A method for face identity verification and identity data encryption storage

Abstract

The present invention proposes a method for face identity verification and identity data encryption storage, comprising the following steps: setting an identity verification area, setting identification check points in the identity verification area, setting a biometric feature recognition unit for performing face recognition on a person entering the identity verification area in a circular array around the identification check points, and importing the recognized multi-angle face information into a feature record database; the feature record database classifies the multi-angle face information data, splits the face information into feature data of at least three angles, and performs image splicing on the multi-angle face information, blurs the pixel points at the splicing, and performs approximate fading processing on the adjacent pixel points at the splicing to obtain the face panoramic data, and records the face panoramic data for storage; establishing an identity database, classifying the identity information in the identity database, and isolating each level of confidentiality storage unit in the identity database through an independent data node.

Description

Technical Field

[0001] The present invention belongs to the field of identity verification, and in particular relates to a method for face identity verification and identity data encryption storage. Background Art

[0002] The emergence of self-service identity verification technology is based on the recognition of the shortcomings and limitations of traditional identity verification methods, so technological innovation and improvement are needed. Traditional identity verification methods mainly include manual verification and password-based verification methods, which have some shortcomings and deficiencies, such as low efficiency, easy forgery, and easy impersonation. Therefore, existing technologies need to be continuously improved and innovated to meet the needs of social development.

[0003] First, the traditional identity verification method relies on manual verification, which is inefficient, especially in places with large traffic, such as airports and stations. Manual verification often cannot meet the needs of real-time verification. In addition, manual verification is prone to negligence and mistakes, and cannot guarantee the accuracy and completeness of information. Moreover, manual verification requires a lot of human resources and time costs, which is not conducive to improving service efficiency and reducing costs.

[0004] Secondly, password-based authentication methods are prone to forgery and impersonation. Passwords can be stolen, leaked, or impersonated by others, resulting in insecure identity authentication. Especially in the fields of online payment and electronic banking, password-based authentication methods are vulnerable to hacker attacks and network viruses, and pose a major security risk.

[0005] In addition, traditional identity authentication methods cannot meet personalized needs and multi-scenario application needs. With the development of science and technology and the progress of society, people have higher and higher requirements for identity authentication, and need personalized authentication methods according to different scenarios and needs. However, traditional identity authentication methods often cannot meet these needs and lack flexibility and adaptability. Existing identity authentication technologies have some shortcomings and deficiencies, which require technological innovation and improvement. The emergence of self-service identity verification technology is a solution to these problems. By combining functions such as biometric recognition, identity document reading and background database comparison, self-service identity verification technology can improve verification efficiency and accuracy, reduce labor costs, and enhance security management and service experience. Therefore, self-service identity verification technology has broad application prospects and market demand. Through continuous technological innovation and improvement, the level and quality of identity authentication technology can be further improved, bringing more convenience and security to social development and people's lives. Summary of the invention

[0006] The present invention provides a method for face identity verification and identity data encryption storage, which solves the problem that traditional identity verification methods in the prior art are low in efficiency and easy to be forged.

[0007] The technical solution of the present invention is implemented as follows: a method for face identity verification and identity data encryption storage, the method comprising the following steps:

[0008] An identity verification area is set up, identification inspection points are set up in the identity verification area, and biometric identification units are set up in a circular array around the identification inspection points, and facial recognition of persons entering the identity verification area is performed through the biometric identification units, and the multi-angle facial information recognized is imported into a feature record database;

[0009] The feature record database classifies the multi-angle facial information data, splits the facial information into feature data of at least three angles, performs image splicing on the multi-angle facial information, blurs the pixels at the splicing, performs approximate fading processing on the adjacent pixels at the splicing to obtain panoramic face data, and records the panoramic face data for storage;

[0010] Establish an identity database, classify identity information in the identity database, isolate each level of confidential storage unit in the identity database through an independent data node, and place the identity information in the corresponding confidential storage unit in sequence according to the information content after classification when storing the identity information, and read the internal storage data in sequence according to the verification requirements through the independent key of each confidential storage unit when verifying, and send the read data to the data matching verification unit;

[0011] The data matching and verification unit associates the multi-angle facial information with the panoramic face data, and then obtains the identity database and sends it to the matching data. When the identity is verified for the first time, the photo data reserved in the matching data is synchronously matched with the multi-angle facial information and the panoramic face data, and then the matching result is fed back to the identity verification area.

[0012] Compared with existing technologies, this self-service identity verification method has the following differences and innovations:

[0013] Multi-angle facial information processing: This method classifies and processes multi-angle facial information, splits the facial information into feature data of at least three angles, and obtains panoramic facial data after image splicing, blurring and approximate fading. The innovation of this step is that through the processing of multi-angle information and the acquisition of panoramic data, the accuracy and completeness of facial information are improved, the robustness and stability of recognition are enhanced, and thus the accuracy and security of identity verification are improved.

[0014] Hierarchical storage of identity database: This method classifies identity information in the identity database and stores it in isolation through independent data nodes. The innovation of this hierarchical storage is that by classifying and storing identity information in a hierarchical manner, the security and privacy protection of information are enhanced, so that identity information at different levels is more strictly protected and managed, and the security and controllability of identity information are improved.

[0015] Data matching and verification unit: This method associates multi-angle facial information with panoramic face data and performs matching verification. The innovation of this step is that by associating and matching multi-angle information with panoramic data, the matching accuracy and robustness of data are improved, the risk of misidentification and impersonation is reduced, and the reliability and security of identity verification are enhanced.

[0016] Initial identity verification: This method will synchronously match the reserved photo data in the matching data with the multi-angle facial information and the panoramic face data when conducting the initial identity verification. This innovation is that by synchronously matching the reserved photo data with the multi-angle information and the panoramic face data, the initial verification is made efficient and accurate, and the real-time and reliability of the verification is improved.

[0017] This self-service identity verification method improves the accuracy, security and real-time performance of identity verification through the innovative design of multi-angle facial information processing, hierarchical storage of identity database, and data matching verification. It can better cope with the limitations and shortcomings of traditional identity authentication methods and has higher reliability and applicability.

[0018] As a preferred implementation, when the data matching verification unit performs matching verification, if the matching data is synchronously matched with both the multi-angle facial information and the panoramic face data, the multi-angle facial information data will be deleted, and subsequent verification and comparison will only be performed through the panoramic face data; if the matching data is consistent with the multi-angle facial information match but inconsistent with the panoramic face data, the panoramic face data will be processed twice until the synchronous match is consistent; if the matching data is inconsistent with the multi-angle facial information match but consistent with the panoramic face data, it will be marked and manually repeated verification will be performed. After the verification is successful, the multi-angle facial information data will be deleted, and the panoramic face data will be retained for subsequent comparison; if the matching data is inconsistent with both the multi-angle facial information and the panoramic face data synchronously matched, an alarm will be triggered.

[0019] As a preferred embodiment, the biometric identification units arranged in a circular array around the identification check point in the identity verification area are arranged in at least three layers from top to bottom, with at least 6 groups arranged in each layer, and the distance between the biometric identification units in each layer is the same as the identification check point, wherein the distance ratio of the spacing between the middle layer and the upper and lower layers, the spacing between the middle layer and the identification check point, and the spacing between the upper and lower layers and the identification check point is 3:4:5.

[0020] As a preferred implementation, when facial information is split into feature data of at least three angles in the feature record database, the three layers of biometric recognition units are divided into regions, and three regions are divided according to the angles. Facial features are collected by 6 biometric recognition units in each region, and there are 2 of the 6 biometric recognition units in each layer.

[0021] As a preferred implementation, when performing identity information classification, the name, gender and date of birth are used as the information stored in the first-level confidential storage unit, the ID number and face scan data are used as the information stored in the second-level confidential storage unit, and the position information and fingerprint information are used as the information stored in the third-level confidential storage unit. According to the identity verification requirements, the information stored in the confidential storage units is retrieved in sequence.

[0022] After adopting the above technical scheme, the beneficial effects of the present invention are: improving verification accuracy: by processing multi-angle facial information and acquiring panoramic data, the accuracy and integrity of facial information are enhanced, making identity verification more accurate and reliable. Enhancing recognition robustness: the processing and matching verification design of multi-angle facial information enhances the robustness and stability of recognition, and reduces the risk of misidentification and impersonation. Strengthening security protection: by reading the internal storage data through the hierarchical storage of the identity database and independent keys, the security and privacy protection of identity information are improved. Improving real-time and efficiency: the efficient matching and verification design during the first identity verification makes identity verification more real-time and efficient, and improves the efficiency of the self-service verification system. Reducing the risk of forgery: the acquisition and matching verification design of panoramic data reduces the risk of forged identity information and enhances the reliability and security of verification. This self-service identity verification method can improve the accuracy, security and real-time of identity verification through innovative designs in many aspects, reduce the risks in the identity verification process, and provide users with a more reliable and convenient identity verification experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0024] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0026] like Figure 1 As shown, a method for face identity verification and identity data encryption storage comprises the following steps:

[0027] An identity verification area is set up, identification inspection points are set up in the identity verification area, and biometric identification units are set up in a circular array around the identification inspection points, and facial recognition of persons entering the identity verification area is performed through the biometric identification units, and the multi-angle facial information recognized is imported into a feature record database;

[0028] The feature record database classifies the multi-angle facial information data, splits the facial information into feature data of at least three angles, performs image splicing on the multi-angle facial information, blurs the pixels at the splicing, performs approximate fading processing on the adjacent pixels at the splicing to obtain panoramic face data, and records the panoramic face data for storage;

[0029] Establish an identity database, classify identity information in the identity database, isolate each level of confidential storage unit in the identity database through an independent data node, and place the identity information in the corresponding confidential storage unit in sequence according to the information content after classification when storing the identity information, and read the internal storage data in sequence according to the verification requirements through the independent key of each confidential storage unit when verifying, and send the read data to the data matching verification unit;

[0030] The data matching and verification unit associates the multi-angle facial information with the panoramic face data, and then obtains the identity database and sends it to the matching data. When the identity is verified for the first time, the photo data reserved in the matching data is synchronously matched with the multi-angle facial information and the panoramic face data, and then the matching result is fed back to the identity verification area.

[0031] This self-service identity verification method uses advanced biometric recognition technology and data storage management methods, which has obvious differences and advantages compared with existing technologies. This method sets up identification inspection points in the identity verification area and sets up biometric recognition units around the identification points. It recognizes the faces of people entering the area and imports the multi-angle facial information into the feature record database. This step uses facial recognition technology to obtain more comprehensive biometric information of individuals, which is more accurate and secure than traditional identity verification methods.

[0032] The feature record database classifies multi-angle facial information data and splits the facial information into feature data from at least three angles, then performs image splicing and blurring and fading processing, and finally obtains panoramic facial data for record and storage. This panoramic data acquisition and storage method can more comprehensively retain individual biometric information, and after processing, it can effectively prevent the risk of information leakage and theft.

[0033] This method establishes an identity database and stores identity information in a hierarchical manner, isolates it through independent data nodes, and uses independent keys to read internally stored data, ensuring the security and privacy of identity information. This data storage and management method is more secure and reliable than traditional database storage, and can effectively prevent identity information from being illegally obtained or tampered with.

[0034] The data matching verification unit associates the multi-angle facial information with the panoramic face data and feeds back the matching results to the identity verification area. This data matching method is more accurate and reliable, and can effectively avoid misjudgment caused by mismatch of a single angle or local features.

[0035] This self-service identity verification method has obvious advantages in biometric recognition, data storage management and data matching verification, and is more accurate, safe and reliable than existing technologies. It will play an important role in various scenarios that require identity verification, including but not limited to ATMs, access control systems, security checkpoints, etc.

[0036] When the data matching verification unit performs matching verification, if the matching data is synchronously matched with both the multi-angle facial information and the face panoramic data, the multi-angle facial information data is deleted, and the subsequent verification and comparison is only performed through the face panoramic data; if the matching data is consistent with the multi-angle facial information, but inconsistent with the face panoramic data, the face panoramic data is processed twice until the synchronous matching is consistent; if the matching data is inconsistent with the multi-angle facial information, but consistent with the face panoramic data, it is marked and repeatedly verified manually. After the verification is successful, the multi-angle facial information data is deleted, and the face panoramic data is retained for subsequent comparison; if the matching data is inconsistent with both the multi-angle facial information and the face panoramic data, an alarm is triggered. This data matching and processing method has certain differences and advantages compared with the existing technology. First of all, traditional face recognition technology usually relies on only a single facial information for verification, but this method not only uses multi-angle facial information, but also matches it with the face panoramic data for verification. This is a major difference compared to traditional methods, because traditional methods may lead to misjudgment due to mismatch of a single angle or local features, while the synchronous matching of multi-angle facial information and panoramic face data can greatly improve the accuracy and reliability of verification. When the matching data is synchronously matched with both the multi-angle facial information and the panoramic face data, the multi-angle facial information data is deleted, and the subsequent verification and comparison is only performed through the panoramic face data. This method can reduce data redundancy and improve the efficiency and security of data management. Traditional methods may retain all facial information data, increasing the storage and management costs of data. If the matching data is consistent with the multi-angle facial information but inconsistent with the panoramic face data, the panoramic face data is processed twice until the synchronous matching is consistent. This processing method can further improve the accuracy of the panoramic face data and ensure that the verification result is more reliable. Traditional methods may directly match data, but do not take into account the consistency of multi-angle facial information and panoramic face data. If the matching data is inconsistent with the multi-angle facial information, but consistent with the panoramic face data, it will be marked and manually verified. After successful verification, the multi-angle facial information data will be deleted and the panoramic face data will be retained for subsequent comparison. This method can mark when the verification fails and perform manual repeated verification, which improves the reliability and security of the verification. Traditional methods may not perform marking and manual repeated verification, resulting in unreliable verification results.

[0037] Compared with traditional methods, this matching and processing method pays more attention to the consistency of multi-angle facial information and panoramic face data, improves the accuracy and reliability of verification, reduces data redundancy, and improves the efficiency and security of data management. The purpose of this setting is to improve the efficiency and security of self-service identity verification methods, avoid possible misjudgments and data management problems in traditional methods, and better meet the needs of identity verification in practical applications.

[0038] The biometric identification units arranged in the circular array around the identification check point in the identity verification area are arranged in at least three layers from top to bottom, and each layer is arranged with at least 6 groups. The distance between the biometric identification units in each layer and the identification check point is the same, wherein the distance ratio between the middle layer and the upper and lower layers, the distance between the middle layer and the identification check point, and the distance between the upper and lower layers and the identification check point is 3:4:5. This setting has obvious differences and advantages compared with the prior art. The existing biometric identification units are usually arranged on the same horizontal plane, while the biometric identification units in the present method are arranged on at least three vertical levels. This multi-layer vertical setting can obtain the biometric information of an individual more comprehensively, avoiding the problems of occlusion and insufficient viewing angle that may exist on a single plane. This setting also takes into account the distance ratio between different levels, that is, the distance ratio between the middle layer and the upper and lower layers, the distance between the middle layer and the identification check point, and the distance between the upper and lower layers and the identification check point is 3:4:5. This ratio setting takes into account the distance relationship between different levels, can better adapt to the identification needs of different distances, and ensures that accurate biometric information can be obtained within different distance ranges. The purpose of this setting is to improve the comprehensiveness and accuracy of biometric identification. Through multi-layer vertical settings, more comprehensive and true biometric information can be obtained at different angles and distances, avoiding the occlusion and insufficient viewing angle that may exist in a single plane setting. At the same time, the distance ratio between different levels is taken into account, which can better adapt to the identification needs within different distance ranges and ensure that accurate biometric information can be obtained at different distances. This setting can improve the accuracy and reliability of identity authentication and is suitable for various scenarios that require identity authentication, including but not limited to ATMs, access control systems, security checkpoints, etc.

[0039] When facial information is split into feature data of at least three angles in the feature record database, the three-layer biometric units are divided into regions, divided into three regions according to the angle, and facial features are collected by 6 biometric recognition units in each region, and there are 2 of the 6 biometric recognition units in each layer. This setting has obvious differences and advantages compared with the prior art. The existing facial feature record database usually only collects facial feature data of a single angle, while the present method splits facial information into feature data of at least three angles, and divides the regions in the three-layer biometric units, divided into three regions according to the angle, and facial features are collected by 6 biometric recognition units in each region. This method of multi-angle feature data collection can obtain individual facial feature information more comprehensively, avoid the problems of occlusion and insufficient viewing angle that may exist at a single angle, and improve the accuracy and reliability of face recognition.

[0040] For feature collection in each area, 6 biometric recognition units are used, 2 in each layer. This setting can better cover the entire face area and ensure that sufficient biometric information can be obtained at every angle. Compared with traditional methods that may only collect data from a small number of feature points, this setting can more comprehensively record facial features and improve the accuracy of face recognition.

[0041] The purpose of this setting is to improve the comprehensiveness and accuracy of the facial feature record database. By collecting multi-angle feature data, more comprehensive and realistic facial feature information can be obtained at different angles, avoiding the occlusion and insufficient viewing angle that may exist at a single angle. At the same time, the use of multiple biometric recognition units for feature collection can cover the entire face area, ensuring that sufficient biometric information can be obtained at each angle. This setting can improve the accuracy and reliability of face recognition and is suitable for various scenarios that require face recognition, including but not limited to security monitoring, face payment, entrance and exit management, etc.

[0042] When classifying identity information, the name, gender and date of birth are used as the information stored in the first-level confidential storage unit, the ID number and face scan data are used as the information stored in the second-level confidential storage unit, and the position information and fingerprint information are used as the information stored in the third-level confidential storage unit. According to the identity verification requirements, the information stored in the confidential storage unit is retrieved in sequence. This setting has obvious differences and advantages compared with the prior art. The existing identity information storage is usually stored uniformly in one unit, while the present method stores the identity information hierarchically and divides it into three confidential storage units, each unit storing identity information of different levels. The name, gender and date of birth are used as the information stored in the first-level confidential storage unit, the ID number and face scan data are used as the information stored in the second-level confidential storage unit, and the position information and fingerprint information are used as the information stored in the third-level confidential storage unit. This hierarchical storage method can better protect personal privacy and ensure that identity information of different levels is properly protected.

[0043] According to the identity verification requirements, the information stored in the confidential storage unit is retrieved in sequence. This sequential retrieval method can selectively extract identity information of the corresponding level according to specific verification requirements, thereby realizing orderly management and use of information. The purpose of this setting is to better protect personal privacy and information security. By storing identity information in a hierarchical manner, it can be protected according to the sensitivity of different information, ensuring the security of personal privacy. At the same time, sequential retrieval of stored information can selectively extract identity information of the corresponding level according to actual verification requirements, thereby improving the efficiency of information management and use. This setting is suitable for various scenarios that require identity verification, including but not limited to financial institutions, security checkpoints, government departments, etc.

[0044] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for face identity verification and identity data encryption storage, characterized in that: The method comprises the following steps: An identity verification area is set up, identification inspection points are set up in the identity verification area, and biometric identification units are set up in a circular array around the identification inspection points, and facial recognition of persons entering the identity verification area is performed through the biometric identification units, and the multi-angle facial information recognized is imported into a feature record database; The feature record database classifies the multi-angle facial information data, splits the facial information into feature data of at least three angles, performs image splicing on the multi-angle facial information, blurs the pixels at the splicing, performs approximate fading processing on the adjacent pixels at the splicing to obtain panoramic face data, and records the panoramic face data for storage; Establish an identity database, classify identity information in the identity database, isolate each level of confidential storage unit in the identity database through an independent data node, and place the identity information in the corresponding confidential storage unit in sequence according to the information content after classification when storing the identity information, and read the internal storage data in sequence according to the verification requirements through the independent key of each confidential storage unit when verifying, and send the read data to the data matching verification unit; The data matching and verification unit associates the multi-angle facial information with the panoramic face data, then obtains the identity database and sends it to the matching data. When the identity is verified for the first time, the reserved photo data in the matching data is synchronously matched with the multi-angle facial information and the panoramic face data, and then the matching result is fed back to the identity verification area; When the data matching verification unit performs matching verification, if the matching data is synchronously matched with the multi-angle facial information and the panoramic face data, the multi-angle facial information data is deleted, and the subsequent verification and comparison is only performed through the panoramic face data; If the matching data is consistent with the multi-angle facial information but inconsistent with the face panoramic data, the face panoramic data is processed again until the synchronous matching is consistent; If the matching data is inconsistent with the multi-angle facial information but consistent with the panoramic face data, it will be marked and manually verified repeatedly. After successful verification, the multi-angle facial information data will be deleted and the panoramic face data will be retained for subsequent comparison; if the matching data is inconsistent with both the multi-angle facial information and the panoramic face data, an alarm will be triggered; The biometric identification units arranged in a circular array around the identification check point in the identity verification area are arranged in at least three layers from top to bottom, with at least 6 groups arranged in each layer, and the distance between the biometric identification units in each layer and the identification check point is the same, wherein the distance ratio between the middle layer and the upper and lower layers, the distance between the middle layer and the identification check point, and the distance between the upper and lower layers and the identification check point is 3:4:5; When the facial information is split into feature data of at least three angles in the feature record database, the three layers of biometric recognition units are divided into three areas according to the angles, and facial features are collected by six biometric recognition units in each area, and there are two of the six biometric recognition units in each layer; When classifying identity information, the name, gender and date of birth are stored in the first-level confidential storage unit, the ID number and face scan data are stored in the second-level confidential storage unit, and the position information and fingerprint information are stored in the third-level confidential storage unit. According to the identity verification requirements, the information stored in the confidential storage units is retrieved in sequence.

Images