A virtual reality game user identity authentication system

Abstract

The present invention discloses a virtual reality game user identity authentication system, which relates to the field of virtual reality security technology and includes an account management module, an information collection module, a biometric verification module, a behavioral feature verification module, an iris feature verification module, and a storage module. The information collection module collects user facial information, behavioral pattern information, iris information, and voiceprint information. The biometric verification module includes a facial feature verification unit and a voiceprint feature verification unit. During the game, environmental risk conditions are regularly analyzed, and the comprehensiveness and security of identity authentication are enhanced by combining multiple biometric information and behavioral pattern analysis. The introduction of an environmental risk coefficient and an iris feature verification module increases the strictness of verification in high-risk environments. By adaptively adjusting the verification threshold, the system flexibly responds to dynamically changing usage environments, thereby improving the security and accuracy of virtual reality user identity authentication.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality security technology, and in particular to a virtual reality game user identity authentication system. Background Art

[0002] Virtual reality technology is a computer simulation system that allows users to create and experience a virtual world. It uses computers to generate simulated environments, immersing users in them. These phenomena can be real objects or invisible materials, represented through three-dimensional models. Because these phenomena are not directly visible but simulated through computer technology, they are called virtual reality. When users enter a virtual reality scene through a virtual reality device, malicious programs can record their actions and information within the scene, potentially leaking user information. This poses a security risk and can cause serious financial losses. Therefore, ensuring the security of VR user accounts is essential.

[0003] Currently, VR game users typically use a password, fingerprint, or facial recognition for authentication. These relatively simple authentication methods pose a risk of being bypassed as counterfeiting techniques advance. Furthermore, due to the volatile VR gaming environment, VR devices are often used both in home and public networks, increasing account security risks. These methods fail to consider the impact of environmental factors on authentication. This inability to flexibly adapt to dynamically changing environments results in poor authentication effectiveness in high-risk environments, failing to fully protect user accounts. Summary of the Invention

[0004] The purpose of the present invention is to provide a virtual reality game user identity authentication system that solves the problems raised in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a virtual reality game user identity verification system, comprising an account management module, an information collection module, a biometric verification module, a behavioral feature verification module, an iris feature verification module, and a storage module;

[0006] The information collection module collects user facial information, behavior pattern information, iris information and voiceprint information, and stores them through the storage module;

[0007] The biometric verification module includes a facial feature verification unit and a voiceprint feature verification unit. The facial feature verification unit compares the current user's face with the user's facial information collected during registration. The voiceprint feature verification unit is used to compare the current user's voice information with the voiceprint information collected during registration, and judge whether the voice is qualified in combination with the environmental risk situation.

[0008] The behavior feature verification module is used to compare the current user's behavior pattern information with the behavior pattern information collected during registration to determine whether it is qualified;

[0009] During the game, the environmental risk situation is analyzed regularly. When the environmental risk situation is too bad, the iris feature verification module compares the current user's iris information with the iris information collected during registration. If the comparison fails, the user needs to log in to the system again.

[0010] Optionally, the facial feature verification unit verification process is as follows:

[0011]

[0012] Where D(F u ,F i ) represents the user's stored facial feature vector F u With the input facial feature vector F i The Euclidean distance between

[0013] F u (k) represents the kth element value of the user's facial feature vector;

[0014] F i (k) represents the kth element value of the input image feature vector;

[0015] n represents the number of elements;

[0016] User's facial feature vector F u With the input image feature vector F i The Euclidean distance D(F u ,F i ) is smaller, the more closely the input image matches the facial information obtained by the facial information acquisition unit, and vice versa, the greater the gap is, and D(F u ,F i ) is Y1, when D(F u ,F i ) is less than the face threshold Y1, indicating that the face verification is qualified. u ,F i ) is greater than the facial threshold Y1, it means that the facial features scanned by the current device are too different from the user facial information collected historically, and the facial verification fails.

[0017] Optionally, the voiceprint feature verification unit verification process is as follows:

[0018]

[0019] Among them Svioce Indicates the voiceprint score;

[0020] V base Indicates the basic matching rate, obtained through the voiceprint matching algorithm, with a value range of 0 to 1;

[0021] D t Indicates the facial abnormality score, where the value is 0.2 if the face is occluded and 0 if the face is not occluded;

[0022] R risk Indicates the environmental risk coefficient, with a value range of 0 to 1;

[0023] C d represents the time warping coefficient;

[0024] T e Indicates the device trust score, which is derived from the device matching degree and location information, and ranges from 0 to 1;

[0025] Time warping coefficient C d The process is as follows:

[0026]

[0027] D d Indicates the DTW distance between the user's current voice and voiceprint information;

[0028] D b Indicates the DTW distance in the user's voiceprint information;

[0029] β represents the dynamic adjustment coefficient, β=1+0.5×R risk ;

[0030] Environmental risk factor R risk The process is as follows:

[0031]

[0032] Where ML represents the network environment score;

[0033] SL represents the threat intelligence score;

[0034] Voiceprint score S vioce Indicates the degree of consistency between the current user's voice information and the voiceprint information collected during registration. The voiceprint score S vioce The larger the value, the higher the consistency, and vice versa. Set the voiceprint score S vioce The voiceprint threshold is Y2, when the voiceprint score S vioce When the value is less than the voiceprint threshold Y2, it indicates failure.

[0035] Optionally, the behavior feature verification module analysis process is as follows:

[0036]

[0037]

[0038] Among them S bvc represents the behavioral pattern consistency score;

[0039] σ H Indicates the standard deviation of historical behavior values, indicating the degree of fluctuation of user behavior;

[0040] Z is a positive number, with a value of 10 -5 , used to avoid the denominator being zero;

[0041] ΔV represents the mean rate of change of behavior;

[0042] B x The xth behavior indicator value representing the current behavior mode;

[0043] H x Indicates the xth behavior indicator value in the historical behavior pattern;

[0044] t represents the time difference, which is used to standardize the behavior change, in seconds;

[0045] R risk Indicates the environmental risk coefficient, with a value range of 0 to 1;

[0046] α represents the environmental risk impact weight, ranging from 0.5 to 1.5, and is used to adjust the impact of environmental risks on behavioral patterns;

[0047] Behavior pattern consistency score S bvc Indicates the degree of consistency between the current user's behavior pattern information and the behavior pattern information collected during registration. The behavior pattern consistency score S bvc The larger the value, the more similar the behavior is, and the smaller the value, the greater the change in behavior. Set the behavior pattern consistency score S bvc The behavioral threshold is Y3, when the behavioral pattern consistency score S bvc > behavior threshold Y3, indicating that the verification is qualified, and the user can log in to the system to play the game.

[0048] Optionally, the setting environmental risk coefficient R risk The environmental threshold is 0.6, when the environmental risk coefficient R risk When the value is greater than 0.6, it indicates that the environmental risk is too high. At this time, the iris feature verification module is used to verify the user during the game. The verification process is as follows:

[0049]

[0050] Where D(A u,A i ) represents the difference between the user's stored iris image and the output iris image features;

[0051] E u (k) represents the kth texture feature value of the user's stored iris image;

[0052] E i (k) represents the kth texture eigenvalue of the output iris image;

[0053] W1 represents the texture feature influence coefficient;

[0054] R u (k) represents the kth color feature value of the user's stored iris image;

[0055] R i (k) represents the kth color feature value of the output iris image;

[0056] W2 represents the color feature influence coefficient;

[0057] y is the number of color features and texture features;

[0058] The overall difference between the two iris images is obtained by weighted summation of the differences in iris texture features and color features. The difference D(A) between the user stored iris image and the output iris image features is obtained. u ,A i ) is smaller, the more similar the iris image features are, and according to the iris verification standard and experimental setting D(A u ,A i ) is 0.2, when D(A u ,A i ) is greater than 0.2, indicating failure. In this case, the user needs to re-verify the biometric verification module and the behavioral feature verification module.

[0059] Optionally, when D(A u ,A i ) is greater than 0.2, the game connection will be automatically disconnected and you will need to log in again. At the same time, the facial threshold Y1 will be lowered, and the voiceprint threshold Y2 and behavior threshold Y3 will be increased to improve the strength of identity authentication.

[0060] Optionally, the storage module encrypts data by combining a symmetric encryption algorithm and an asymmetric encryption algorithm when storing data, and configures a key management system for managing encryption and decryption keys to ensure the security of the encrypted data.

[0061] Optionally, the account management module is used to manage user accounts, including registration and login of user accounts, and binding user accounts with virtual reality hardware IDs.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. The present invention collects multiple biometric information and behavioral pattern information of users, and performs verification through the biometric verification module and the behavioral feature verification module during subsequent user login verification. By cooperating with different verification methods, user identity verification is more comprehensive, and the accuracy and security of identity verification are improved. In addition, an environmental risk factor is introduced into the verification process to fully consider the impact of the user's network environment on identity verification. Compared with traditional static verification methods, the complexity of identity verification is increased and the risk of bypass is reduced.

[0064] 2. The present invention analyzes environmental risk conditions at regular intervals during the game and can verify the user's iris information at any time through the iris feature verification module, thereby improving the security of the user account and enabling iris verification only when the environmental risk is too high. This helps to balance security and performance and make rational use of computing resources. When iris information verification fails, the system automatically adjusts the various verification thresholds to increase the strictness of subsequent verifications. This adaptive threshold adjustment method can effectively increase the difficulty of verification and prevent user accounts from being bypassed by malicious attackers, thereby flexibly responding to the dynamically changing virtual reality device usage environment, ensuring the quality of identity authentication, and improving the security of the user identity authentication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a block diagram of the system module of the present invention;

[0066] Figure 2 This is the identity authentication flow chart of the present invention;

[0067] Figure 3 This is a block diagram of the information acquisition module of the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0069] Example 1:

[0070] See also Figures 1 to 3 ,This implementation provides a virtual reality game user identity authentication system, including an account management module, an information collection module, a biometric verification module, a behavioral feature verification module, an iris feature verification module, and a storage module;

[0071] The account management module is used to manage user accounts, including user account registration, login, and binding with VR hardware ID;

[0072] The storage module uses a combination of symmetric and asymmetric encryption algorithms to encrypt data when storing data, and configures a key management system to manage encryption and decryption keys to ensure the security of encrypted data;

[0073] When a user registers an account, the information collection module collects the user's facial information, behavior pattern information, iris information, and voiceprint information, and encrypts and stores it in the storage module for subsequent verification;

[0074] The biometric verification module includes a facial feature verification unit and a voiceprint feature verification unit. The facial feature verification unit compares the current user's face with the user's facial information collected during registration. The voiceprint feature verification unit is used to compare the current user's voice information with the voiceprint information collected during registration, and judge whether the voice is qualified based on the environmental risk situation.

[0075] The behavior feature verification module is used to compare the current user's behavior pattern information with the behavior pattern information collected during registration to determine whether it is qualified;

[0076] During the game, the environmental risk situation is analyzed regularly. When the environmental risk situation is too bad, the iris feature verification module will compare the current user's iris information with the iris information collected during registration. If the comparison fails, the user will be required to log in to the system again.

[0077] More specifically, in this embodiment, the account management module first binds user account information to the virtual reality hardware ID. This ensures that each account can only be used on a specific hardware device, effectively preventing account sharing, theft, or malicious logins. Furthermore, the information collection module collects various biometric and behavioral information during user registration. During subsequent user login verification, verification is performed using the biometric and behavioral verification modules. The interplay of these different verification methods improves the accuracy and security of identity authentication, increases the complexity of user identity authentication, and reduces the risk of bypass. Furthermore, an environmental risk factor is introduced into the verification process to fully consider the impact of the user's network environment on identity authentication. Furthermore, environmental risk conditions are regularly analyzed during gameplay, and the iris feature verification module can be used to verify the user's iris information at any time. In the event of iris verification failure, the system automatically adjusts various verification thresholds to increase the stringency of subsequent verifications. This adaptive threshold adjustment method effectively increases the difficulty of verification and prevents user accounts from being bypassed by malicious attackers. This allows for flexible adaptation to the dynamically changing virtual reality device usage environment, ensures effective identity authentication in high-risk environments, and improves the security of the user identity authentication system.

[0078] Furthermore, the facial feature verification unit verification process is as follows:

[0079]

[0080] Where D(F u ,F i ) represents the user's facial feature vector F u With the input image feature vector F i The Euclidean distance between the user's facial feature vector F u The input image feature vector F is obtained by the facial information acquisition unit. i Derived by the facial feature verification unit;

[0081] F u (k) represents the kth element value of the user's facial feature vector;

[0082] F i (k) the kth element value of the input image feature vector;

[0083] n represents the number of elements;

[0084] By calculating [F u (k)-F i (k)] 2,The purpose is to eliminate positive and negative differences and prevent ,the same differences from canceling each other out due to different ,signs. Secondly, it is to aggravate the impact of larger differences. ,If the difference between two features is large, the square operation will ,increase the impact of their difference, thus strengthening ,the impact of the difference on the final verification result.

[0085] By summing the squared differences across all dimensions, we obtain a total difference. The differences in each dimension are accumulated during the summation process, ultimately reflecting the overall difference in facial features. The summation is intended to comprehensively consider all dimensions of facial features, avoiding focusing on only certain specific features and ensuring verification accuracy.

[0086] The final Euclidean distance is obtained by performing a square root operation on the summation result. The role of the square root operation is to convert the total difference value into an intuitive distance value instead of continuing to exist in the form of a square. The square root helps to ensure that the calculation result conforms to the actual physical distance concept.

[0087] Specifically, the user's facial feature vector F u With the input image feature vector F i The Euclidean distance D(F u ,F i ) is smaller, the more closely the input image matches the facial information obtained by the facial information acquisition unit, and vice versa, the greater the gap is, and D(F u ,F i ) is Y1, and the value of Y1 is 0.4. Different thresholds can be set and the false recognition rate and missed recognition rate of the system can be detected. Then the face threshold Y1 can be set according to actual needs. When D(F u ,F i ) is less than the face threshold Y1, indicating that the face verification is qualified. u ,F i ) is greater than the facial threshold Y1, it means that the facial features scanned by the current device are too different from the user facial information collected historically, and the facial verification fails.

[0088] Furthermore, the voiceprint feature verification unit verification process is as follows:

[0089]

[0090] is the threat correction term, and the nonlinear amplification of the threat level is achieved through exponential operation. When the exponential term 1+R risk Increased, exponential increase leads to:

[0091] like The voiceprint score is reduced at an accelerated rate. The voiceprint score is penalized in reverse, so that the voiceprint feature verification unit focuses on the quality of the voiceprint information itself and the impact of the environment;

[0092] is the device trust compensation term, and the time warping coefficient C d and device trust score e Multiplication can establish device trust score e and the time warping coefficient C d Collaborative verification mode, when the device trusts the e When the time warping coefficient C is low, d High, the overall contribution will also be reduced, thereby increasing security, the denominator size can represent the voiceprint score S vioce The denominator is set to 3 to reduce the impact of the device trust compensation item on the voiceprint feature verification unit, ensuring that more attention is paid to the quality of the voiceprint information itself;

[0093] And add the above two items together to perform multimodal information fusion;

[0094] Among them S vioce Indicates the voiceprint score;

[0095] V base Represents the basic matching rate, obtained by the voiceprint matching algorithm, with a value range of 0 to 1, where 1 indicates a perfect match. The voiceprint matching algorithm is cosine similarity, which measures similarity by calculating the cosine value of the angle between two voiceprint feature vectors;

[0096] D t Indicates the facial abnormality score, where the value is 0.2 if the face is occluded and 0 if the face is not occluded;

[0097] R risk Indicates the environmental risk coefficient, with a value range of 0 to 1;

[0098] C d Indicates the time warping coefficient, ranging from 0 to 1.2. The larger the value, the better the sound matching.

[0099] T e Indicates the device trust score, which is derived from the device matching degree and location information. The value ranges from 0 to 1, and the initial value is set to 1. The trust score will decrease when encountering a new device or unfamiliar network;

[0100] Specifically, the voiceprint score S vioce Indicates the degree of consistency between the current user's voice information and the voiceprint information collected during registration. The voiceprint score S vioce The larger the value, the higher the consistency, and vice versa. Set the voiceprint score S vioce The voiceprint threshold is Y2, when it is less than the voiceprint score S vioce, it means the voiceprint verification failed.

[0101] Time warping coefficient C d The process is as follows:

[0102]

[0103] D d Indicates the DTW distance between the user's current voice and voiceprint information;

[0104] D b Indicates the DTW distance in the user's voiceprint information. The average DTW distance is taken when the information collection module collects the user's voiceprint information. For example, if the user's voiceprint is collected five times, the average DTW distance of these five times is calculated and taken.

[0105] β represents the dynamic adjustment coefficient, β=1+0.5×R risk ;

[0106] Environmental risk factor R risk The process is as follows:

[0107]

[0108] Where ML is the network environment score;

[0109]

[0110] SL is the threat intelligence score;

[0111]

[0112] If multiple conditions are triggered for the threat intelligence score SL, the condition with the largest result value is used.

[0113] Specifically, by introducing the environmental risk factor R risk , can automatically adjust the strictness of identity authentication according to the real-time risk level of the user's environment, and the environmental risk coefficient R risk The larger it is, the higher the risk of the user's current network environment and the less secure the account is. This allows the system to fully consider the impact of the user's network environment on identity authentication, thereby flexibly responding to the changing virtual reality device usage environment and improving the security of the user identity authentication system.

[0114] Furthermore, the analysis process of the behavioral feature verification module is as follows:

[0115]

[0116] Among them S bvc represents the behavioral pattern consistency score;

[0117] σ HIndicates the standard deviation of historical behavior values, indicating the degree of fluctuation of user behavior;

[0118] Z is a positive number, with a value of 10 -5 , used to avoid the denominator being zero;

[0119] ΔV represents the mean rate of change of behavior;

[0120] B x Indicates the xth behavioral indicator value of the current behavior pattern, such as click frequency, head turning angle, walking rhythm, etc.

[0121] H x Indicates the xth behavior indicator value in the historical behavior pattern;

[0122] t represents the time difference, which is used to standardize the behavior change, in seconds;

[0123] R risk Indicates the environmental risk coefficient, with a value range of 0 to 1;

[0124] α represents the environmental risk impact weight, ranging from 0.5 to 1.5, and is used to adjust the impact of environmental risks on behavioral patterns;

[0125] The behavioral feature verification module confirms the user's identity by analyzing their behavioral characteristics. Unlike traditional static verification methods such as passwords and fingerprints, behavioral pattern verification is dynamic and difficult to forge. Even if an attacker obtains the user's account information or device, the system will reject their login request if the behavioral pattern does not match.

[0126] Behavior pattern consistency score S bvc Indicates the degree of consistency between the current user's behavior pattern information and the behavior pattern information collected during registration. The behavior pattern consistency score S bvc The larger the value, the more similar the behavior is, and the smaller the value, the greater the change in behavior. Set the behavior pattern consistency score S bvc The behavioral threshold is Y3, when the behavioral pattern consistency score S bvc When the threshold exceeds Y3, the user passes verification and can log in to the system to play games. By adding the behavioral verification module after passing the biometric verification module, user verification becomes more complex. Furthermore, environmental risk factors are introduced into both the biometric and behavioral verification processes, ensuring verification strength and improving the accuracy of identity authentication.

[0127] Furthermore, through multiple experiments and analysis of user behavior patterns, the environmental risk coefficient R is set. risk The environmental threshold is 0.6, when the environmental risk coefficient R riskWhen the value is greater than 0.6, it indicates that the environmental risk is too high. At this time, the iris feature verification module is used to verify the user during the game. The verification process is as follows:

[0128]

[0129] Where D(A u ,A i ) represents the difference between the user's stored iris image and the output iris image features. The smaller the difference, the more similar the iris features are.

[0130] E u (k) represents the kth texture feature value of the user's stored iris image. Texture features include pupil, iris ring and cornea, etc.

[0131] E i (k) represents the kth texture eigenvalue of the output iris image;

[0132] W1 represents the texture feature influence coefficient;

[0133] R u (k) indicates the kth color feature value of the user-stored iris image, such as iris spots, color difference, etc.;

[0134] R i (k) represents the kth color feature value of the output iris image;

[0135] W2 represents the color feature influence coefficient;

[0136] y is the number of color features and texture features;

[0137] Specifically, the overall difference between the two iris images is obtained by weighted summation of the differences in iris texture features and color features. The difference D(A) between the user stored iris image and the output iris image features is u ,A i ) is smaller, the more similar the iris image features are, and according to the iris verification standard and experimental setting D(A u ,A i ) is 0.2, when D(A u ,A i ) is greater than 0.2, indicating failure. In this case, the user needs to re-verify the biometric verification module and the behavioral feature verification module.

[0138] Since the virtual reality game environment is dynamic, the user's network status, device, and external threat intelligence change at any time. The system determines whether to initiate stricter identity authentication based on the real-time environmental risk value. When the environmental risk is too high, real-time iris verification of users in the game can improve the security of user accounts. Iris verification is only enabled when the environmental risk is too high, which helps to balance security and performance and make rational use of computing resources.

[0139] Furthermore, when D(A u ,A i ) is greater than 0.2, the game connection is automatically disconnected and re-login is required. At the same time, the facial threshold Y1 is lowered, and the voiceprint threshold Y2 and behavior threshold Y3 are increased to improve the strength of identity authentication;

[0140] The specific adjustment process is as follows:

[0141] The process of adjusting the voiceprint threshold Y2 is as follows:

[0142]

[0143] Where NY1 is the new face threshold;

[0144] NY2 is the new voiceprint threshold;

[0145] NY3 is the new behavior threshold;

[0146] The denominator of 3 represents the adjustment strength of the facial threshold Y1. The smaller the denominator, the greater the adjustment strength, and vice versa. It can be set according to needs in actual use.

[0147] Specifically, when the user's iris verification fails, it also indicates that the environmental risk is too high. In order to prevent the leakage of game account information, the security verification strength needs to be improved. Therefore, by adjusting the various thresholds, the system can adapt to changes in the user's gaming environment, ensure the identity authentication effect in high-risk environments, and ensure the security of the account.

[0148] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A virtual reality game user identity authentication system, characterized in that: It includes account management module, information collection module, biometric verification module, behavioral feature verification module, iris feature verification module, and storage module; The information collection module collects user facial information, behavior pattern information, iris information and voiceprint information, and stores them through the storage module; The biometric verification module includes a facial feature verification unit and a voiceprint feature verification unit. The facial feature verification unit compares the current user's face with the user's facial information collected during registration. The voiceprint feature verification unit is used to compare the current user's voice information with the voiceprint information collected during registration, and judge whether the voice is qualified in combination with the environmental risk situation. The voiceprint feature verification unit verification process is as follows: ； Among them S vioce Indicates the voiceprint score; V base Indicates the basic matching rate, obtained through the voiceprint matching algorithm, with a value range of 0 to 1; D t Indicates the facial abnormality score, where the value is 0.2 if the face is occluded and 0 if the face is not occluded; R risk Indicates the environmental risk coefficient, with a value range of 0 to 1; C d represents the time warping coefficient; T e Indicates the device trust score, which is derived from the device matching degree and location information, and ranges from 0 to 1; Time warping coefficient C d The process is as follows: ； D d Indicates the DTW distance between the user's current voice and voiceprint information; D b Indicates the DTW distance in the user's voiceprint information; β represents the dynamic adjustment coefficient, β=1+0.5×R risk ; Environmental risk factor R risk The process is as follows: ； Where ML represents the network environment score; SL represents the threat intelligence score; Voiceprint score S vioce Indicates the degree of consistency between the current user's voice information and the voiceprint information collected during registration. The voiceprint score S vioce The larger the value, the higher the consistency, and vice versa. Set the voiceprint score S vioce The voiceprint threshold is Y2, when the voiceprint score S vioce When the value is less than the voiceprint threshold Y2, it means it is unqualified; The behavior feature verification module is used to compare the current user's behavior pattern information with the behavior pattern information collected during registration to determine whether it is qualified; During the game, the environmental risk situation is analyzed regularly. When the environmental risk situation is too bad, the iris feature verification module compares the current user's iris information with the iris information collected during registration. If the comparison fails, the user needs to log in to the system again.

2. The virtual reality game user identity authentication system according to claim 1, characterized in that: The facial feature verification unit verification process is as follows: ； Where D(F u ,F i ) represents the user's stored facial feature vector F u With the input facial feature vector F i The Euclidean distance between F u (k) represents the kth element value of the user's facial feature vector; F i (k) represents the kth element value of the input image feature vector; n represents the number of elements; User's facial feature vector F u With the input image feature vector F i The Euclidean distance D(F u ,F i ) is smaller, the more closely the input image matches the facial information obtained by the facial information acquisition unit, and vice versa, the greater the gap is, and D(F u ,F i ) is Y1, when D(F u ,F i ) is less than the face threshold Y1, indicating that the face verification is qualified. u ,F i ) is greater than the facial threshold Y1, it means that the facial features scanned by the current device are too different from the user facial information collected historically, and the facial verification fails.

3. The virtual reality game user identity authentication system according to claim 2, characterized in that: Set the environmental risk factor R risk The environmental threshold is 0.6, when the environmental risk coefficient R risk When the value is greater than 0.6, it indicates that the environmental risk is too high. At this time, the iris feature verification module is used to verify the user during the game. The verification process is as follows: ; Where D(A u ,A i ) represents the difference between the user's stored iris image and the output iris image features; E u (k) represents the kth texture feature value of the user's stored iris image; E i (k) represents the kth texture eigenvalue of the output iris image; W1 represents the texture feature influence coefficient; R u (k) represents the kth color feature value of the user's stored iris image; R i (k) represents the kth color feature value of the output iris image; W2 represents the color feature influence coefficient; y is the number of color features and texture features; The overall difference between the two iris images is obtained by weighted summation of the differences in iris texture features and color features. The difference D(A) between the user stored iris image and the output iris image features is obtained. u ,A i ) is smaller, the more similar the iris image features are, and according to the iris verification standard and experimental setting D(A u ,A i ) is 0.2, when D(A u ,A i ) is greater than 0.2, indicating failure. In this case, the user needs to re-verify the biometric verification module and the behavioral feature verification module.

4. The virtual reality game user identity authentication system according to claim 3, characterized in that: When D(A u ,A i ) is greater than 0.2, the game connection will be automatically disconnected and you will need to log in again. At the same time, the facial threshold Y1 will be lowered, and the voiceprint threshold Y2 and behavior threshold Y3 will be increased to improve the strength of identity authentication.

5. The virtual reality game user identity authentication system according to claim 1, characterized in that: The storage module encrypts data by combining a symmetric encryption algorithm and an asymmetric encryption algorithm when storing data, and configures a key management system for managing encryption and decryption keys to ensure the security of the encrypted data.

6. The virtual reality game user identity authentication system according to claim 1, characterized in that: The account management module is used to manage user accounts, including registration and login of user accounts, and binding user accounts with virtual reality hardware IDs.

Images