Alarm system and method based on face recognition

Abstract

The present application discloses an alarm system and method based on face recognition, and relates to the field of alarm technology of face recognition. The present application includes an information management module, a face recognition module, a tailing analysis module and an early warning terminal. By collecting and transmitting faces and personal information of campus personnel to various databases for storing facial feature values ​​and personal information, a face recognition machine identifies a target person, determines whether the target person has access authority, analyzes whether the target person is a blacklisted person, and determines whether a suspicious person has access authority. This solves the limitation problem in the current campus face recognition development feasibility analysis process. When the target person has authority, it checks whether there is a tailer in the video, and sends the tailer's feature value to the face recognition, thus realizing comprehensive and objective analysis of face recognition and providing an early warning function to ensure the safety of various places in the campus.

Description

Technical Field

[0001] The present application relates to the technical field of face recognition alarm, and in particular to an alarm system and method based on face recognition. Background Art

[0002] Nowadays, facial recognition technology is developing rapidly and being widely used. By combining advanced technologies such as visual analysis technology, artificial intelligence and big data, it has achieved efficient collection, accurate recognition and real-time alarm of facial images, bringing revolutionary changes to campus security monitoring and crime prevention.

[0003] Prior art, such as the invention patent application with announcement number: CN109727352A, discloses a campus early warning method based on face recognition, including a personnel management module for access control authority management and personnel entry and exit monitoring, an early warning module for pushing early warning information to parents and faculty members, a visitor reservation module for temporary visitors to apply for entry and exit authorization, a perimeter security module for monitoring and recording perimeter alarm equipment and sending alarm signals, and a video intercom module for security and faculty communication and information query. The present invention combines an intelligent camera with face recognition and license plate recognition with perimeter security equipment and an attendance system to form a complete set of digital campus security systems, so that the campus security system can further realize comprehensive intelligent and automated supervision after integration, reduce the work pressure of security personnel, and improve the safety level of the campus.

[0004] With respect to the above scheme, the inventors of the present application have found that the above technology has at least the following technical problems: 1. Currently, facial data of students and teachers on campus are collected and identified, but facial data of other staff members, such as the security department, logistics department, administrative department and other work departments, are not collected. As a result, the actual facial data of various departments on campus cannot be managed more accurately, and the comprehensiveness and authenticity of the facial recognition process cannot be guaranteed, and thus the reference and accuracy of the analysis results cannot be guaranteed.

[0005] 2. There is currently a lack of a blacklist face database, which makes it impossible to truly display the situation of face recognition, and thus cannot guarantee the security of various places on campus. The lack of multiple databases cannot reduce the false alarms and missed alarms caused by database mismatches, and the physical isolation of data cannot be achieved. This reduces the security of the system to a certain extent, making it inconvenient to manage and maintain, and to adapt to different application scenarios. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of this application is to provide an alarm system and method based on face recognition.

[0007] To solve the above technical problems, the present application adopts the following technical solutions: In the first aspect, the present application provides an alarm system based on face recognition, comprising the following modules: an information management module, used to collect face information and personal information of personnel in various departments on campus, and then transmit the face information of personnel in various departments on campus to the information management center, so as to obtain the facial feature values ​​corresponding to the face information of each department, and classify and store the facial feature values ​​and personal information of each department on campus into the corresponding face database.

[0008] The face recognition module is used to obtain the position of the face recognition machine and the facial feature values ​​of the target person when the target person appears in front of a face recognition machine for face recognition, and then analyze whether the target person has access authority, thereby analyzing whether the suspicious person is a blacklisted person and analyzing whether the suspicious person has access authority.

[0009] The tailing analysis module is used to obtain the target person's entry video when the target person has entry permission, analyze whether there is a tailing person in the entry video, obtain the facial feature value of each tailing person in the entry video, and transmit the facial feature value of each tailing person to the facial analysis module for analysis.

[0010] The early warning terminal is used to issue an early warning when the target person or suspicious person does not have access authority; it is also used to issue an early warning when the suspicious person is on the blacklist.

[0011] Preferably, the analysis of whether the target person has entry permission is specifically performed as follows: a corresponding face database is obtained according to the position of the face recognition machine, and then the face feature value of the target person is compared with each face feature value in the face database corresponding to the position of the face recognition machine; when the face feature value of the target person matches a certain face feature value in the face database corresponding to the position of the face recognition machine, it is judged that the target person has entry permission; when the face feature value of the target person does not match each face feature value in the face database corresponding to the position of the face recognition machine, it is judged that the target person is a suspicious person, and an analysis is performed on whether the suspicious person has entry permission.

[0012] Preferably, the analysis of whether the suspicious person is a blacklisted person is carried out as follows: the facial feature value of the suspicious person is compared with the facial feature values ​​of each person in the blacklist face database. When the facial feature value of the suspicious person matches the facial feature value of a person in the blacklist face database, the background pager sends a message to the security personnel corresponding to the location of the facial recognition machine to check the suspicious person.

[0013] Preferably, the analysis of whether the suspicious person has the right to enter is carried out, and the specific analysis process is as follows: the facial feature value of the suspicious person is then compared with the facial feature values ​​of each person in the face master database. When the facial feature value of the suspicious person does not match the facial feature values ​​of each person in the face master database, it is determined that the suspicious person does not have the right to enter, and the security personnel corresponding to the location of the face recognition machine are paged in the background to check the suspicious person; when the facial feature value of the suspicious person matches the facial feature value of a person in the face master database, the personal information corresponding to the facial feature value is obtained;

[0014] Based on the personal information of the suspicious person, the task database is searched to see whether the suspicious person has a task in the time period and the location of the facial recognition machine. When the suspicious person has a task in the time period and the location of the facial recognition machine, the background will page the security personnel corresponding to the location of the facial recognition machine to conduct a task check. If the security personnel's task check is passed, it is determined that the suspicious person has entry permission and the suspicious person is restored to the target person; otherwise, it is determined that the suspicious person does not have entry permission, and the security personnel corresponding to the location of the facial recognition machine will be paged in the background to check the suspicious person.

[0015] Preferably, the analysis of whether there is a trailing person in the entry video is specifically performed as follows: using visual analysis technology to analyze the entry video of the target person, when no moving object enters at the same time as the target person enters, it is determined that there is no trailing person in the entry video; when there are moving objects entering at the same time as the target person enters, using visual analysis technology to analyze whether each moving object is a human, when a certain moving object is a human, the moving object is recorded as a trailing person, thereby obtaining each trailing person, and obtaining a facial feature value of each trailing person, transmitting the facial feature value of each trailing person to a facial analysis module for analysis, and determining whether each trailing person has entry authority.

[0016] In a second aspect, the present application provides an alarm method for an alarm system based on face recognition, comprising the following steps: Step 1, information management: collecting facial information and personal information of personnel from various departments on campus, and then transmitting the facial information of personnel from various departments on campus to an information management center, thereby obtaining facial feature values ​​corresponding to the facial information of each department, and classifying and storing the facial feature values ​​and personal information of each department on campus into corresponding face databases.

[0017] Step 2: Face recognition: When a target person appears in front of a face recognition machine for face recognition, the position of the face recognition machine and the face feature value of the target person are obtained, and then the target person is analyzed to see whether he has access permission, and then the suspicious person is analyzed to see whether he is a blacklisted person, and whether he has access permission.

[0018] Step 3: Tailing analysis: When the target person has entry permission, obtain the target person's entry video, analyze whether there is a tailing person in the entry video, obtain the facial feature value of each tailing person in the entry video, and transmit the facial feature value of each tailing person to the face analysis module for analysis;

[0019] Step 4: Early warning: When the target person or suspicious person does not have access permission, an early warning will be issued; when the suspicious person is on the blacklist, an early warning will be issued.

[0020] The beneficial effects of the present application are: 1. The face recognition-based alarm system and method provided by the present application, by collecting and transmitting the faces and personal information of campus personnel to various databases for storing facial feature values ​​and personal information, the face recognition machine identifies the target person, determines whether the target person has access authority, and analyzes whether the target person is a blacklisted person, and determines whether the suspicious person has access authority, thereby solving the limitation problem in the current campus face recognition development feasibility analysis process, and checks whether there is a follower in the video when the target person has authority, and sends the follower's feature value to the face recognition, thereby realizing the comprehensiveness and objectivity of face recognition analysis, and providing an early warning function at the same time, ensuring the safety of various places in the campus.

[0021] 2. In this application, the facial recognition machines at different locations correspond to different databases, which improves the accuracy and efficiency of recognition to a certain extent, greatly reduces the false alarms and missed alarms caused by database mismatch, realizes the physical isolation of data, helps prevent data leakage and abuse, and enhances the security of the system. Even if the database at a certain location is hacked, the databases at other locations will not be affected, which is convenient for management and maintenance and adapts to different application scenarios.

[0022] 3. This application provides different early warning prompts. Different early warning levels mean different degrees of risk. School security personnel can take different levels of handling measures according to the different early warning levels, which helps to reasonably allocate security resources and ensure a quick and effective response in emergency situations, thereby improving the efficiency of security prevention. The school can formulate a more scientific and reasonable security management strategy based on the analysis results of the early warning data, thereby improving the overall security management level of the campus. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0024] Figure 1 This is a schematic diagram of the system structure connection for this application.

[0025] Figure 2 The figure is a flowchart of the implementation steps of the present application method. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] See also Figure 1 As shown, the present application provides, in a first aspect, an alarm system based on face recognition, including an information management module, a face recognition module, a tailing analysis module and an early warning terminal.

[0028] The information management module is used to collect facial information and personal information of personnel in various departments on campus, and then transmit the facial information of personnel in various departments on campus to the information management center, so as to obtain the facial feature values ​​corresponding to the facial information of each department, and classify the facial feature values ​​and personal information of each department on campus into the corresponding face database.

[0029] In a specific example, the facial information includes facial contour, facial features, skin texture and facial landmarks.

[0030] It should be noted that facial marks such as moles and birthmarks.

[0031] In a specific example, the facial information and personal information of each department on campus are collected, and the specific management process is as follows: the departments on campus include the student department, the teacher department, the security department, the logistics department, the administrative department and other work departments; and then the facial information and personal information of each student in the student department, each teacher in the teacher department, each security personnel in the security department, each logistics personnel in the logistics department, each administrative personnel in the administrative department and each staff member in other work departments are collected.

[0032] It should be noted that the personal information required by the Student Department, Teacher Department, Security Department, Logistics Department, Administration Department and other work departments are not the same. For example, the personal information of each student in the Student Department includes name, gender, dormitory location, telephone number, emergency contact, college major, grade class and home address, etc.; for example, the personal information of each teacher in the Teacher Department includes name, gender, dormitory location or home address, telephone number, emergency contact, college major class and office location, etc.; for example, the personal information of each security personnel in the Security Department includes name, gender, dormitory location, telephone number, emergency contact, home address, office location and security responsible area, etc.; for example, the personal information of each logistics personnel in the Logistics Department includes name, gender, dormitory location, telephone number, emergency contact, home address, office location and logistics responsible area, etc., among which the logistics responsible areas include library hygiene, hygiene of a dormitory building and hygiene of a teaching building.

[0033] It should be noted that facial contour refers to the overall shape and structure of the face, including the shapes of the forehead, cheekbones, jaw and other parts; facial features refer to the shape of the eyebrows, eye contour, shape and size of the nose, thickness and shape of the lips, etc.; skin texture refers to the texture and texture of the facial skin, such as pores, wrinkles, etc.; facial marks refer to unique facial marks such as moles, scars, birthmarks, etc.

[0034] In a specific example, the facial feature values ​​corresponding to the facial information of each department are obtained, and the facial feature values ​​and personal information of each department on campus are classified and stored in a corresponding database. The specific acquisition process is as follows: the facial information of the personnel of each department on campus is transmitted to the information management center, and the facial feature values ​​corresponding to the facial information of each department are transmitted. The facial information and the corresponding facial feature values ​​of the personnel of each department on campus are stored using a non-relational database. At the same time, a corresponding face database is established according to each department, and then the personal information, facial information and corresponding facial feature values ​​of the personnel of each department are stored in the corresponding database respectively, and a face master database is provided for storing the personal information, facial information and corresponding facial feature values ​​of all department personnel; a blacklist face database is also provided for storing the facial feature values ​​of each person on the blacklist; at the same time, each face database, the face master database and the blacklist face database are connected to the corresponding face recognition machine.

[0035] It should be noted that the face detection model in OpenCV is used to transmit the facial feature values ​​corresponding to the face information of each department. The face detection model in OpenCV is a public technology.

[0036] It should be noted that non-relational databases do not rely on table structures to store data, but instead use non-traditional relational data table structures such as key-value pairs, column families, documents, or graphs for storage; this storage method is more flexible and can handle various types of data, including structured, semi-structured, and unstructured data.

[0037] This application uses a non-relational database. There is an essential difference between the storage methods of non-relational databases and relational databases. The relational database relies on a table structure to store data and has higher reliability and stability; while the non-relational database adopts a more flexible unstructured storage method, which can handle various types of data and has high availability and scalability, and can better manage the facial information and personal information of personnel in various departments within the school.

[0038] The face recognition module is used to obtain the position of the face recognition machine and the facial feature values ​​of the target person when the target person appears in front of a face recognition machine for face recognition, and then analyze whether the target person has access authority, thereby analyzing whether the suspicious person is a blacklisted person and analyzing whether the suspicious person has access authority.

[0039] In a specific example, the analysis of whether the target person has entry permission is performed as follows: the corresponding face database is obtained according to the position of the face recognition machine, and then the face feature value of the target person is compared with each face feature value in the face database corresponding to the position of the face recognition machine. When the face feature value of the target person matches a certain face feature value in the face database corresponding to the position of the face recognition machine, it is judged that the target person has entry permission; when the face feature value of the target person does not match each face feature value in the face database corresponding to the position of the face recognition machine, it is judged that the target person is a suspicious person, and an analysis is performed on whether the suspicious person has entry permission.

[0040] It should be noted that the facial recognition machine obtains the facial database corresponding to the location. For example, the school gate corresponds to the main facial database.

[0041] In a specific example, the analysis of whether a suspicious person is a blacklisted person is as follows: the facial feature value of the suspicious person is compared with the facial feature values ​​of each person in the blacklist face database. When the facial feature value of the suspicious person matches the facial feature value of a person in the blacklist face database, the background pager sends a message to the security personnel corresponding to the location of the facial recognition machine to check the suspicious person.

[0042] In a specific example, the analysis of whether the suspicious person has entry permission is performed, and the specific analysis process is as follows: the facial feature value of the suspicious person is then compared with the facial feature values ​​of each person in the face master database. When the facial feature value of the suspicious person does not match the facial feature values ​​in the face master database, it is determined that the suspicious person does not have entry permission, and the background pager is used to send a security personnel corresponding to the location of the facial recognition machine to check the suspicious person; when the facial feature value of the suspicious person matches the facial feature value of a person in the face master database, the personal information corresponding to the facial feature value is obtained.

[0043] Based on the personal information of the suspicious person, the task database is searched to see whether the suspicious person has a task in the time period and the location of the facial recognition machine. When the suspicious person has a task in the time period and the location of the facial recognition machine, the background will page the security personnel corresponding to the location of the facial recognition machine to conduct a task check. If the security personnel's task check is passed, it is determined that the suspicious person has entry permission and the suspicious person is restored to the target person; otherwise, it is determined that the suspicious person does not have entry permission, and the security personnel corresponding to the location of the facial recognition machine will be paged in the background to check the suspicious person.

[0044] It should be noted that the task database stores tasks such as dormitory inspections by counselors and maintenance by maintenance personnel, and also stores the time periods and locations of the tasks.

[0045] In this application, the facial recognition machines at different locations correspond to different databases, which improves the accuracy and efficiency of recognition to a certain extent, greatly reduces the false alarms and missed alarms caused by database mismatch, realizes the physical isolation of data, helps prevent data leakage and abuse, and enhances the security of the system. Even if the database at a certain location is hacked, the databases at other locations will not be affected, which is convenient for management and maintenance and adapts to different application scenarios.

[0046] The tailing analysis module is used to obtain the target person's entry video when the target person has entry permission, analyze whether there is a tailing person in the entry video, obtain the facial feature value of each tailing person in the entry video, and transmit the facial feature value of each tailing person to the facial analysis module for analysis.

[0047] In a specific example, the analysis of whether there is a trailing person in the entry video is performed as follows: the entry video of the target person is analyzed using visual analysis technology. When no moving object enters at the same time as the target person enters, it is determined that there is no trailing person in the entry video; when a moving object enters at the same time as the target person enters, visual analysis technology is used to analyze whether each moving object is a human. When a moving object is a human, the moving object is recorded as a trailing person, thereby obtaining each trailing person and obtaining a facial feature value of each trailing person, transmitting the facial feature value of each trailing person to a facial analysis module for analysis, and determining whether each trailing person has entry authority.

[0048] The early warning terminal is used to issue an early warning when the target person or suspicious person does not have access authority; it is also used to issue an early warning when the suspicious person is on the blacklist.

[0049] In a specific example, when the target person or suspicious person does not have access permission, an early warning is issued; when the suspicious person is a blacklisted person, an early warning is issued. The specific early warning process is as follows: when the target person does not have access permission, a third-level early warning is issued; when the suspicious person is a blacklisted person, a first-level early warning is issued; when the suspicious person does not have access permission, a second-level early warning is issued.

[0050] It should be noted that the first-level warning is the highest level of warning, and the third-level warning is the lowest level of warning.

[0051] Different early warning prompts are provided in this application. Different early warning levels mean different degrees of risk. School security personnel can take different levels of handling measures according to the different early warning levels, which helps to reasonably allocate security resources and ensure a quick and effective response in emergency situations, thereby improving the efficiency of security prevention. The school can formulate a more scientific and reasonable security management strategy based on the analysis results of the early warning data, thereby improving the overall security management level of the campus.

[0052] See also Figure 2 As shown, the present application provides an alarm method for an alarm system based on face recognition in a second aspect, comprising the following steps: Step 1, information management: collecting facial information and personal information of personnel from various departments on campus, and then transmitting the facial information of personnel from various departments on campus to an information management center, thereby obtaining facial feature values ​​corresponding to the facial information of each department, and classifying and storing the facial feature values ​​and personal information of each department on campus into corresponding face databases.

[0053] Step 2: Face recognition: When a target person appears in front of a face recognition machine for face recognition, the position of the face recognition machine and the face feature value of the target person are obtained, and then the target person is analyzed to see whether he has access permission, and then the suspicious person is analyzed to see whether he is a blacklisted person, and whether he has access permission.

[0054] Step 3: Tailing analysis: When the target person has entry permission, obtain the target person's entry video, analyze whether there is a tailing person in the entry video, obtain the facial feature value of each tailing person in the entry video, and transmit the facial feature value of each tailing person to the face analysis module for analysis.

[0055] Step 4: Early warning: When the target person or suspicious person does not have access permission, an early warning will be issued; when the suspicious person is on the blacklist, an early warning will be issued.

[0056] The face recognition-based alarm system and method provided by the present application collect and transmit the faces and personal information of campus personnel to various databases for storing facial feature values ​​and personal information. The face recognition machine identifies the target person, determines whether the target person has access authority, analyzes whether the target person is a blacklisted person, and determines whether the suspicious person has access authority. This solves the limitations of the current campus face recognition development feasibility analysis process. When the target person has authority, it checks whether there is a follower in the video and sends the follower's feature value to the face recognition, achieving comprehensive and objective analysis of face recognition. At the same time, it provides an early warning function to ensure the safety of various places on campus.

[0057] The above contents are merely examples and explanations of the concept of the present application. The technical personnel in the relevant technical field may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present application.

Claims

1. The alarm system based on face recognition is characterized by: include: The information management module is used to collect the facial information and personal information of personnel in various departments on campus, and then transmit the facial information of personnel in various departments on campus to the information management center, so as to obtain the facial feature values ​​corresponding to the facial information of each department, and classify and store the facial feature values ​​and personal information of each department on campus into the corresponding face database; The facial feature values ​​corresponding to the facial information of each department are obtained, and the facial feature values ​​and personal information of each department on campus are classified and stored in the corresponding database. The specific acquisition process is as follows: The facial information of personnel in various departments on campus is transmitted to the information management center, and the facial feature values ​​corresponding to the facial information of each department are transmitted. The facial information and corresponding facial feature values ​​of personnel in various departments on campus are stored using a non-relational database. At the same time, a corresponding facial database is established according to each department, and then the personal information, facial information and corresponding facial feature values ​​of personnel in each department are stored in the corresponding database respectively, and a facial master database is provided for storing the personal information, facial information and corresponding facial feature values ​​of personnel in all departments; a blacklist facial database is also provided for storing the facial feature values ​​of each person in the blacklist; at the same time, each facial database, the facial master database and the blacklist facial database are connected to the corresponding facial recognition machine; The face recognition module is used to obtain the position of the face recognition machine and the face feature value of the target person when the target person appears in front of a face recognition machine for face recognition, and then analyze whether the target person has access authority, thereby analyzing whether the suspicious person is a blacklisted person, and analyzing whether the suspicious person has access authority; The specific analysis process of analyzing whether the target person has access authority is as follows: The corresponding face database is obtained according to the position of the face recognition machine, and then the face feature value of the target person is compared with each face feature value in the face database corresponding to the position of the face recognition machine. When the face feature value of the target person matches a face feature value in the face database corresponding to the position of the face recognition machine, it is judged that the target person has access authority; when the face feature value of the target person does not match each face feature value in the face database corresponding to the position of the face recognition machine, it is judged that the target person is a suspicious person, and whether the suspicious person has access authority is analyzed; The tailing analysis module is used to obtain the target person's entry video when the target person has entry permission, and when analyzing whether there is a tailing person in the entry video, obtain the facial feature value of each tailing person in the entry video, and transmit the facial feature value of each tailing person to the face analysis module for analysis; Early warning terminal, used to issue early warning prompts when the target person or suspicious person does not have access authority; When the suspicious person is on the blacklist, an early warning will be issued.

2. The face recognition based alarm system according to claim 1, characterized in that: The facial information includes facial contour, facial features, skin texture and facial landmarks.

3. The face recognition based alarm system according to claim 2, characterized in that: The specific management process of collecting facial information and personal information of various departments on campus is as follows: The various departments on campus include the Student Department, Teacher Department, Security Department, Logistics Department, Administration Department and other work departments; and then collect the facial information and personal information of each student in the Student Department, each teacher in the Teacher Department, each security personnel in the Security Department, each logistics personnel in the Logistics Department, each administrative personnel in the Administration Department and each staff member in other work departments.

4. The face recognition based alarm system according to claim 1, characterized in that: The specific analysis process of analyzing whether a suspicious person is a blacklisted person is as follows: The facial feature values ​​of the suspicious person are compared with the facial feature values ​​of each person in the blacklist face database. When the facial feature values ​​of the suspicious person match the facial feature values ​​of someone in the blacklist face database, the background will page the security personnel corresponding to the location of the facial recognition machine to check the suspicious person.

5. The face recognition based alarm system according to claim 4, characterized in that: The specific analysis process of analyzing whether a suspicious person has access authority is as follows: Then, the facial feature value of the suspicious person is compared with the facial feature values ​​of each person in the face master database. When the facial feature value of the suspicious person does not match the facial feature values ​​in the face master database, it is determined that the suspicious person does not have access authority, and the background pager is sent to the security personnel corresponding to the location of the face recognition machine to check the suspicious person; when the facial feature value of the suspicious person matches the facial feature value of a person in the face master database, the personal information corresponding to the facial feature value is obtained; Based on the personal information of the suspicious person, the task database is searched to see whether the suspicious person has a task in the time period and the location of the facial recognition machine. When the suspicious person has a task in the time period and the location of the facial recognition machine, the background will page the security personnel corresponding to the location of the facial recognition machine to conduct a task check. If the security personnel's task check is passed, it is determined that the suspicious person has entry permission and the suspicious person is restored to the target person; otherwise, it is determined that the suspicious person does not have entry permission, and the security personnel corresponding to the location of the facial recognition machine will be paged in the background to check the suspicious person.

6. The face recognition-based alarm system according to claim 5, characterized in that: The specific analysis process of analyzing whether there is a person following in the video is as follows: Use visual analysis technology to analyze the target person's entry video. When no moving objects enter at the same time as the target person enters, it is determined that there is no following person in the entry video. When a moving object enters at the same time as the target person enters, use visual analysis technology to analyze whether each moving object is a human. When a moving object is a human, the moving object is recorded as a following person, thereby obtaining each following person and the facial feature value of each following person. The facial feature value of each following person is transmitted to the facial analysis module for analysis to determine whether each following person has entry permission.

7. The face recognition based alarm system according to claim 6, characterized in that: When the target person or suspicious person does not have access authority, an early warning is issued; when the suspicious person is a blacklisted person, an early warning is issued. The specific early warning process is as follows: When the target person does not have access permission, a third-level warning will be issued; when the suspicious person is on the blacklist, a first-level warning will be issued; when the suspicious person does not have access permission, a second-level warning will be issued.

8. A face recognition-based alarm method implemented by the face recognition-based alarm system according to any one of claims 1 to 7, characterized in that: include: Step 1: Information management: collect facial information and personal information of personnel from various departments on campus, and then transmit the facial information of personnel from various departments on campus to the information management center, so as to obtain the facial feature values ​​corresponding to the facial information of each department, and classify and store the facial feature values ​​and personal information of each department on campus into the corresponding face database; Step 2: Face recognition: When a target person appears in front of a face recognition machine for face recognition, the face recognition machine position and the face feature value of the target person are obtained, and then the target person is analyzed to see whether he has access authority, and then the suspicious person is analyzed to see whether he is a blacklisted person, and whether he has access authority; Step 3: Tailing analysis: When the target person has entry permission, obtain the target person's entry video, analyze whether there is a tailing person in the entry video, obtain the facial feature value of each tailing person in the entry video, and transmit the facial feature value of each tailing person to the face analysis module for analysis; Step 4: Early warning: When the target person or suspicious person does not have access permission, an early warning will be issued; When the suspicious person is on the blacklist, an early warning will be issued.

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