matches the at least one template fingerprint image, the fingerprint image is used as the first template fingerprint image.

It should be noted that during the fingerprint verification process, the user's fingers are affected by the external environment and are easily stained with dust, water, stains and other foreign matter. If the fingers are stained with foreign matter, the fingerprint image can still match the template. Fingerprint image matching indicates that the finger fingerprint image can be used as a template fingerprint image. In this way, when the subsequent fingerprint module collects finger fingerprint images that are also stained with foreign matter, the success rate of fingerprint image matching can be improved.

In another embodiment, obtaining at least one first template fingerprint image of the user may include: obtaining a pre-entered template fingerprint image from a memory of the electronic device as the first template fingerprint image.

In another embodiment, obtaining at least one first template fingerprint image of the user may include: obtaining the template fingerprint image from a cloud server through the network as the first template fingerprint image. It should be noted that the user can upload the template fingerprint image recorded in the old electronic device to the cloud server. In this way, when using the new electronic device, the electronic device can be used to download and obtain the template fingerprint image from the cloud server. , there is no need for users to repeatedly enter template fingerprint images, and the loss of template data is avoided.

S202: Perform degradation processing on the first template fingerprint image to obtain at least one second template fingerprint image.

In one embodiment, performing degradation processing on the first template fingerprint image to obtain at least one second template fingerprint image includes: performing mean fuzzy degradation processing on the first template fingerprint image based on a fuzzy degradation model, At least one second template fingerprint image is obtained.

Specifically, calculate the primitive average value of a preset number of primitive points around each primitive point in the first template fingerprint image, and replace the primitive value of the primitive point with the primitive average value , thereby obtaining at least one second template fingerprint image. Wherein, the preset number is x*x. Optionally, x is 3. please Refer to Figure 3, which shows the first template fingerprint image before mean blur processing and the second template fingerprint image after mean blur processing.

In one embodiment, performing degradation processing on the first template fingerprint image to obtain at least one second template fingerprint image further includes: performing degradation processing on the first template fingerprint image based on a random noise degradation model, At least one second template fingerprint image is obtained.

Specifically, a numerical range of noise is set, and noise within the numerical range is randomly superimposed on the first template fingerprint image, thereby obtaining at least one second template fingerprint image. In one implementation, the numerical range of the noise can be a range of primitive values. The numerical range of the noise can be obtained by setting the maximum primitive value and the minimum primitive value of the noise, and then random primitives are value is superimposed onto the first template fingerprint image. In another embodiment, the numerical range of the noise (signal-to-noise ratio) may also be a decibel value range. Please refer to Figure 4, which shows the first template fingerprint image before random noise processing and the second template fingerprint image after random noise processing.

In one embodiment, performing degradation processing on the first template fingerprint image to obtain at least one second template fingerprint image includes: inputting the first template fingerprint image into a trained neural network model, by The trained neural network model performs degradation processing on the first template fingerprint image.

In one embodiment, a neural network model is trained by deep learning using a training data set, so that the neural network model learns the degradation process from high quality to low quality and learns the feature distribution of low quality images. Wherein, the training data set includes a plurality of high-quality fingerprint images and a plurality of low-quality fingerprint images. Optionally, the training data set can be obtained from a server through the network. In other embodiments, it is also possible to receive fingerprint images that are not stained with foreign matter and fingerprint images that are stained with foreign matter that have been entered multiple times by the user, and use the plurality of fingerprint images that are not stained with foreign matter as high-level results in the training data set. For high-quality fingerprint images, multiple fingerprint images stained with foreign matter are used as low-quality fingerprint images in the training data set.

In one embodiment, the neural network model is a convolutional neural network model. The parameters of the neural network model include convolution layer parameters, weights, learning rates, number of iterations, etc. The convolution layer parameters include convolution layer parameters. The number of layers, convolution kernel size, convolution step size and number of filling layers. The backbone network of the convolutional neural network of the neural network model can adopt network models such as AlexNet, VGG16, GoogleNet, ResNet, DenseNet, MobileNets, Ghosnet, etc.

Specifically, the training process of the neural network model includes: setting the initial parameters of the neural network model, using high-quality images in the training data set as input data of the neural network model, and using low-quality images as input data for the neural network model. As the output data of the neural network model, multiple sets of training data consisting of one high-quality image and at least one low-quality image are logged into the neural network model, and the neural network model extracts high-quality images. The characteristics of the image and the characteristics of the low-quality image, and establish the mapping relationship between the high-quality image and the low-quality image. Through repeated operations of multiple sets of training data, the neural network model updates the mapping relationship between high-quality images and low-quality images until the output value of the loss function is less than the output value, thereby generating a trained neural network model. Network model.

Specifically, performing degradation processing on the first template fingerprint image through the trained neural network model includes: inputting the first template fingerprint image into the trained neural network model, the The trained neural network model extracts the features of the first template fingerprint image (high-quality image) and generates the features of the low-quality image based on the mapping relationship between the high-quality image and the low-quality image. , that is, the characteristics of the second template fingerprint image are fused with the generated characteristics of the second template fingerprint image to obtain the second template fingerprint image.

In one embodiment, performing degradation processing on the first template fingerprint image to obtain at least one second template fingerprint image further includes: performing degradation processing on the first template fingerprint image using each image degradation model, To obtain a preset number of second template fingerprint images.

In one embodiment, the above-mentioned image degradation model can perform degradation processing on all areas or part of the first template fingerprint image to obtain the second template fingerprint image.

S203: Obtain the fingerprint image to be identified, and verify the fingerprint image to be identified based on the at least one first template fingerprint image and the at least one second template fingerprint image.

In one embodiment, in scenarios such as unlocking, payment, and login that require fingerprint verification, the fingerprint image of the user is collected through a fingerprint module, and the collected fingerprint image is identified and verified.

In one embodiment, verifying the fingerprint image to be identified based on the at least one first template fingerprint image and the at least one second template fingerprint image includes: determining the fingerprint image to be identified Whether it matches at least one template fingerprint image among the at least one first template fingerprint image and the at least one second template fingerprint image. If it is determined that the fingerprint image to be identified matches at least one of the at least one first template fingerprint image and the at least one second template fingerprint image, determine the fingerprint image to be identified. Like verified. If it is determined that the fingerprint image to be identified does not match all template fingerprint images in the at least one first template fingerprint image and the at least one second template fingerprint image, determine that the fingerprint to be identified is Image verification failed.

It should be noted that since the second template fingerprint image is generated based on the degradation of the first template fingerprint image, the difference between the second template fingerprint image and the first template fingerprint image is only in the figure. Like the difference in quality, the fingerprint texture remains unchanged. In this way, it can be used as a template fingerprint image. On the premise of ensuring privacy and security, the template fingerprint image can be expanded to compare with fingerprint images under different noise or external environments to improve the quality of the fingerprint image. The fingerprint matching success rate in the fingerprint verification process is improved.

Please refer to FIG. 5 , which is a schematic structural diagram of an electronic device provided by a preferred embodiment of the present application.

The electronic device 1 includes, but is not limited to, a processor 10 , a memory 20 , a computer program 30 stored in the memory 20 and executable on the processor 10 , and a fingerprint module 40 . For example, the computer program 30 is a fingerprint recognition program. When the processor 10 executes the computer program 30 , the steps in the fingerprint identification method are implemented, such as steps S201 to S203 shown in FIG. 2 .

Exemplarily, the computer program 30 can be divided into one or a plurality of modules/units, and the one or a plurality of modules/units are stored in the memory 20 and executed by the processor 10 , to complete this application. The one or multiple modules/units may be a series of computer program instruction segments capable of completing specific functions. The instruction segments are used to describe the execution process of the computer program 30 in the electronic device 1 .

Those skilled in the art can understand that the schematic diagram is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1. It may include more or fewer components than shown in the diagram, or some components may be combined or different. Components, for example, the electronic device 1 may also include input and output devices, network access devices, buses, etc.

The processor 10 may be a central processing unit (CPU), or other general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC). , off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor 10 may be any conventional processor, etc. The processor 10 is the control center of the electronic device 1 and uses various interfaces and lines to connect the entire electronic device 1 various parts.

The memory 20 can be used to store the computer program 30 and/or module/unit, and the processor 10 runs or executes the computer program and/or module/unit stored in the memory 20, and calling the data stored in the memory 20 to realize various functions of the electronic device 1 . The memory 20 may mainly include a storage program area and a data storage area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; and a storage data area. The area can store data created based on the use of the electronic device 1 (such as audio data, phone books, etc.). In addition, the memory 20 may include volatile and non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital, SD) card, flash memory card (Flash Card), at least one disk memory device, flash memory device, or other memory device.

The fingerprint module 40 at least includes a fingerprint collection unit and an image generation unit. The fingerprint collection unit is used to accept the touch of a finger and collect finger fingerprints. The image generation unit is used to generate a fingerprint based on the collected finger fingerprint lines. images.

In one embodiment, the fingerprint module 40 is an independent fingerprint identification chip and can independently execute the fingerprint identification method, that is, it can independently implement steps S201 to S203 shown in FIG. 2 .

If the integrated modules/units of the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the above embodiment methods by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in the form of original program code, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a flash drive, a mobile hard drive, a magnetic disk, an optical disk, computer memory, and read-only memory (ROM, Read-only memory). Only Memory), random access memory (RAM, Random Access Memory).

The fingerprint identification method, fingerprint module and electronic device provided by this application perform degradation processing on the template fingerprint image in the electronic device, while expanding the template fingerprint image, and at the same time, compare the fingerprint image with poor quality. In this way, in the fingerprint During the identification process, even if the quality of the collected fingerprint images is poor due to the influence of noise and external environment, the matching can still be successful, thereby increasing the success rate of fingerprint matching and improving user experience.

It is obvious to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, and can be embodied in other specific forms without departing from the spirit or essential characteristics of the present application. Form implementation of this application. Therefore, the embodiments should be regarded as illustrative and non-restrictive from any point of view, and the scope of the present application is defined by the appended patent scope rather than the above description, and it is therefore intended that the scope of the application fall within the scope of the application. All changes within the meaning and scope of equivalent elements of the patent scope are included in this application. Any reference signs in a claim shall not be construed as limiting the scope of the claim concerned. Furthermore, it is clear that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device patent scope may also be implemented by the same unit or device through software or hardware. Words such as first and second are used to indicate names and do not indicate any specific order.

To sum up, this invention meets the requirements for an invention patent and a patent application should be filed in accordance with the law. However, the above are only the preferred embodiments of the present invention. Anyone familiar with the art of this case will find equivalent modifications or changes made in accordance with the spirit of the present invention, and they should be covered by the following patent applications.

S201-S203: Steps

Claims (9)

A fingerprint identification method, applied to electronic devices, wherein the method includes: obtaining at least a first template fingerprint image of the user; inputting the at least one first template fingerprint image into a trained neural network model, whereby The trained neural network model performs degradation processing on the at least one first template fingerprint image to obtain at least a second template fingerprint image; the fingerprint image to be identified is obtained, based on the at least one first template fingerprint image. The template fingerprint image and the at least one second template fingerprint image verify the fingerprint image to be recognized. The fingerprint identification method according to claim 1, wherein obtaining at least one first template fingerprint image of the user includes: when entering the template fingerprint, collecting the fingerprint of at least one finger of the user, and generating the at least one first template fingerprint image. Template fingerprint image. The fingerprint identification method according to claim 2, wherein obtaining at least one first template fingerprint image of the user includes: during fingerprint verification, collecting the fingerprint of at least one finger of the user and generating the fingerprint of the at least one finger. image; identify the fingerprint image and determine whether the fingerprint image matches the at least one first template fingerprint image; if it is determined that the fingerprint image matches the at least one template fingerprint image, The fingerprint image is used as the first template fingerprint image. The fingerprint identification method according to claim 1, wherein the degradation processing of the first template fingerprint image includes: Perform degradation processing on the first template fingerprint image based on a fuzzy degradation model, calculate the primitive average of a preset number of primitive points around each primitive point in the first template fingerprint image, and convert the image into The primitive value of a primitive point is replaced by the primitive average. The fingerprint identification method according to claim 1, wherein said performing degradation processing on the first template fingerprint image includes: performing degradation processing on the first template fingerprint image based on a random noise degradation model, setting the noise a numerical range of the signal, and the noise within the numerical range is randomly superimposed onto the first template fingerprint image. The fingerprint identification method according to any one of claims 4 to 5, wherein said performing degradation processing on the first template fingerprint image includes: performing each image degradation model on the first template fingerprint image. Degradation processing is performed to obtain a preset number of second template fingerprint images. The fingerprint identification method according to claim 1, wherein the verification of the fingerprint image to be identified based on the at least one first template fingerprint image and the at least one second template fingerprint image includes: Determine whether the fingerprint image to be identified matches at least one of the at least one first template fingerprint image and the at least one second template fingerprint image; if it is determined that the fingerprint to be identified The image matches at least one of the at least one first template fingerprint image and the at least one second template fingerprint image, and it is determined that the fingerprint image to be identified passes the verification; or if it is determined that the fingerprint image to be identified passes the verification; The fingerprint image to be identified does not match all template fingerprint images in the at least one first template fingerprint image and the at least one second template fingerprint image, and it is determined that the fingerprint image to be identified is verified. Fail. A fingerprint module, which is used to perform the fingerprint identification method described in any one of claims 1 to 7. An electronic device, wherein the electronic device includes: a processor; and a memory, in which a plurality of program modules are stored, and the plurality of program modules are loaded by the processor and executed as requested. The fingerprint identification method described in any one of items 1 to 7.

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