An information-based injury ticket assignment system based on facial recognition

Abstract

The present invention provides an information-based injury ticket granting system based on facial recognition, which relates to the field of identity authentication technology. The system includes a physiological state monitoring module, an authority adjustment control module, an identity adaptability matching module, a gait behavior verification module, and a terminal data linkage module. By analyzing eye movements, blinking frequency, pupil changes, and facial muscle activity, the individual's physiological state is monitored to achieve dynamic authority adjustment during identity authentication. Micro-expression analysis optimizes matching accuracy and reduces mismatches caused by emotional fluctuations. Gait analysis strengthens the adaptability of identity verification through behavioral data and maintains a high accuracy rate. The data linkage mechanism between multiple devices improves the real-time performance of ticket management and access control. Combined with multi-dimensional biometrics, the security of verification and the stability of remote operation are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of identity authentication, and in particular to an information-based injury ticket granting system based on facial recognition. Background Art

[0002] The field of identity verification technology encompasses technologies that verify personal identity through methods such as biometrics, passwords, and tokens. Biometric-based authentication primarily includes fingerprint, iris, and facial recognition, leveraging computer vision, pattern recognition, and artificial intelligence technologies to extract, analyze, and compare individual features to confirm user identity. Facial recognition technology, a key component of this technology, captures facial images and utilizes feature point analysis and deep learning models for matching and identification, enabling identity recognition and authorization verification. This technology is widely used in a variety of fields, including financial payments, security monitoring, access control, and public transportation. While improving verification efficiency and security, it also places higher demands on device performance, algorithm optimization, and data security.

[0003] Among them, an information-based ticket granting system based on facial recognition refers to a device that uses facial recognition technology to verify the identity of a specific group and grants electronic or physical tickets after the verification is passed. The system obtains facial information through an image acquisition unit, and uses facial feature extraction and comparison methods to perform identity matching based on a preset database to complete the identity verification process. After the verification is passed, the system combines with the ticket management system to generate or retrieve the corresponding ticket information, and completes the ticket issuance in the form of electronic storage or physical printing. The system usually uses image processing technology for face detection and feature point recognition, and uses classification algorithms to complete identity matching to ensure the accuracy and uniqueness of the ticketing process. At the same time, combined with database query logic, it realizes ticket distribution and management.

[0004] Existing technologies rely on a single biometric feature for identity authentication and lack awareness of changes in user status, which may affect verification accuracy in situations of distraction, fatigue, or emotional fluctuations. Permission management uses fixed rules and fails to dynamically adjust according to the user's current status. Sensitive operating permissions may still be open when it is inappropriate, increasing the risk of misoperation or security. Facial recognition technology mainly relies on static feature matching, which is difficult to effectively adapt to changes in individual micro-expressions and is prone to matching failures or misidentification due to temporary facial abnormalities. Gait verification is mostly based on a single comparison and is not dynamically corrected in combination with user behavior characteristics. It is difficult to maintain accuracy when environmental factors change. In addition, existing technologies have limited data synchronization capabilities between devices, and authentication and permission adjustment results cannot be quickly shared to other terminals, affecting the security and consistency of cross-device operations and increasing management complexity. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an information-based injury ticket granting system based on facial recognition.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an information-based injury ticket assignment system based on facial recognition includes:

[0007] The physiological status monitoring module acquires facial data, calculates eye trajectory direction rate, blink rate and closure duration, pupil diameter change and response time, analyzes facial muscle activity, calculates the drooping ratio of the mouth corners and the stretching rate of the muscles around the eyes, and ultimately determines the physiological status level.

[0008] The authority adjustment control module calls the physiological status level, divides the authority level according to the threshold, adjusts the injury ticket modification and information change authority, controls remote scheduling access, and calculates the recovery conditions to obtain the injury ticket operation authority;

[0009] The identity adaptive matching module obtains micro-expression data based on the injury ticket operation authority, calculates the ratio of eye corner contraction, forehead stretching, and mouth corner change, analyzes the matching error, adjusts the matching parameters according to the emotional state, and obtains the identity matching adjustment value;

[0010] The gait behavior verification module calls the identity matching adjustment value, obtains gait data to calculate step length, step frequency stability, and center of gravity offset angle, analyzes gait trajectory deviation, and obtains a gait verification matching value;

[0011] The terminal data linkage module calls the injury ticket authority and gait matching value, calculates the device authority and synchronization status, sends a permission update instruction to the logged-in device, and obtains the terminal synchronization status.

[0012] Optionally, the physiological state level includes eye movement state, blinking pattern, pupil response characteristics, and facial muscle activity; the injury ticket operation authority includes authority level, executable operation range, and recovery conditions; the identity matching adjustment value includes micro-expression matching error, emotion matching parameter, and identity adaptability correction value; the gait verification matching value includes pace characteristics, cadence stability, center of gravity offset range, and trajectory matching coefficient; the terminal synchronization status includes device authority synchronization, identity matching update, and login device status.

[0013] Optionally, the physiological status monitoring module includes:

[0014] The eye movement submodule obtains eye position parameters from facial region data, calculates the eye displacement direction vector and instantaneous rate, filters the rate fluctuation interval based on the angular velocity change between consecutive frames, analyzes the rate change mean and deviation range, and determines the movement trend based on the trajectory offset direction to obtain the trajectory offset dynamic change rate;

[0015] The blink rate submodule uses the trajectory offset dynamic change rate to detect the eyelid opening and closing status of consecutive frames, calculates the number of blinks and the duration of closure per unit time, filters out abnormal closure duration intervals, determines the fluctuation characteristics of blink frequency and closure duration, and obtains the eye closure rate interval;

[0016] The pupil change submodule calls the eye closure rate interval, extracts pupil diameter change data, calculates the contraction amplitude and dilation time ratio, selects the response time interval based on the diameter change gradient of the continuous time period, calculates the mean of the contraction and dilation response time, and obtains the pupil contraction dynamic ratio;

[0017] The facial muscle activity submodule calls the pupil contraction dynamic ratio, obtains the drooping angle of the mouth corners and the stretching displacement of the muscles around the eyes, calculates the mouth corner displacement rate and stretching ratio per unit time, screens the abnormal stretching rate interval, calculates the overall facial muscle activity trend, and obtains the physiological state level.

[0018] Optionally, the eyeball displacement direction vector and instantaneous rate calculation formula are specifically:

[0019] ;

[0020] in, Represents the eyeball displacement direction vector and instantaneous velocity, Represents the number of frames in the calculation time window, Representative The instantaneous frame rate, Representative The weight factor of the frame, represents the weighted average rate, Representative The angular acceleration of the frame, represents the mean angular acceleration, represents the variance of angular acceleration, Representative The frame's motion direction angle, Representative The frame's motion direction angle, Represents the total direction change in the entire time window, Represents the total duration of the calculation time window.

[0021] Optionally, the authority adjustment control module includes:

[0022] The permission classification submodule calls the physiological state level, sets the boundary values ​​of high permission, limited permission, and minimum permission according to the permission threshold range corresponding to the physiological state, calculates the permission category corresponding to the current physiological state, and obtains the physiological state permission level;

[0023] The permission calculation submodule calls the physiological status permission level, obtains the injury ticket permission configuration data, screens the executable operations that meet the current permission level, calculates the currently adjustable injury ticket modification permission, information change permission, and remote scheduling access permission, screens the adjustment range based on the permission configuration table, and obtains the injury ticket adjustment permission set;

[0024] The permission recovery submodule calls the injury ticket adjustment permission set, calculates the change trend of the physiological status level, filters the physiological status change interval corresponding to the recovery condition, determines the recoverable permission category based on the permission recovery threshold, and obtains the injury ticket operation permission.

[0025] Optionally, the identity adaptability matching module includes:

[0026] The micro-expression feature submodule calls the ticket operation permission to obtain facial micro-expression data, extracts the degree of eye corner contraction, forehead stretching, and mouth corner change ratio, calculates the feature change rate per unit time, screens the feature mean value range in the stable state, determines the feature offset trend, and obtains the micro-expression feature offset value;

[0027] The matching error calculation submodule calls the micro-expression feature offset value, obtains the identity data of the ticket holder, extracts the facial expression parameters in the identity record, calculates the numerical difference between the current micro-expression feature and the identity data, selects the error interval of the feature, calculates the overall matching deviation mean, and obtains the micro-expression matching error;

[0028] The matching parameter adjustment submodule calls the micro-expression matching error, extracts the matching error correction parameter according to the relaxation, tension, and anxiety states, calculates the matching parameter adjustment amplitude in each state, selects the applicable correction interval, calculates the change trend of the corrected matching parameter, and obtains the identity matching adjustment value.

[0029] Optionally, the characteristic change rate calculation formula per unit time is specifically:

[0030] ;

[0031] in, Represents the rate of change of characteristics per unit time, Represents the number of frames in the calculation time window, Representative The degree of eye contraction of the frame, Representative The timestamp of the frame, Representative The forehead stretching range of the frame, Represents the mean forehead stretch value within the calculation time window, represents the forehead stretch variance, Representative The mouth corner change ratio of the frame, Represents the mouth corner change ratio of the previous frame, Represents the total change in the mouth corner change ratio within the calculation time window.

[0032] Optionally, the gait behavior verification module includes:

[0033] The gait parameter calculation submodule calls the identity matching adjustment value, obtains gait behavior data, extracts stride length, stride frequency variation range, and center of gravity offset angle, calculates the time series change rate of gait parameters, selects the stride adjustment interval under stable state, calculates the mean value and fluctuation trend of gait characteristics, and obtains a gait characteristic parameter set;

[0034] The trajectory deviation calculation submodule calls the gait feature parameter set, obtains the verification terminal interaction data, extracts the gait trajectory deviation value, calculates the displacement error between the gait motion trajectory and the standard gait path, filters out abnormal deviation points in the gait change, calculates the overall trajectory deviation mean and the gait continuity change trend, and obtains the gait trajectory deviation value;

[0035] The matching stability evaluation submodule calls the gait trajectory deviation value, extracts the gait parameter fluctuation range, calculates the joint stability index of step frequency, stride length and trajectory deviation, screens the matching stability parameter range, calculates the overall stability coefficient of gait behavior, and obtains the gait verification matching value.

[0036] Optionally, the terminal data linkage module includes:

[0037] The authority status calculation submodule calls the ticket operation authority and gait verification matching value, obtains the terminal device information, extracts the current device authority configuration and synchronization status parameters, calculates the joint weight of the device authority and identity matching status, filters the current device executable authority range, calculates the authority status adjustment parameter, and obtains the terminal authority synchronization parameter;

[0038] The device data synchronization submodule calls the terminal authority synchronization parameters, sends a data update instruction to the logged-in device that matches the ticket authority and identity, extracts the device response delay and data update status, calculates the device response stability index, screens the data interval for successful synchronization, calculates the synchronization deviation of the terminal authority and identity matching, and obtains the terminal synchronization status data;

[0039] The terminal linkage evaluation submodule calls the terminal synchronization status data, calculates the synchronization consistency index of the multi-device linkage status, filters the permission conflict interval that occurs during the terminal data linkage process, analyzes the dynamic adjustment coefficient of the permission and identity matching between devices, filters the scope of linkage matching, and obtains the terminal synchronization status.

[0040] Optionally, the synchronization consistency index calculation formula is specifically:

[0041] ;

[0042] Calculate the synchronization consistency index, analyze the dynamic adjustment coefficient of the authority and identity matching between devices, filter the scope of linkage matching, and obtain the terminal synchronization status;

[0043] in, represents the synchronization consistency indicator, Represents the total number of terminals analyzed, Representative The permission value of each terminal, Represents the average authority value of all terminals, represents the standard deviation of the authority values, Represents the conflict determination threshold.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by analyzing eye movements, blinking frequency, pupil changes and facial muscle activities, the individual's physiological state is monitored, and dynamic permission adjustment is achieved during identity authentication. Micro-expression analysis optimizes matching accuracy and reduces false matches caused by emotional fluctuations. Gait analysis strengthens the adaptability of identity verification through behavioral data and maintains high accuracy. The data linkage mechanism between multiple devices improves the real-time performance of ticket management and access control. Combined with multi-dimensional biometrics, the security of verification and the stability of remote operation are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0047] Figure 1 is a system flow chart of the present invention;

[0048] Figure 2 It is a submodule flow chart of the present invention;

[0049] Figure 3 This is a flow chart of the physiological status monitoring module of the present invention;

[0050] Figure 4 This is a flowchart of the authority adjustment control module of the present invention;

[0051] Figure 5 This is a flow chart of the identity adaptive matching module of the present invention;

[0052] Figure 6This is a flow chart of the gait behavior verification module of the present invention;

[0053] Figure 7 This is a flow chart of the terminal data linkage module of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0056] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0057] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] See also Figure 1 , an information-based injury ticket granting system based on facial recognition includes:

[0060] S100: The physiological status monitoring module obtains facial area data, uses eye movement trajectory to calculate direction rate changes, uses blink rate to calculate blink frequency and closure duration, uses pupil diameter changes to calculate contraction amplitude and response time, and uses facial muscle activity to calculate mouth corner droop ratio and eye muscle stretch rate to obtain the physiological status level;

[0061] S200: The authority adjustment control module calls the physiological status level, divides it into high authority, restricted authority, and minimum authority based on the threshold, obtains the injury ticket authority configuration data to calculate the currently executable operations, adjusts the injury ticket modification, information change, and remote scheduling access rights, calculates the restoration conditions based on the physiological status change trend, and obtains the injury ticket operation authority;

[0062] S300: The identity adaptive matching module invokes the injury ticket operation authority to obtain facial micro-expression data, calculate the degree of eye corner contraction, forehead stretching, and mouth corner change ratio, obtains the injury ticket holder's identity data, calculates the micro-expression data matching error, calculates matching parameters based on relaxation, tension, and anxiety states, and obtains the identity matching adjustment value;

[0063] S400: The gait behavior verification module calls the identity matching adjustment value, obtains gait behavior data to calculate step length, cadence stability, and center of gravity offset angle, obtains verification terminal interaction data, calculates gait trajectory deviation, calculates the matching stability coefficient based on the gait parameter fluctuation range, and obtains the gait verification matching value;

[0064] S500. The terminal data linkage module calls the ticket operation authority and gait verification matching value to obtain the terminal device information calculation authority and synchronization status, sends the ticket authority and identity matching data update instruction to the logged-in device, and obtains the terminal synchronization status.

[0065] The physiological state level includes eye movement state, blinking pattern, pupil response characteristics, and facial muscle activity. The injury ticket operation authority includes authority level, executable operation range, and recovery conditions. The identity matching adjustment value includes micro-expression matching error, emotion matching parameters, and identity adaptability correction value. The gait verification matching value includes pace characteristics, cadence stability, center of gravity offset range, and trajectory matching coefficient. The terminal synchronization status includes device authority synchronization, identity matching update, and login device status.

[0066] See also Figure 3 and Figure 2 , the physiological status monitoring module includes:

[0067] S110, the eye movement submodule obtains eye position parameters from the facial region data, calculates the eye displacement direction vector and instantaneous rate, filters the rate fluctuation interval based on the angular velocity change between consecutive frames, analyzes the rate change mean and deviation range, determines the movement trend based on the trajectory offset direction, and obtains the trajectory offset dynamic change rate;

[0068] The calculation formula of eyeball displacement direction vector and instantaneous rate is as follows:

[0069] ;

[0070] in, Represents the eyeball displacement direction vector and instantaneous velocity, Represents the number of frames in the calculation time window, Representative The instantaneous frame rate, Representative The weight factor of the frame, represents the weighted average rate, Representative The angular acceleration of the frame, represents the mean angular acceleration, represents the variance of angular acceleration, Representative The frame's motion direction angle, Representative The frame's motion direction angle, Represents the total direction change in the entire time window, Represents the total duration of the calculation time window:

[0071] This formula is used to adjust for fluctuations in eye movement rate, integrating changes in eye velocity, acceleration, and angular velocity. Parameters considered include instantaneous velocity , angle change between frames , and inter-frame acceleration The following is a detailed explanation of the parameters and the formula derivation process:

[0072] Rate adjustment calculation:

[0073] Assume that in the time window Inside, there Frame data.

[0074] Instantaneous rate Extracted from the video frames by image recognition software, the rate per frame might be as follows (taking the first 5 data samples):

[0075] ;

[0076] Weight Set to be inversely related to the rate, calculate the weight of each frame, e.g. , the first 5 data may be:

[0077] ;

[0078] The weighted average rate is calculated as:

[0079] ;

[0080] Calculate rate fluctuation adjustment value :

[0081] ;

[0082] Angular acceleration variance calculation:

[0083] Assuming angular acceleration (Take the first 5 data examples) as follows:

[0084] ;

[0085] Mean angular acceleration Calculated as:

[0086] ;

[0087] Angular acceleration variance Calculated as:

[0088] ;

[0089] Total angle change:

[0090] Assumed angle The changes (taking the first 5 data examples) are:

[0091] ;

[0092] The total angle change is calculated as:

[0093] ;

[0094] Angle change term Calculated as:

[0095] ;

[0096] Substitute the above calculation results into the initial formula to calculate the final This result indicates that the overall fluctuation of eye movement rate has been adjusted to accurately assess the dynamic trend of eye movement.

[0097] S120: The blink rate submodule calls the trajectory offset dynamic change rate to detect the eyelid opening and closing status of consecutive frames, calculates the number of blinks and the duration of closure per unit time, filters out abnormal closure duration intervals, determines the fluctuation characteristics of blink frequency and closure duration, and obtains the eye closure rate interval;

[0098] Using the eyelid opening and closing degree detection model, the eye area of ​​consecutive frames is binarized to extract the eyelid distance , calculate the standardized eyelid opening and closing status index ,in and The eyelid distance of an individual in the closed and open eyes states is respectively, according to the set eye closure judgment threshold ,filter The frames are taken as closed eye frames and the number of closed eyes per unit time is calculated. and duration of eye closure , in the inter-frame time interval If the blink frequency is known, the blink frequency can be calculated ,in is the observation duration. For example, if 20 blinks are detected in a 10-second sampling window, Hz, for abnormal detection of eye closure duration, calculate its mean and standard deviation , filter out of range The eye closure events are detected and their distribution is calculated. The interval data of eye closure rate is screened by combining the fluctuation characteristics of blink frequency and eye closure time.

[0099] S130: The pupil change submodule calls the eye closure rate interval, extracts pupil diameter change data, calculates the contraction amplitude and dilation time ratio, selects the response time interval based on the diameter change gradient of the continuous time period, calculates the mean of the contraction and dilation response time, and obtains the pupil contraction dynamic ratio;

[0100] Extract pupil diameters from consecutive frames using facial video data normalized by illumination environment , calculate the diameter change between each frame , define the contraction amplitude as the maximum change , the expansion time ratio is calculated as ,in is the contraction time period, is the dilation time period. For example, in a 5-second sampling window, if pupil contraction lasts for 1.2 seconds and dilation lasts for 2.8 seconds, then , based on the diameter change gradient of continuous time periods Calculate the response time interval and filter the absolute value of the gradient to be greater than the set threshold The time period of the contraction response time is calculated and expansion response time , and finally obtain the pupil contraction dynamic ratio data.

[0101] S140: The facial muscle activity submodule calls the pupil contraction dynamic ratio to obtain the drooping angle of the mouth corners and the stretching displacement of the muscles around the eyes, calculates the mouth corner displacement rate and stretching ratio per unit time, screens the abnormal stretching rate interval, calculates the overall facial muscle activity trend, and obtains the physiological state level;

[0102] Analyze the dynamic changes of mouth corner displacement and eye muscle stretching. First, extract the coordinates of the key points of the mouth corners. Calculate mouth corner displacement , calculate the displacement rate of the mouth corner per unit time , extract the tensile deformation of the periocular muscles , define the stretch ratio as ,in The maximum stretching amount is used as a reference, the abnormal stretching rate interval is screened, the overall facial muscle activity trend is calculated, and the final physiological state level data is obtained.

[0103] See also Figure 4 and Figure 2 , the permission adjustment control module includes:

[0104] S210, the permission classification submodule calls the physiological state level, sets the boundary values ​​of high permission, limited permission, and minimum permission according to the permission threshold range corresponding to the physiological state, calculates the permission category corresponding to the current physiological state, and obtains the physiological state permission level;

[0105] First, the physiological state parameters of the current individual are read. These parameters are derived from the comprehensive calculation results of multiple indicators such as facial muscle activity, pupil contraction ratio, blinking rate, etc., and are stored in the physiological state database. Then, the authority threshold range is set. According to the fluctuation of the individual's historical physiological state data, the boundary values ​​of high authority, restricted authority, and minimum authority are divided. Among them, the authority threshold is set in the form of a baseline value plus a deviation interval. For example, in the monitoring data of the past month, the average value of the physiological state value is set to , the standard deviation is set to , then the high authority threshold can be set to , the restricted permissions are , the minimum permission range is less than Next, the permission category corresponding to the current physiological state is calculated by comparing the real-time physiological state value with the preset threshold range. If the current state value falls into the high permission range, it is set to high permission; if it falls into the restricted permission range, it is set to restricted permission; if it is less than the minimum permission threshold, it is set to the minimum permission. For example, the current physiological state value is , then its permission category is determined to be restricted permission, and the physiological status permission level is finally obtained.

[0106] S220: The permission calculation submodule calls the physiological status permission level, obtains the injury ticket permission configuration data, screens the executable operations that meet the current permission level, calculates the currently adjustable injury ticket modification permission, information change permission, and remote scheduling access permission, screens the adjustment range based on the permission configuration table, and obtains the injury ticket adjustment permission set;

[0107] After reading the current permission level, obtain the injury ticket permission configuration data. The configuration data contains a list of operations that can be executed at each permission level and is stored in the permission database. Filter the executable operations that meet the current permission level according to the permission level, set the filtering conditions, traverse the permission configuration data table, and find the permission category matching items. For example, in the permission table, high permissions allow modification of all injury ticket information, restricted permissions allow partial modification, and the lowest permissions allow only viewing. When the current permission level is restricted, the filtered executable operations are partial injury ticket modification permissions. Next, calculate the currently adjustable injury ticket modification permissions, information change permissions, and remote scheduling access permissions. Call the configuration items of each permission category separately to confirm the current user's permission range. For example, for injury ticket modification permissions, if the modification permission level corresponding to the current permission level is 2 (where level 1 represents viewing only, level 2 represents partial modification, and level 3 represents full modification), the currently executable injury ticket modification operations are limited to partial adjustments. Remote scheduling access permissions are also filtered according to the same rules, and the baseline permission level value is set. , calculate the permission adjustment range ,in The threshold for permission adjustment range. For example, for a certain user, the base permission level is 2. , the adjustment range is , combined with the permission configuration table for screening, and finally obtain the injury ticket adjustment permission set.

[0108] S230: The permission recovery submodule calls the injury ticket adjustment permission set, calculates the change trend of the physiological status level, selects the physiological status change interval corresponding to the recovery condition, determines the restorable permission category based on the permission recovery threshold, and obtains the injury ticket operation permission;

[0109] Read the current user's physiological status level data, calculate its change trend, and define the physiological status change rate , the calculation formula is , where and Represent the physiological state values ​​of the current and previous moments respectively, Represents a time interval. For example, if a user's physiological state has changed from Upgrade to ,but , then, screen the physiological state change interval corresponding to the recovery conditions and set the recovery threshold ,when When the threshold is exceeded, it is determined to be a recoverable state, for example, , if the current physiological state change rate is , then the recovery conditions are met. Then, the recoverable permission category is determined based on the permission recovery threshold, and the current permission category is compared with the permission recovery threshold range. If the current permission category is in restricted permission but the recovery conditions are met, the permission can be upgraded to high permission, and finally the ticket operation permission is obtained.

[0110] See also Figure 5 and Figure 2 , the identity adaptive matching module includes:

[0111] S310: The micro-expression feature submodule invokes the ticket operation permission to obtain facial micro-expression data, extracts the degree of eye corner contraction, forehead stretching, and mouth corner change ratio, calculates the feature change rate per unit time, screens the feature mean value range in a stable state, determines the feature offset trend, and obtains the micro-expression feature offset value;

[0112] The specific formula for calculating the characteristic change rate per unit time is:

[0113] ;

[0114] in, Represents the rate of change of characteristics per unit time, Represents the number of frames in the calculation time window, Representative The degree of eye contraction of the frame, Representative The timestamp of the frame, Representative The forehead stretching range of the frame, Represents the mean forehead stretch value within the calculation time window, represents the forehead stretch variance, Representative The mouth corner change ratio of the frame, Represents the mouth corner change ratio of the previous frame, Represents the total change in the mouth corner change ratio within the calculation time window:

[0115] This formula calculates the rate of change of micro-expression features per unit time, taking into account the dynamic changes in the degree of eye corner contraction, forehead stretch, and mouth corner change ratio. Frame data within the time window is used to calculate the trend of micro-expression features and filter the stable state range.

[0116] Parameter acquisition method and value setting:

[0117] Frame rate Determined by the frame rate and sampling time of the monitoring device. For example, if the frame rate is set to 30fps and the sampling time is set to 5 seconds, then Timestamp Obtained by accumulating frame times, for example

[0118] , , … .

[0119] Degree of eye canthus contraction Calculated by the change of facial key point coordinates, such as partial data measured in the sampling frame .

[0120] Forehead stretch Calculate the vertical distance of key points on the forehead, such as partial data measured by the sampling frame .

[0121] Mouth corner change ratio Calculated by the relative displacement of the mouth corners, for example, some data is measured .

[0122] Calculation process:

[0123] Calculate the characteristic rate of change term

[0124] ;

[0125] Calculate some data:

[0126] ;

[0127] ;

[0128] Calculate the average value after the complete frame:

[0129] ;

[0130] Calculate forehead stretch fluctuation Mean:

[0131] ;

[0132] variance:

[0133] ;

[0134] ;

[0135] Calculate the cumulative change of mouth corners

[0136] ;

[0137] Calculate some data:

[0138] ;

[0139] ;

[0140] Calculate the final value:

[0141] ;

[0142] Analysis of calculation results:

[0143] Calculation results Represents the rate of change of micro-expression features. A larger value indicates a more dramatic dynamic change in facial micro-expressions, while a smaller value indicates a more stable feature. This value is directly related to the offset value of the micro-expression feature and can be used to determine the trend of micro-expression changes.

[0144] S320, the matching error calculation submodule calls the micro-expression feature offset value, obtains the identity data of the ticket holder, extracts the facial expression parameters in the identity record, calculates the numerical difference between the current micro-expression feature and the identity data, selects the error interval of the feature, calculates the overall matching deviation mean, and obtains the micro-expression matching error;

[0145] First, obtain the identity data of the injury ticket holder, which includes historical facial expression records and corresponding status parameters, read the facial expression parameters in the identity record, and extract the historical feature mean of the corners of the eyes, forehead and mouth corners. and standard deviation , calculate the numerical difference between the current micro-expression feature and the identity data, and define the error as ,in Represents the characteristic value of the current frame. For example, if the current eye corner contraction degree is 0.8, the historical mean is 0.6, and the standard deviation is 0.1, then , then, filter the error interval of the feature and set the error judgment range ,in is the error threshold, for example, ,like If the error exceeds the threshold, it is considered an abnormal error. Finally, the mean of the overall matching deviation is calculated and the error weights of different features are set. , calculate the weighted mean , and finally the micro-expression matching error is obtained.

[0146] S330: The matching parameter adjustment submodule calls the micro-expression matching error, extracts the matching error correction parameter based on the relaxation, tension, and anxiety states, calculates the matching parameter adjustment range in each state, selects the applicable correction interval, calculates the change trend of the corrected matching parameter, and obtains the identity matching adjustment value;

[0147] Extract matching error correction parameters based on relaxation, tension, and anxiety states, and set the error adjustment factor for each state , calculate the adjustment range of the matching parameters in each state and define the adjustment amount ,in According to individual historical data, such as relaxation state , tension state , anxiety state , filter the applicable correction interval, calculate the changing trend of the corrected matching parameters, and define the corrected matching parameters , and finally obtain the identity matching adjustment value.

[0148] See also Figure 6 and Figure 2 , the gait behavior verification module includes:

[0149] S410: The gait parameter calculation submodule calls the identity matching adjustment value to obtain gait behavior data, extracts the step length, step frequency variation range, and center of gravity offset angle, calculates the time series change rate of the gait parameters, selects the step adjustment interval under the stable state, calculates the gait characteristic mean and fluctuation trend, and obtains the gait characteristic parameter set;

[0150] After obtaining the gait behavior data, the gait detection system is first called to obtain the individual's motion trajectory, extract the step length, step frequency variation range and center of gravity offset angle, and the step length is calculated from the Euclidean distance of the coordinates of the adjacent footstep landing points. ,in Indicates the current step point coordinates. The range of step frequency is calculated by the rate of change of the number of steps over time. ,in is the unit time step, is the measurement time window, for example, if 30 steps are detected within 10 seconds, then Hz, the center of gravity offset angle is calculated by the body center of gravity trajectory angle change ,in is the current center of gravity position, and then the time series change rate of the gait parameters is calculated, and the change rate is defined as in is the current parameter value, For the time interval, filter the pace adjustment interval under the stable state, calculate the mean value and fluctuation trend of gait characteristics, and set the gait stability interval in For empirical parameters, for example, , calculate the changing trend of the overall gait parameter set, and finally obtain the gait feature parameter set.

[0151] S420: The trajectory deviation calculation submodule calls the gait feature parameter set, obtains the verification terminal interaction data, extracts the gait trajectory deviation value, calculates the displacement error between the gait motion trajectory and the standard gait path, filters out abnormal deviation points in the gait change, calculates the overall trajectory deviation mean and the gait continuity change trend, and obtains the gait trajectory deviation value;

[0152] Obtain verification terminal interaction data, extract gait trajectory offset value, calculate the displacement error between gait motion trajectory and standard gait path, and define error ,in is the coordinate of the standard trajectory point, For the current trajectory point coordinates, filter out abnormal offset points in gait changes and calculate the rate of change of offset during gait movement , set the abnormal threshold ,like It is marked as an abnormal deviation point, and the mean of the overall deviation of the trajectory is calculated, and the mean is defined as in is the total number of trajectory points, calculates the gait continuity change trend, and defines the trend index , and finally the gait trajectory deviation value is obtained.

[0153] S430: The matching stability assessment submodule calls the gait trajectory deviation value, extracts the gait parameter fluctuation range, calculates the joint stability index of the cadence, stride length, and trajectory deviation, selects the matching stability parameter range, calculates the overall stability coefficient of the gait behavior, and obtains the gait verification matching value;

[0154] Extract the fluctuation range of gait parameters, calculate the joint stability index of cadence, stride length and trajectory deviation, and define the joint stability index ,in is the weight coefficient of different parameters, for example, , , , filter and match the stable parameter range, set the stable interval , calculate the overall stability coefficient of gait behavior Normalization processing is performed to obtain the matching degree, and finally the gait verification matching value is obtained.

[0155] See also Figure 7 and Figure 2 , the terminal data linkage module includes:

[0156] S510: The authority status calculation submodule calls the ticket operation authority and gait verification matching value to obtain terminal device information, extract the current device authority configuration and synchronization status parameters, calculate the joint weight of the device authority and identity matching status, filter the current device executable authority range, calculate the authority status adjustment parameter, and obtain the terminal authority synchronization parameter;

[0157] First, obtain the terminal device information, read the current device's identity code, and extract the device's current permission configuration parameters, including device access level, executable operation set, data modification permission, and remote control permission. Set permission mapping rules and match the device permission configuration table with the user permission level. Then, extract the synchronization status parameters, obtain the current device and management server connection status and permission synchronization delay value, calculate the joint weight of the device permission and identity matching status, and define the joint weight. for ,in Represents the user permission level, Represents the device permission level, is the weight coefficient, for example, when , , , When , the joint weight is calculated as , then, filter the current device executable permission range and set the device permission range threshold and ,judge Is it within the range? If it is out of the range, adjust the permissions and calculate the permission status adjustment parameters. ,in For example, when ,but , and finally obtain the terminal permission synchronization parameters.

[0158] S520: The device data synchronization submodule calls the terminal authority synchronization parameter, sends a data update instruction to the logged-in device to match the ticket authority with the identity, extracts the device response delay and data update status, calculates the device response stability index, selects the data interval for successful synchronization, calculates the synchronization deviation between the terminal authority and identity, and obtains the terminal synchronization status data;

[0159] Send data update instructions that match ticket permissions and identities to logged-in devices, and extract device response delays and data update status , computing device response stability index pass Calculate, where For the set response threshold, e.g. ,like ,but , filter the data interval for successful synchronization and set the success rate threshold , calculate the synchronization success rate ,in is the number of successful synchronization requests, is the total number of requests, for example , ,but ,like Exceed , then the synchronization is determined to be successful, and the synchronization deviation between the terminal authority and identity matching is calculated ,in The permission weight value stored for the device, such as ,but , and finally obtain the terminal synchronization status data.

[0160] S530: The terminal linkage evaluation submodule calls the terminal synchronization status data, calculates the synchronization consistency index of the multi-device linkage status, filters the permission conflict intervals that appear during the terminal data linkage process, analyzes the dynamic adjustment coefficient of the permission and identity matching between devices, filters the linkage matching range, and obtains the terminal synchronization status;

[0161] The calculation formula for synchronization consistency index is as follows:

[0162] ;

[0163] Calculate synchronization consistency indicators, analyze the dynamic adjustment coefficients of permissions and identities between devices, filter the scope of linkage matching, and obtain terminal synchronization status;

[0164] in, represents the synchronization consistency indicator, Represents the total number of terminals analyzed, Representative The permission value of each terminal, Represents the average authority value of all terminals, represents the standard deviation of the authority values, represents the conflict determination threshold;

[0165] Formula parameter description: The number of terminals, for example, in a network environment, there may be terminals; Obtained from the security system of each terminal in real time, the permission value of each terminal may be recorded during a month of monitoring, such as ; Calculated from The average value of ; Calculated from The standard deviation of ; The settings are based on historical data analysis, assuming , which means that when the standardized deviation is greater than 1.5, it will be identified as a permission violation.

[0166] Calculation Example: Assume , permission value , calculated as follows:

[0167]

[0168] Check each terminal Values ​​such as:

[0169] like No satisfaction, therefore no conflict.

[0170] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An information-based injury ticket assignment system based on facial recognition, characterized by: The system comprises: The physiological status monitoring module acquires facial data, calculates eye trajectory direction rate, blink rate and closure duration, pupil diameter change and response time, analyzes facial muscle activity, calculates the drooping ratio of the mouth corners and the stretching rate of the muscles around the eyes, and ultimately determines the physiological status level. The authority adjustment control module calls the physiological status level, divides the authority level according to the threshold, adjusts the injury ticket modification and information change authority, controls remote scheduling access, and calculates the recovery conditions to obtain the injury ticket operation authority; The identity adaptive matching module obtains micro-expression data based on the injury ticket operation authority, calculates the ratio of eye corner contraction, forehead stretching, and mouth corner change, analyzes the matching error, adjusts the matching parameters according to the emotional state, and obtains the identity matching adjustment value; The gait behavior verification module calls the identity matching adjustment value, obtains gait data to calculate step length, step frequency stability, and center of gravity offset angle, analyzes gait trajectory deviation, and obtains a gait verification matching value; The terminal data linkage module calls the injury ticket authority and gait matching value, calculates the device authority and synchronization status, sends the authority update instruction to the logged-in device, and obtains the terminal synchronization status; The physiological status monitoring module includes: The eye movement submodule obtains eye position parameters from facial region data, calculates the eye displacement direction vector and instantaneous rate, filters the rate fluctuation interval based on the angular velocity changes between consecutive frames, analyzes the rate change mean and deviation range, and determines the movement trend based on the trajectory offset direction to obtain the trajectory offset dynamic change rate; The blink rate submodule uses the trajectory offset dynamic change rate to detect the eyelid opening and closing status of consecutive frames, calculates the number of blinks and the duration of closure per unit time, filters out abnormal closure duration intervals, determines the fluctuation characteristics of blink frequency and closure duration, and obtains the eye closure rate interval; The pupil change submodule calls the eye closure rate interval, extracts pupil diameter change data, calculates the contraction amplitude and dilation time ratio, selects the response time interval based on the diameter change gradient of the continuous time period, calculates the mean of the contraction and dilation response time, and obtains the pupil contraction dynamic ratio; The facial muscle activity submodule calls the pupil contraction dynamic ratio, obtains the drooping angle of the mouth corners and the stretching displacement of the muscles around the eyes, calculates the mouth corner displacement rate and stretching ratio per unit time, filters out abnormal stretching rate intervals, calculates the overall facial muscle activity trend, and obtains the physiological state level.

2. The information-based injury ticket assignment system based on facial recognition according to claim 1 is characterized by: The physiological state level includes eye movement state, blinking pattern, pupil response characteristics, and facial muscle activity; the injury ticket operation authority includes authority level, executable operation range, and recovery conditions; the identity matching adjustment value includes micro-expression matching error, emotion matching parameter, and identity adaptability correction value; the gait verification matching value includes pace characteristics, cadence stability, center of gravity offset range, and trajectory matching coefficient; the terminal synchronization status includes device authority synchronization, identity matching update, and login device status.

3. The information-based injury ticket assignment system based on facial recognition according to claim 1 is characterized by: The eyeball displacement direction vector and instantaneous rate calculation formula are specifically as follows: ； in, Represents the eyeball displacement direction vector and instantaneous velocity, Represents the number of frames in the calculation time window, Representative The instantaneous frame rate, Representative The weight factor of the frame, represents the weighted average rate, Representative The angular acceleration of the frame, represents the mean angular acceleration, represents the variance of angular acceleration, Representative The frame's motion direction angle, Representative The frame's motion direction angle, Represents the total direction change in the entire time window, Represents the total duration of the calculation time window.

4. The information-based injury ticket assignment system based on facial recognition according to claim 1 is characterized by: The authority adjustment control module includes: The permission classification submodule calls the physiological state level, sets the boundary values ​​of high permission, limited permission, and minimum permission according to the permission threshold range corresponding to the physiological state, calculates the permission category corresponding to the current physiological state, and obtains the physiological state permission level; The permission calculation submodule calls the physiological status permission level, obtains the injury ticket permission configuration data, screens the executable operations that meet the current permission level, calculates the currently adjustable injury ticket modification permission, information change permission, and remote scheduling access permission, screens the adjustment range based on the permission configuration table, and obtains the injury ticket adjustment permission set; The permission recovery submodule calls the injury ticket adjustment permission set, calculates the change trend of the physiological status level, filters the physiological status change interval corresponding to the recovery condition, determines the recoverable permission category based on the permission recovery threshold, and obtains the injury ticket operation permission.

5. The information-based injury ticket assignment system based on facial recognition according to claim 1 is characterized by: The identity adaptability matching module includes: The micro-expression feature submodule calls the ticket operation permission to obtain facial micro-expression data, extracts the degree of eye corner contraction, forehead stretching, and mouth corner change ratio, calculates the feature change rate per unit time, screens the feature mean value range in the stable state, determines the feature offset trend, and obtains the micro-expression feature offset value; The matching error calculation submodule calls the micro-expression feature offset value, obtains the identity data of the ticket holder, extracts the facial expression parameters in the identity record, calculates the numerical difference between the current micro-expression feature and the identity data, selects the error interval of the feature, calculates the overall matching deviation mean, and obtains the micro-expression matching error; The matching parameter adjustment submodule calls the micro-expression matching error, extracts the matching error correction parameter according to the relaxation, tension, and anxiety states, calculates the matching parameter adjustment amplitude in each state, selects the applicable correction interval, calculates the change trend of the corrected matching parameter, and obtains the identity matching adjustment value.

6. The information-based injury ticket assignment system based on facial recognition according to claim 5 is characterized by: The calculation formula of the characteristic change rate per unit time is specifically: ； in, Represents the rate of change of characteristics per unit time, Represents the number of frames in the calculation time window, Representative The degree of eye contraction of the frame, Representative The timestamp of the frame, Representative The forehead stretching range of the frame, Represents the mean forehead stretch value within the calculation time window, represents the forehead stretch variance, Representative The mouth corner change ratio of the frame, Represents the mouth corner change ratio of the previous frame, Represents the total change in the mouth corner change ratio within the calculation time window.

7. The information-based injury ticket assignment system based on facial recognition according to claim 1 is characterized by: The gait behavior verification module includes: The gait parameter calculation submodule calls the identity matching adjustment value, obtains gait behavior data, extracts stride length, stride frequency variation range, and center of gravity offset angle, calculates the time series change rate of gait parameters, selects the stride adjustment interval under stable state, calculates the mean value and fluctuation trend of gait characteristics, and obtains a gait characteristic parameter set; The trajectory deviation calculation submodule calls the gait feature parameter set, obtains the verification terminal interaction data, extracts the gait trajectory deviation value, calculates the displacement error between the gait motion trajectory and the standard gait path, filters out abnormal deviation points in the gait change, calculates the overall trajectory deviation mean and the gait continuity change trend, and obtains the gait trajectory deviation value; The matching stability evaluation submodule calls the gait trajectory deviation value, extracts the gait parameter fluctuation range, calculates the joint stability index of step frequency, stride length and trajectory deviation, screens the matching stability parameter range, calculates the overall stability coefficient of gait behavior, and obtains the gait verification matching value.

8. The information-based injury ticket assignment system based on facial recognition according to claim 1 is characterized by: The terminal data linkage module includes: The authority status calculation submodule calls the ticket operation authority and gait verification matching value, obtains the terminal device information, extracts the current device authority configuration and synchronization status parameters, calculates the joint weight of the device authority and identity matching status, filters the current device executable authority range, calculates the authority status adjustment parameter, and obtains the terminal authority synchronization parameter; The device data synchronization submodule calls the terminal authority synchronization parameters, sends a data update instruction to the logged-in device that matches the ticket authority and identity, extracts the device response delay and data update status, calculates the device response stability index, screens the data interval for successful synchronization, calculates the synchronization deviation of the terminal authority and identity matching, and obtains the terminal synchronization status data; The terminal linkage evaluation submodule calls the terminal synchronization status data, calculates the synchronization consistency index of the multi-device linkage status, filters the permission conflict interval that occurs during the terminal data linkage process, analyzes the dynamic adjustment coefficient of the permission and identity matching between devices, filters the scope of linkage matching, and obtains the terminal synchronization status.

9. The information-based injury ticket assignment system based on facial recognition according to claim 8 is characterized by: The synchronization consistency index calculation formula is specifically as follows: ； Calculate the synchronization consistency index, analyze the dynamic adjustment coefficient of the authority and identity matching between devices, filter the scope of linkage matching, and obtain the terminal synchronization status; in, represents the synchronization consistency indicator, Represents the total number of terminals analyzed, Representative The permission value of each terminal, Represents the average authority value of all terminals, represents the standard deviation of the authority values, Represents the conflict determination threshold.

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