Card verification method and device, medium and electronic equipment

Abstract

The embodiment of the invention discloses an identification card verification method and device, a medium and electronic equipment, and relates to the technical field of identification card verification. Based on a current identification card verification request, the face image of a current verified person is collected while portrait information pre-stored in an identification card is read; the method comprises the following steps: acquiring a face image of a target person, extracting second feature information on the face image for comparison, and in a comparison verification stage, determining whether the target person is consistent with pre-stored in a certificate by verifying the similarity between the first feature information and the second feature information. According to the method, the similarity representing the features on the first feature information and the second feature information and the distribution similarity representing the features on the first feature information and the second feature information are measured, the situation of independent feature comparison is considered, the distribution situation between the features is also considered, the influence of verification of a certain feature on overall verification is reduced, and the verification efficiency is improved. And the card verification level is improved.

Description

Technical Field

[0001] The present application relates to the field of card verification technology, and in particular to a card verification method, device, medium and electronic device. Background Art

[0002] Card verification is a form of identity authentication used to confirm the identity of the cardholder. For example, card verification in an airport environment aims to verify the identity information of passengers and staff to ensure the safe and orderly operation of the airport. Card verification is multifaceted, one of which is to verify whether the user information built into the card matches the current cardholder, and an important method is to verify whether the user portrait information built into the card matches the portrait of the current cardholder. In simple terms, it is to determine whether the cardholder is the person holding the card through verification, which is usually used in check-in, security check, boarding gate verification and other links.

[0003] The main means of card verification are manual verification and biometrics. Manual verification is to confirm by visually observing the cardholder by security personnel and comparing the cardholder's information with the cardholder's ID. However, this method is inefficient and is only applicable in special cases in the era of electronic information. Biometrics technologies such as face recognition and fingerprint recognition can achieve rapid card verification by entering the cardholder's biometric information during the card application stage and collecting the cardholder's biometric information for comparison during the verification stage. Under the current verification method based on face recognition technology, the similarity comparison of feature information such as eyes, nose, mouth, etc. is usually adopted. Then, if the similarity of the comparison is greater than the set threshold, it can be considered that the person and the ID are consistent. However, this method only considers the feature comparison separately and does not pay attention to the correlation between features. As a result, the verification result may be affected by a certain feature comparison, resulting in a large deviation and a lower level of verification. Summary of the invention

[0004] The main purpose of the present application is to provide a card verification method, device, medium and electronic device, aiming to solve the problem of low level of card verification based on face recognition technology in the prior art.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0006] In a first aspect, an embodiment of the present application provides a card verification method, comprising the following steps:

[0007] According to the card verification request, read the portrait information pre-stored in the target card and take a photo to obtain the face image of the target person; wherein the portrait information includes the first feature information of the face of the target person;

[0008] Extracting features from the face image to obtain second feature information;

[0009] According to the similarity between the first feature information and the second feature information, the similarity between the face image of the target person and the portrait information pre-stored in the target card is obtained to realize the card verification; wherein, the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity is used to characterize the similarity of features on the first feature information and the second feature information, and the second similarity is used to characterize the distribution similarity of features on the first feature information and the second feature information.

[0010] In a possible implementation of the first aspect, before obtaining the similarity between the face image of the target person and the portrait information pre-stored in the target ID card according to the similarity between the first feature information and the second feature information, the method further includes:

[0011] Obtaining a first similarity according to the similarity between the features on the first feature information and the second feature information;

[0012] Obtaining a second similarity according to the distribution similarity of features on the first feature information and the second feature information;

[0013] The first similarity and the second similarity are combined to obtain the similarity between the first feature information and the second feature information.

[0014] In a possible implementation manner of the first aspect, obtaining the first similarity according to the similarity of features on the first feature information and the second feature information includes:

[0015] According to the first feature information and the second feature information, a plurality of feature groups are obtained; wherein one feature group includes a first feature on the first feature information and a second feature on the second feature information corresponding to the first feature;

[0016] The similarities between the first features and the second features in the plurality of feature groups are fused to obtain a first similarity.

[0017] In a possible implementation manner of the first aspect, before obtaining the second similarity according to the distribution similarity of features on the first feature information and the second feature information, the method further includes:

[0018] The distribution similarity of features on the first feature information and the second feature information is obtained according to a first distance from a feature other than the first feature in the features of the first feature information to the first feature, and a second distance from a feature other than the second feature in the features of the second feature information to the second feature.

[0019] In a possible implementation manner of the first aspect, obtaining the second similarity according to the distribution similarity of features on the first feature information and the second feature information includes:

[0020] The distribution similarities of the features on the plurality of first feature information and the second feature information are fused to obtain a second similarity.

[0021] In a possible implementation of the first aspect, before obtaining the similarity between the face image of the target person and the portrait information pre-stored in the target ID card according to the similarity between the first feature information and the second feature information, the method further includes:

[0022] Obtain a similarity adjustment coefficient based on the time difference between the portrait information pre-stored in the target card and the face image of the target person obtained by shooting;

[0023] According to the similarity between the first feature information and the second feature information, the similarity between the face image of the target person and the portrait information pre-stored in the target card is obtained, including:

[0024] According to the similarity between the first feature information and the second feature information, and the similarity adjustment coefficient, the similarity between the face image of the target person and the portrait information pre-stored in the target ID card is obtained.

[0025] In a possible implementation manner of the first aspect, the similarity adjustment coefficient includes a first similarity adjustment coefficient and a second similarity adjustment coefficient, the first similarity adjustment coefficient is used to adjust the first similarity, and the second similarity adjustment coefficient is used to adjust the second similarity.

[0026] In a second aspect, an embodiment of the present application provides a card verification device, comprising:

[0027] The acquisition module is used to read the portrait information pre-stored in the target card and take a photo to obtain the face image of the target person according to the card verification request; wherein the portrait information includes the first feature information of the face of the target person;

[0028] An extraction module, the extraction module is used to extract features on the face image to obtain second feature information;

[0029] A verification module, the verification module is used to obtain the similarity between the face image of the target person and the portrait information pre-stored in the target card according to the similarity between the first feature information and the second feature information, so as to realize card verification; wherein the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity is used to characterize the similarity of features on the first feature information and the second feature information, and the second similarity is used to characterize the distribution similarity of features on the first feature information and the second feature information.

[0030] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, the card verification method provided in any one of the first aspects above is implemented.

[0031] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein:

[0032] The memory is used to store computer programs;

[0033] The processor is used to load and execute a computer program so that the electronic device executes a card verification method as provided in any one of the first aspects above.

[0034] Compared with the prior art, the beneficial effects of this application are:

[0035] The embodiments of the present application provide a card verification method, device, medium and electronic device, the method comprising: reading portrait information pre-stored in a target card and photographing to obtain a face image of a target person according to a card verification request; wherein the portrait information comprises first feature information of the face of the target person; extracting features on the face image to obtain second feature information; and obtaining similarity between the face image of the target person and the portrait information pre-stored in the target card according to the similarity between the first feature information and the second feature information, so as to realize card verification; wherein the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity being used to characterize the similarity between features on the first feature information and the second feature information, and the second similarity being used to characterize the distribution similarity between features on the first feature information and the second feature information. This application is based on the current card verification request. While reading the portrait information pre-stored in the card, it collects the face image of the person currently being verified, and extracts the second feature information on the face image for comparison. In the comparison verification stage, the similarity between the first feature information and the second feature information is verified to determine whether the target person is consistent with the pre-stored one in the document. Since the similarity comparison measures the similarity of the features characterizing the first feature information and the second feature information, as well as the distribution similarity of the features characterizing the first feature information and the second feature information, not only the comparison of the features alone is considered, but also the distribution between the features is considered, which reduces the impact of the verification of a certain feature on the overall verification and improves the level of card verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present application;

[0037] Figure 2 A flowchart of a card verification method provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of a module of a card verification device provided in an embodiment of the present application;

[0039] Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. DETAILED DESCRIPTION

[0040] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] See attached Figure 1 , attached Figure 1 This is a schematic diagram of the structure of an electronic device of the hardware operating environment involved in the embodiment of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage; the processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0042] Those skilled in the art will appreciate that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0043] As attached Figure 1 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module and a card verification device.

[0044] In the attached Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set in the electronic device, and the electronic device calls the card verification device stored in the memory 105 through the processor 101, and executes the card verification method provided in the embodiment of the present application.

[0045] See attached Figure 2 Based on the hardware device of the aforementioned embodiment, the embodiment of the present application provides a card verification method, comprising the following steps:

[0046] S10: According to the card verification request, read the portrait information pre-stored in the target card and take a photo to obtain a face image of the target person; wherein the portrait information includes first feature information of the face of the target person.

[0047] In the specific implementation process, the card verification request indicates that there is a cardholder who needs to verify the consistency of the person and the card. For example, when accepting security inspection at the airport, the cardholder puts the card into the scanning or sensing area of ​​the card reader, which can be regarded as initiating a card verification request. Then the portrait information stored in the card can be read. The target person is the person who is currently holding the card for verification, and the target card is the card held by the target person. The ultimate goal is to verify whether the portrait of the target person is consistent with the portrait stored in the target card. While reading the information in the target card, the camera set up at the security checkpoint can take the face image of the target person for subsequent comparison and verification. The target card will pre-store various information in the processing stage. For example, the ID card verified by airport security will record fingerprints, portraits, names, ages, and places of origin in the processing stage. In other application scenarios, such as credit cards used for swiping cards for consumption, portraits, names, ID numbers, account balances, and other information will be recorded in the processing stage. The embodiment of the present application is aimed at card verification under face recognition technology, and it is necessary to ensure that at least the target card has portrait information stored.

[0048] S20: Extract features from the face image to obtain second feature information.

[0049] In the specific implementation process, the features on the face image are extracted through face recognition technology to obtain the second feature information. It should be noted that the second feature information here corresponds to the first feature information included in the portrait information pre-stored in the target card. Face recognition technology is based on feature point extraction of 2D images, or recognition based on 3D technology, but the current mainstream recognition technology is still deep learning technology. Face recognition uses convolutional neural networks for feature extraction, which not only greatly improves the accuracy, but also can continuously optimize the extraction model based on the learning ability of big data.

[0050] The feature extraction process based on convolutional neural network is as follows: first, the face image is taken as input, and then the convolutional neural network is used to extract features of the input face image. The convolution kernel in the convolution layer will traverse the input image, perform multiplication and accumulation operations on each position, and thus generate feature maps. These feature maps contain the key feature information of the face, such as the shape and position of the eyes, nose, mouth and other parts, which is the second feature information. Similarly, the portrait information pre-stored in the card can also be used to obtain the first feature information in this way.

[0051] S30: According to the similarity between the first feature information and the second feature information, the similarity between the face image of the target person and the portrait information pre-stored in the target card is obtained to realize card verification; wherein the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity is used to characterize the similarity of features on the first feature information and the second feature information, and the second similarity is used to characterize the distribution similarity of features on the first feature information and the second feature information.

[0052] In the specific implementation process, the card verification is converted into the similarity verification of the first feature information and the second feature information, and the similarity measurement is divided into two parts, one is the similarity based on feature comparison, and the other is the similarity based on feature distribution comparison. In this way, the two similarities are combined to measure the similarity between the target person's face image and the portrait information pre-stored in the target card, which can effectively reduce the problem that the deviation of single feature comparison has a greater impact on the verification result. In short, the similarity of feature parts such as eyes, nose, and mouth is considered in the verification, and the similarity of the position distribution between feature parts such as eyes, nose, and mouth is also considered.

[0053] In one embodiment, before obtaining the similarity between the face image of the target person and the portrait information pre-stored in the target ID card according to the similarity between the first feature information and the second feature information, the method further includes:

[0054] Obtaining a first similarity based on the similarity between the first feature information and the second feature information;

[0055] Obtaining a second similarity based on the distribution similarity of features on the first feature information and the second feature information;

[0056] The first similarity and the second similarity are combined to obtain the similarity between the first feature information and the second feature information.

[0057] In a specific implementation process, the first similarity is obtained based on feature comparison, and can be obtained by comparing the corresponding features on the first feature information and the second feature information, that is, according to the similarity of the features on the first feature information and the second feature information, the first similarity is obtained, including:

[0058] According to the first feature information and the second feature information, a plurality of feature groups are obtained; wherein one feature group includes a first feature on the first feature information and a second feature on the second feature information corresponding to the first feature;

[0059] The similarities between the first features and the second features in the plurality of feature groups are fused to obtain a first similarity.

[0060] The feature on the first feature information is recorded as the first feature, and the feature on the second feature information is recorded as the second feature. The corresponding features are grouped into a feature group, and the similarity of the features in the feature group is measured. For example, the first feature corresponding to the mouth part on the first feature information and the second feature corresponding to the second feature information form a feature group. In this way, multiple feature groups can be formed according to the type of features. The similarity between the first feature and the second feature in each feature group is the similarity of the features compared separately. The Euclidean distance or cosine similarity can be used to obtain the similarity. These similarities are fused, such as directly averaging, or taking into account the weighted average under the influence of factors such as image distortion and transmission, and finally fused to obtain a percentage data as the first similarity.

[0061] The second feature is obtained based on the distribution comparison of the feature, and can be obtained by comparing the distribution of the features on the first feature information and the second feature information, that is, according to the distribution similarity of the features on the first feature information and the second feature information, before obtaining the second similarity, the method further includes:

[0062] The distribution similarity of features on the first feature information and the second feature information is obtained according to a first distance from a feature other than the first feature in the features of the first feature information to the first feature, and a second distance from a feature other than the second feature in the features of the second feature information to the second feature.

[0063] Using the first feature and the second feature defined in the feature group divided in the above-mentioned embodiment, firstly, for the information pre-stored on the card, a first feature is determined as the target feature in the first feature information, and the distances from other features to the target feature are obtained; similarly, for the face image, a second feature is determined as the target feature in the second feature information, and the distances from other features to the target feature are obtained, and the similarity is calculated for the corresponding distances. For example, in the first feature information, the mouth is the target feature, and the distances to the mouth can be determined for the left eye and the right eye respectively. Similarly, the distances to the mouth from the left eye and the right eye can also be confirmed in the second feature information. Then, the similarity is calculated for the distance from the left eye to the mouth, and the similarity is calculated for the distance from the right eye to the mouth. In this way, the distribution similarity is converted into the similarities of multiple distances. These distance similarities are merged, and a distribution similarity expressed as a percentage can also be obtained by averaging or weighted averaging.

[0064] To further reduce the impact of a single feature, different target features are selected to obtain the above distribution similarity, so that multiple distribution similarities obtained with different features as target features can be obtained, and then these multiple distribution similarities are merged to determine the final second similarity, so that the feature distribution can be better integrated into the card verification. That is: according to the distribution similarity of the features on the first feature information and the second feature information, the second similarity is obtained, including:

[0065] The distribution similarities of the features on the plurality of first feature information and the second feature information are fused to obtain a second similarity.

[0066] In one embodiment, before obtaining the similarity between the face image of the target person and the portrait information pre-stored in the target ID card according to the similarity between the first feature information and the second feature information, the method further includes:

[0067] The similarity adjustment coefficient is obtained according to the time difference between the portrait information pre-stored in the target card and the facial image of the target person obtained by shooting.

[0068] In the specific implementation process, since the portrait information in the card will not be updated at any time, for example, the ID card can be used for more than ten years. During this period, it is generally not re-issued without special circumstances. However, during this period, the cardholder may have facial features and facial feature distribution changes due to age. If the portrait is still verified with the same high standard, there is a high probability that it will not pass. At this time, the similarity can be measured by the time difference between the current verification time and the time when the portrait information is pre-stored when the certificate is processed. It is obviously inconvenient to lower the similarity threshold for verification in the actual application stage. Therefore, the similarity of the feature is adjusted by the similarity adjustment coefficient obtained by this time difference. The greater the time difference, the greater the appearance change may be. The threshold set for verification can be appropriately lowered. Conversely, the similarity at the time of verification can be appropriately increased. Since the measurement of similarity is divided into two parts, the setting of the similarity adjustment coefficient can also be divided into two parts, namely: the similarity adjustment coefficient includes a first similarity adjustment coefficient and a second similarity adjustment coefficient. The first similarity adjustment coefficient is used to adjust the first similarity, and the second similarity adjustment coefficient is used to adjust the second similarity. The change of feature distribution is greater than the change of features, so in the actual application stage, the second similarity adjustment coefficient is usually greater than the first similarity adjustment coefficient. However, these changes are subtle and usually will not cause the verification effect to deteriorate due to similarity adjustment. Another function of adjustment is to provide data that can more accurately describe the degree of similarity.

[0069] Based on the above steps, according to the similarity between the first feature information and the second feature information, the similarity between the face image of the target person and the portrait information pre-stored in the target ID card is obtained, including:

[0070] According to the similarity between the first feature information and the second feature information, and the similarity adjustment coefficient, the similarity between the face image of the target person and the portrait information pre-stored in the target ID card is obtained.

[0071] In this embodiment, based on the current card verification request, while reading the portrait information pre-stored in the card, the face image of the person currently being verified is collected, and the second feature information on the face image is extracted for comparison. In the comparison verification stage, the similarity between the first feature information and the second feature information is verified to determine whether the target person is consistent with the pre-stored one in the document. Since the similarity comparison measures the similarity of the features characterizing the first feature information and the second feature information, as well as the distribution similarity of the features characterizing the first feature information and the second feature information, not only the comparison of the features alone is considered, but also the distribution between the features is considered, thereby reducing the impact of the verification of a certain feature on the overall verification and improving the level of card verification.

[0072] See attached Figure 3 Based on the same inventive concept as in the above-mentioned embodiment, the embodiment of the present application further provides a card verification device, comprising:

[0073] The acquisition module is used to read the portrait information pre-stored in the target card and take a photo to obtain the face image of the target person according to the card verification request; wherein the portrait information includes the first feature information of the face of the target person;

[0074] An extraction module, the extraction module is used to extract features on the face image to obtain second feature information;

[0075] A verification module, the verification module is used to obtain the similarity between the face image of the target person and the portrait information pre-stored in the target card according to the similarity between the first feature information and the second feature information, so as to realize card verification; wherein the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity is used to characterize the similarity of features on the first feature information and the second feature information, and the second similarity is used to characterize the distribution similarity of features on the first feature information and the second feature information.

[0076] Those skilled in the art should understand that the division of the various modules in the embodiment is only a division of logical functions, and can be fully or partially integrated into one or more actual carriers in actual application, and these modules can be all implemented in the form of software called by the processing unit, or all implemented in the form of hardware, or implemented in the form of a combination of software and hardware. It should be noted that the modules in the card verification device in this embodiment correspond one-to-one to the steps in the card verification method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned card verification method, which will not be repeated here.

[0077] Based on the same inventive concept as in the aforementioned embodiment, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements a card verification method as provided in the embodiment of the present application.

[0078] Based on the same inventive concept as in the above-mentioned embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein:

[0079] The memory is used to store computer programs;

[0080] The processor is used to load and execute a computer program so that the electronic device executes the card verification method provided in the embodiment of the present application.

[0081] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0082] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0083] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0084] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0085] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0086] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0088] In summary, the present application provides a card verification method, device, medium and electronic device, the method comprising: according to a card verification request, reading the portrait information pre-stored in the target card and photographing to obtain the face image of the target person; wherein the portrait information includes the first feature information of the face of the target person; extracting the features on the face image to obtain the second feature information; according to the similarity between the first feature information and the second feature information, obtaining the similarity between the face image of the target person and the portrait information pre-stored in the target card to realize card verification; wherein the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity is used to characterize the similarity of features on the first feature information and the second feature information, and the second similarity is used to characterize the distribution similarity of features on the first feature information and the second feature information. This application is based on the current card verification request. While reading the portrait information pre-stored in the card, it collects the face image of the person currently being verified, and extracts the second feature information on the face image for comparison. In the comparison verification stage, the similarity between the first feature information and the second feature information is verified to determine whether the target person is consistent with the pre-stored one in the document. Since the similarity comparison measures the similarity of the features characterizing the first feature information and the second feature information, as well as the distribution similarity of the features characterizing the first feature information and the second feature information, not only the comparison of the features alone is considered, but also the distribution between the features is considered, which reduces the impact of the verification of a certain feature on the overall verification and improves the level of card verification.

[0089] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A card verification method, characterized in that: The following steps are involved: According to the card verification request, read the portrait information pre-stored in the target card and take a photo to obtain the face image of the target person; wherein the portrait information includes the first feature information of the face of the target person; Extracting features from the face image to obtain second feature information; According to the similarity between the first feature information and the second feature information, the similarity between the facial image of the target person and the portrait information pre-stored in the target card is obtained to realize card verification; wherein, the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity is used to characterize the similarity of features on the first feature information and the second feature information, and the second similarity is used to characterize the distribution similarity of features on the first feature information and the second feature information.

2. The card verification method according to claim 1, characterized in that: Before obtaining the similarity between the face image of the target person and the portrait information pre-stored in the target ID card according to the similarity between the first feature information and the second feature information, the method further includes: Obtaining the first similarity according to the similarity between the features on the first feature information and the second feature information; Obtaining the second similarity according to the distribution similarity of features on the first feature information and the second feature information; The first similarity and the second similarity are combined to obtain similarity between the first feature information and the second feature information.

3. The card verification method according to claim 2, characterized in that: The obtaining the first similarity according to the similarity between the features on the first feature information and the second feature information includes: According to the first feature information and the second feature information, a plurality of feature groups are obtained; wherein one of the feature groups includes a first feature on the first feature information and a second feature on the second feature information corresponding to the first feature; The similarities between the first feature and the second feature in a plurality of the feature groups are merged to obtain the first similarity.

4. The card verification method according to claim 3, characterized in that: Before obtaining the second similarity according to the distribution similarity of features on the first feature information and the second feature information, the method further includes: The distribution similarity of features on the first feature information and the second feature information is obtained according to a first distance from a feature other than the first feature in the features of the first feature information to the first feature, and a second distance from a feature other than the second feature in the features of the second feature information to the second feature.

5. The card verification method according to claim 4, characterized in that: The obtaining the second similarity according to the distribution similarity of features on the first feature information and the second feature information includes: The second similarity is obtained by fusing distribution similarities of features on a plurality of the first feature information and the second feature information.

6. The card verification method according to claim 1, characterized in that: Before obtaining the similarity between the face image of the target person and the portrait information pre-stored in the target ID card according to the similarity between the first feature information and the second feature information, the method further includes: Obtaining a similarity adjustment coefficient according to a time difference between the portrait information pre-stored in the target card and the facial image of the target person obtained by shooting; The obtaining, according to the similarity between the first feature information and the second feature information, the similarity between the face image of the target person and the portrait information pre-stored in the target ID card includes: The similarity between the facial image of the target person and the portrait information pre-stored in the target ID card is obtained according to the similarity between the first feature information and the second feature information, and the similarity adjustment coefficient.

7. The card verification method according to claim 6, characterized in that: The similarity adjustment coefficient includes a first similarity adjustment coefficient and a second similarity adjustment coefficient, the first similarity adjustment coefficient is used to adjust the first similarity, and the second similarity adjustment coefficient is used to adjust the second similarity.

8. A card verification device, characterized in that: include: An acquisition module, the acquisition module is used to read the portrait information pre-stored in the target card and take a photo to obtain the face image of the target person according to the card verification request; wherein the portrait information includes the first feature information of the face of the target person; An extraction module, the extraction module is used to extract features on the face image to obtain second feature information; A verification module, the verification module is used to obtain the similarity between the face image of the target person and the portrait information pre-stored in the target card according to the similarity between the first feature information and the second feature information, so as to realize card verification; wherein, the similarity between the first feature information and the second feature information is obtained based on the first similarity and the second similarity, the first similarity is used to characterize the similarity of features on the first feature information and the second feature information, and the second similarity is used to characterize the distribution similarity of features on the first feature information and the second feature information.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by a processor, the card verification method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute the computer program so that the electronic device executes the card verification method according to any one of claims 1 to 7.

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