Smart access control intelligent management system based on face recognition

Abstract

The present invention relates to the field of computer vision technology, and more specifically, to a smart access control intelligent management system based on facial recognition. The system comprises: a user management unit that manages and maintains the personal information and access rights of authorized users based on an information management module; a facial recognition unit that, based on the user information of the user management unit, captures facial images of people entering and leaving the system through a portrait acquisition module, introduces aerosol influence variables for data processing, and performs identity authentication through a convolutional neural network algorithm that introduces illumination variables; an access control unit that controls access control through a control module based on the identity authentication results of the facial recognition unit; and a communication integration unit that establishes a network connection through a wireless network module based on an IP network protocol to ensure stable data transmission between the various components within the access control system. The present invention design, through strict permission settings, ensures that only authorized personnel can enter specific areas, thereby enhancing the security of the system.

Description

Technical Field

[0001] The present invention relates to the field of computer vision technology, and in particular to a smart access control intelligent management system based on face recognition. Background Art

[0002] A smart access control management system based on facial recognition is a highly integrated security solution. It leverages advanced computer vision and artificial intelligence technologies, particularly deep learning algorithms, to capture facial images of people entering or leaving a specific area through cameras. These images are then compared in real time against a pre-registered user database to automatically identify individuals and control access rights. This system goes beyond simple door opening and closing operations and offers a range of value-added services, such as visitor management, traffic statistics, abnormal behavior monitoring, and emergency response. Leveraging the Internet of Things (IoT), smart access control can seamlessly connect with other security devices, such as surveillance cameras, alarm systems, and smart home devices, forming a comprehensive security network. Furthermore, leveraging the powerful processing capabilities and big data analytics tools of cloud computing platforms, the system enables remote management and intelligent decision-making, ensuring the safety and efficiency of community or business environments. Furthermore, to protect user privacy and data security, the system typically employs high-level encryption and strict access control policies to prevent unauthorized access and personal information leakage. Overall, the smart access control and intelligent management system based on facial recognition represents an important innovation in the field of modern security. It greatly improves the convenience and security of access control, and provides a more intelligent and humane service experience for people's lives and work.

[0003] In existing smart access control and management systems based on facial recognition, cameras may not be able to clearly capture facial images in strong sunlight or dimly lit environments, resulting in recognition failure. Furthermore, items such as hats, glasses, and masks can obscure facial features, affecting recognition accuracy. Despite continuous technological advancements, under certain conditions (such as highly similar twins, significant changes in facial expression, and changes in appearance due to aging), the system may still experience misidentification (mistaking different individuals for the same person) or rejection (failure to correctly identify legitimate users), which can degrade the user experience and raise security concerns. Facial recognition involves the collection, storage, and use of personal biometric data. The leakage of this sensitive information can pose a serious threat to user privacy. Therefore, a smart access control and management system based on facial recognition is designed. Summary of the Invention

[0004] The purpose of the present invention is to provide a smart access control intelligent management system based on facial recognition to solve the problem raised in the above background technology that in the existing smart access control intelligent management system based on facial recognition, the camera may not be able to clearly capture the facial image under direct strong light or dim environment, resulting in recognition failure; and wearing items such as hats, glasses, masks, etc. may block facial features, affecting recognition accuracy. Although technology is constantly advancing, under certain conditions (such as twins with high similarity, large changes in facial expressions, changes in appearance caused by aging, etc.), the system may still have misidentification (mistaking different people for the same person) or rejection (failure to correctly identify legitimate users), which will reduce the user experience and cause security issues. Facial recognition involves the collection, storage and use of personal biometric data. Once this sensitive information is leaked, it may pose a serious threat to the user's privacy.

[0005] To achieve the above objectives, the present invention aims to provide a smart access control intelligent management system based on face recognition, including a user management unit, which manages and maintains the personal information and access rights of authorized users based on an information management module;

[0006] A face recognition unit, which captures facial images of people entering and leaving the user's home using a portrait acquisition module based on the user information from the user management unit, introduces aerosol influence variables for data processing, and performs identity verification using a convolutional neural network algorithm that introduces illumination variables;

[0007] An access control unit, which controls access through a control module based on the result of identity authentication by the face recognition unit;

[0008] The communication integration unit is based on the IP network protocol and establishes a network connection through a wireless network module to ensure stable data transmission between various components in the access control system.

[0009] As a further improvement of this technical solution, the user management unit includes an information management module;

[0010] The information management module is used to manage user information data and admit new user information data, and set different access rights for each user according to the user's role.

[0011] As a further improvement to this technical solution, facial images of people entering and leaving are captured by the portrait acquisition module, aerosol influence variables are introduced for data processing, and identity verification is performed using a convolutional neural network algorithm that introduces illumination variables. The specific steps involved are as follows:

[0012] S3.1. When a person approaches the access control system, the portrait acquisition module captures the facial image of the person entering or leaving the access control system.

[0013] Among them, facial images include facial contours, facial features, and skin color;

[0014] S3.2. Introducing the aerosol influence variable to process the facial image captured by the portrait acquisition module to obtain a facial feature vector;

[0015] S3.3. Analyze facial feature vectors using a convolutional neural network algorithm that introduces illumination variables.

[0016] S3.4. Compare the analysis results with the facial data of the authorized user stored in the user management unit to determine whether there is a match;

[0017] The authorized user facial data includes the recorded facial image and the recorded user information data;

[0018] S3.5. Regardless of the verification result, the face recognition unit will record the verification event for subsequent query and audit.

[0019] As a further improvement of the present technical solution, in S3.1, the portrait acquisition module includes an infrared sensor module and a camera module;

[0020] The infrared sensor module is used to determine whether there is a person approaching the access control;

[0021] The camera module is used to capture facial images when the infrared sensor module is triggered.

[0022] As a further improvement of the present technical solution, in S3.2, the specific steps of introducing the aerosol influence variable to process the facial image captured by the portrait acquisition module to obtain the facial feature vector are as follows:

[0023] Input facial image:

[0024] ;

[0025] in, represents the input facial image; Indicates the height of the facial image; Indicates the width of the facial image; Indicates the number of channels; represents the set of real numbers;

[0026] Normalize the facial image:

[0027] ;

[0028] in, represents the normalized facial image; represents the mean of facial images; represents the standard deviation of facial images;

[0029] The expression for correcting the posture by the least squares method is:

[0030] ;

[0031] in, Represents posture parameters; represents the function that transforms the key points from the world coordinate system to the image coordinate system; Indicates the position of the reference key point; Indicates the number of key points; Represents the key point index variable; Represents the coordinates of the key point in the world coordinate system;

[0032] According to the estimated posture parameters , apply the affine transformation matrix Correct the face image to a frontal perspective;

[0033] The transformed image is:

[0034] ;

[0035] in, represents the transformed facial image; represents the affine transformation matrix;

[0036] Considering that the aerosol influence variable has an impact on the accuracy of the facial feature vector, the aerosol influence variable is introduced to optimize the facial feature vector;

[0037] Among them, aerosol-affecting variables include aerosol concentration;

[0038] Using the Aerosol Concentration Estimation Network The corrected image Evaluate the degree of its influence by aerosol and obtain the aerosol concentration value;

[0039] ;

[0040] in, Indicates the concentration of aerosol;

[0041] According to the concentration of aerosol Dynamically adjust feature maps , generate the compensated feature map ;

[0042] ;

[0043] in, Represents the parameters of the compensation network; represents a fully connected network;

[0044] The compensated feature map Convert to a fixed-length vector ;

[0045] ;

[0046] in, Represents high-dimensional features of images; Indicates that the compensated feature map is at position The values of all channels at ; Indicates all channels; Indicates the height of the feature map; Indicates the width of the feature map; A variable representing the height index of the feature map; The width index variable representing the feature map;

[0047] Normalize the eigenvector and output the final eigenvector:

[0048] ;

[0049] in, Represents the high-dimensional features of the image after normalization; express The Euclidean norm of .

[0050] As a further improvement of this technical solution, in S3.3, the specific process of analyzing the facial feature vector using the convolutional neural network algorithm that introduces the illumination variable is as follows:

[0051] Use the final feature vector in S3.2 ;

[0052] Define the Euclidean distance similarity metric:

[0053] ;

[0054] in, Represents the feature vector and The Euclidean distance between Represents element index variable; represents the dimension of the feature vector; represents the Euclidean distance;

[0055] The loss function of the final feature vector is obtained:

[0056] ;

[0057] in, represents the loss function of the final feature vector; represents the sample label; Indicates boundary value;

[0058] Considering that the illumination variable will affect the robustness of the feature vector, the illumination variable is introduced Optimize;

[0059] Among them, the illumination variable Including light intensity and light direction ;

[0060] ;

[0061] Lighting variables Perform lighting compensation:

[0062] ;

[0063] in, Represents the feature vector after illumination compensation; Represents the parameters of the illumination compensation network; A fully connected network representing illumination compensation;

[0064] The definition of Euclidean distance similarity metric after introducing the illumination variable:

[0065] ;

[0066] in, Represents the feature vector and The Euclidean distance between

[0067] The loss function of the feature vector after illumination compensation is obtained:

[0068] ;

[0069] in, Represents the loss function of the feature vector after illumination compensation.

[0070] As a further improvement of this technical solution, the specific steps involved in controlling access control through the control module are:

[0071] S7.1. Receive the identity verification result from the face recognition unit;

[0072] S7.2. When the verification result is passed, the control module sends an opening signal to the access control hardware to allow the person to pass;

[0073] S7.3. If the verification fails, the access control module is used to keep the door closed.

[0074] S7.4. After the access control action is completed, a confirmation message is sent to the face recognition unit.

[0075] As a further improvement of this technical solution, the control module includes a verification module and a switch module;

[0076] The verification module is used to make a judgment based on the verification result and then convey an instruction to the switch module;

[0077] The switch module is used to control the switch of the access control.

[0078] As a further improvement of this technical solution, the specific steps involved in establishing a network connection through a wireless network module based on the IP network protocol to ensure stable data transmission between the various components in the access control system are as follows:

[0079] S9.1. Use the IP network protocol to establish a network connection with the other components of the access control system through the wireless network module to ensure stable data transmission;

[0080] S9.2. Pack, encrypt, and send data exchanged between units. Simultaneously, receive, decrypt, and parse data packets from other units.

[0081] S9.3. Monitor the data of each unit and keep them synchronized;

[0082] S9.4. Monitor the status of network connections, detect and resolve network failures, and ensure the normal operation of the system.

[0083] As a further improvement of the present technical solution, in S9.1, the wireless network module includes a wireless communication module and a network interface module;

[0084] Wherein, the wireless communication module is used for sending and receiving wireless signals;

[0085] The network interface module is used to manage network connections and process data packets in the network protocol stack.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] 1. This facial recognition-based smart access control and management system utilizes strict permission settings to ensure only authorized personnel can enter specific areas, enhancing system security. High recognition accuracy is maintained even in complex environments, reducing the risk of misidentification. Real-time facial image capture and processing ensures a fast authentication process and improves access efficiency. A convolutional neural network algorithm that incorporates illumination variables compensates for the effects of lighting, ensuring stable and accurate user recognition under varying lighting conditions.

[0088] 2. This facial recognition-based smart access control system strictly controls the on / off status of access control, preventing unauthorized access and enhancing system security. Wireless network connectivity reduces wiring costs and installation complexity, enhancing system flexibility and scalability. IP network protocols ensure reliable data transmission, supporting wide-area network coverage and suitable for venues of all sizes. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is the overall flow chart of the present invention;

[0090] The meaning of each number in the figure is:

[0091] 1. User management unit; 11. Information management module; 2. Face recognition unit; 21. Portrait acquisition module; 211. Infrared sensor module; 212. Camera module; 3. Access control unit; 31. Control module; 311. Verification module; 312. Switch module; 4. Communication integration unit; 41. Wireless network module; 411. Wireless communication module; 412. Network interface module. DETAILED DESCRIPTION

[0092] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0093] See also Figure 1 As shown, a smart access control intelligent management system based on face recognition is provided, including a user management unit 1, which manages and maintains the personal information and access rights of authorized users based on an information management module 11;

[0094] In this example, the user management unit 1 includes an information management module 11;

[0095] The information management module 11 is used to manage user information data and admit new user information data, and set different access rights for each user according to the user's role.

[0096] Specifically, the information management module 11 within the user management unit 1 plays a crucial role. It manages and maintains detailed information about all users, including but not limited to personal identification information and facial images, and supports the entry of new user information. More importantly, the information management module 11 can assign appropriate access rights to each user based on their role (e.g., resident, visitor, staff member, etc.), ensuring that only authorized personnel can access specific areas, thereby improving system security and management efficiency. In this way, the information management module 11 not only simplifies the user information management process but also effectively enhances the intelligence and security of the access control system.

[0097] The smart access control intelligent management system based on face recognition also includes a face recognition unit 2. The face recognition unit 2 captures the facial images of people entering and leaving the user through the portrait acquisition module 21 based on the user information of the user management unit 1, introduces the aerosol influence variable for data processing, and performs identity authentication through a convolutional neural network algorithm that introduces the illumination variable;

[0098] In this example, facial images of people entering and leaving the room are captured by the portrait acquisition module 21, aerosol influence variables are introduced for data processing, and identity verification is performed using a convolutional neural network algorithm that introduces illumination variables. The specific steps involved are as follows:

[0099] S3.1. When a person approaches the access control, the portrait acquisition module 21 captures the facial image of the person entering or leaving the access control;

[0100] Among them, facial images include facial contours, facial features, and skin color;

[0101] In this example, the portrait acquisition module 21 includes an infrared sensor module 211 and a camera module 212;

[0102] The infrared sensor module 211 is used to determine whether there is a person approaching the access control;

[0103] The camera module 212 is used to capture facial images when the infrared sensor module 211 is triggered.

[0104] Specifically, the infrared sensor module 211 and camera module 212 in the portrait acquisition module 21 work together to ensure the system's efficient and accurate identification of individuals. The infrared sensor module 211 first detects whether a person is approaching the access control system. Once this is detected, it immediately triggers the camera module 212 to activate, preparing to capture facial images. This design not only reduces unnecessary resource consumption (such as power and processing power) but also improves system responsiveness and accuracy, as the camera module is only activated when absolutely needed. This way, the system can quickly prepare for facial recognition before a person reaches the access control system, achieving a seamless and secure access management experience.

[0105] S3.2. Introducing the aerosol influence variable to process the facial image captured by the portrait acquisition module 21 to obtain a facial feature vector;

[0106] In this example, the specific steps of introducing the aerosol influence variable to process the facial image captured by the portrait acquisition module 21 and obtain the facial feature vector are as follows:

[0107] Input facial image:

[0108] ;

[0109] in, represents the input facial image; Indicates the height of the facial image; Indicates the width of the facial image; Indicates the number of channels; represents the set of real numbers;

[0110] Normalize the facial image:

[0111] ;

[0112] in, represents the normalized facial image; represents the mean of facial images; represents the standard deviation of facial images;

[0113] The expression for correcting the posture by the least squares method is:

[0114] ;

[0115] in, Represents posture parameters; represents the function that transforms the key points from the world coordinate system to the image coordinate system; Indicates the position of the reference key point; Indicates the number of key points; Represents the key point index variable; Represents the coordinates of the key point in the world coordinate system;

[0116] Specifically, The function expression is:

[0117] ;

[0118] in, Indicates the key point Coordinates on the axis; Indicates the key point Coordinates on the axis; Indicates the key point Coordinates on the axis;

[0119] According to the estimated posture parameters , apply the affine transformation matrix Correct the face image to a frontal perspective;

[0120] The transformed image is:

[0121] ;

[0122] in, represents the transformed facial image; represents the affine transformation matrix;

[0123] Specifically, the expression of the affine transformation matrix is:

[0124] ;

[0125] in, Indicates control zoom; Indicates controlled rotation; and Indicates controlled shearing; Indicates The amount of translation in the axis direction; Indicates The amount of translation in the axis direction;

[0126] Specifically, this process significantly enhances the robustness and accuracy of facial recognition in smart access control systems, especially when dealing with faces in varying poses and angles. Through precise posture correction, the system effectively reduces misidentifications caused by variations in head posture, ensuring only authorized individuals can pass through the access control system, while improving the user experience and overall system security. Furthermore, the standardized frontal view provides a more consistent foundation for subsequent feature extraction and matching, further enhancing recognition efficiency and accuracy.

[0127] Considering that the aerosol influence variable has an impact on the accuracy of the facial feature vector, the aerosol influence variable is introduced to optimize the facial feature vector;

[0128] Among them, aerosol-affecting variables include aerosol concentration;

[0129] Using the Aerosol Concentration Estimation Network The corrected image Evaluate the degree of its influence by aerosol and obtain the aerosol concentration value;

[0130] ;

[0131] in, Indicates the concentration of aerosol;

[0132] Specifically, the aerosol concentration estimation network for:

[0133] ;

[0134] in, Indicates the number of layers in the network; Indicates the The weight matrix of the layer; Indicates the The weight matrix of the layer; Indicates the The bias vector of the layer; Indicates the The bias vector of the layer; Indicates the The weight matrix of the layer; Indicates the The bias vector of the layer; Represents the activation function:

[0135] in, for:

[0136] ;

[0137] in, represents the Euler number; represents the input vector;

[0138] According to the concentration of aerosol Dynamically adjust feature maps , generate the compensated feature map ;

[0139] ;

[0140] in, Represents the parameters of the compensation network; represents a fully connected network;

[0141] The compensated feature map Convert to a fixed-length vector ;

[0142] ;

[0143] in, Represents high-dimensional features of images; Indicates that the compensated feature map is at position The values of all channels at ; Indicates all channels; Indicates the height of the feature map; Indicates the width of the feature map; A variable representing the height index of the feature map; The width index variable representing the feature map;

[0144] Normalize the eigenvector and output the final eigenvector:

[0145] ;

[0146] in, Represents the high-dimensional features of the image after normalization; express The Euclidean norm of .

[0147] Specifically, by accurately assessing the impact of aerosol and applying corresponding feature compensation, the system can maintain efficient recognition performance even in adverse environments, ensuring that only authorized personnel can enter specific areas, thereby improving the overall system security and user experience. At the same time, this approach also reduces the risk of misidentification, making access management more intelligent and efficient.

[0148] The introduction of aerosol impact variables significantly improves the recognition accuracy and reliability of facial recognition-based smart access control systems in adverse weather conditions, such as fog and haze. By quantifying the impact of aerosol on image quality and dynamically adjusting the feature extraction process to compensate for these adverse factors, the system can more accurately identify facial features and reduce the risk of misidentification caused by environmental interference. This mechanism ensures stable operation of the access control system even in low visibility conditions, maintaining efficient and secure access management, and enhancing the overall robustness of the system and user experience.

[0149] S3.3. Analyze facial feature vectors using a convolutional neural network algorithm that introduces illumination variables.

[0150] In this example, the specific process of analyzing facial feature vectors using the convolutional neural network algorithm with the introduction of illumination variables is as follows:

[0151] Use the final feature vector in S3.2 ;

[0152] Define the Euclidean distance similarity metric:

[0153] ;

[0154] in, Represents the feature vector and The Euclidean distance between Represents element index variable; represents the dimension of the feature vector; represents the Euclidean distance;

[0155] The loss function of the final feature vector is obtained:

[0156] ;

[0157] in, represents the loss function of the final feature vector; represents the sample label; Indicates boundary value;

[0158] Considering that the illumination variable will affect the robustness of the feature vector, the illumination variable is introduced Optimize;

[0159] Among them, the illumination variable Including light intensity and light direction ;

[0160] ;

[0161] Lighting variables Perform lighting compensation:

[0162] ;

[0163] in, Represents the feature vector after illumination compensation; Represents the parameters of the illumination compensation network; A fully connected network representing illumination compensation;

[0164] The definition of Euclidean distance similarity metric after introducing the illumination variable:

[0165] ;

[0166] in, Represents the feature vector and The Euclidean distance between

[0167] The loss function of the feature vector after illumination compensation is obtained:

[0168] ;

[0169] in, Represents the loss function of the feature vector after illumination compensation.

[0170] Specifically, in a smart access control and intelligent management system based on facial recognition, introducing a convolutional neural network algorithm with illumination variables to analyze facial feature vectors can significantly improve the system's robustness and accuracy. By performing illumination compensation on the final feature vector received from the S3.2 module, the system can maintain stable recognition performance under different lighting conditions (such as daytime, nighttime, indoors, and outdoors). Specifically, the illumination compensation mechanism reduces the impact of illumination changes on feature vectors by estimating and adjusting the light intensity and direction, making the feature representation of faces of the same identity more consistent under different lighting conditions. This not only improves the similarity between positive sample pairs, but also increases the difference between negative sample pairs, thereby enhancing the system's discriminative ability. Ultimately, the illumination-compensated loss function ensures that the model can more accurately identify users, reduces false positive and false negative rates, and improves user experience and security.

[0171] S3.4. Compare the analysis results with the facial data of the authorized user stored in the user management unit 1 to determine whether there is a match;

[0172] The authorized user facial data includes the recorded facial image and the recorded user information data;

[0173] S3.5. Regardless of the verification result, the face recognition unit 2 will record the verification event for subsequent query and audit.

[0174] Specifically, the facial images of people entering and leaving are captured by the portrait acquisition module 21, and the identity authentication process is performed in combination with a convolutional neural network algorithm that takes into account the variables affected by aerosol, ensuring that the system can accurately identify users under various environmental conditions. Specifically, when a person approaches the access control, the system automatically captures their facial image and uses an optimized deep learning algorithm to analyze the image and extract facial feature points, which can maintain a high recognition accuracy even in the presence of adverse environmental factors such as aerosol. Subsequently, the extracted features are compared with the facial data of authorized users in the database to quickly determine whether the person has access rights. Regardless of the verification result, the system will record the event and provide detailed logs for subsequent query and audit. This process not only enhances the security and reliability of the access control system, but also improves the user experience and ensures efficient and intelligent access management.

[0175] The smart access control intelligent management system based on face recognition also includes an access control control unit 3, which controls the access control through a control module 31 based on the result of the identity authentication of the face recognition unit 2;

[0176] In this example, the specific steps involved in controlling access control through the control module 31 are:

[0177] S7.1. Receive the identity verification result from the face recognition unit 2;

[0178] S7.2. When the verification result is passed, the control module 31 sends an opening signal to the access control hardware to allow the person to pass;

[0179] In this example, the control module 31 includes a verification module 311 and a switch module 312;

[0180] The verification module 311 is used to make a judgment based on the verification result and then transmit an instruction to the switch module 312;

[0181] The switch module 312 is used to control the opening and closing of the access control.

[0182] Specifically, in the smart access control intelligent management system based on facial recognition, the verification module 311 and the switch module 312 in the control module 31 work together to ensure the security and automation of access control operations. Specifically, the verification module 311 makes a judgment based on the identity authentication result provided by the facial recognition unit, decides whether to allow passage, and conveys the corresponding instructions to the switch module 312. The switch module 312 accurately controls the opening or closing of the access control based on the received instructions. This mechanism not only achieves a seamless connection between fast and accurate identity authentication and access control, but also ensures that only authorized personnel can enter the controlled area, thereby enhancing the security and reliability of the system, while providing an efficient access management experience.

[0183] S7.3. If the verification fails, the control module 31 keeps the door closed.

[0184] S7.4. After the access control action is completed, a confirmation message is sent to the face recognition unit 2.

[0185] Specifically, the control module 31 plays a key bridging role, connecting the identity authentication process with the operation of the physical access control. The control module 31 receives the identity authentication result from the face recognition unit 2 and decides whether to send an opening signal or keep the access control closed based on whether the authentication is successful. When the authentication is successful, it triggers the access control hardware to open, allowing authorized personnel to pass; when the authentication fails, it ensures that the access control remains locked to prevent unauthorized access. In addition, after each access control action is completed, the control module 31 will also feedback a confirmation message to the face recognition unit 2 to complete the entire control cycle and record the event, ensuring the security of the system and the traceability of the operation. This mechanism is crucial for achieving efficient, secure and automated access management, ensuring that only verified personnel can enter the controlled area.

[0186] The smart access control intelligent management system based on face recognition also includes a communication integration unit 4. The communication integration unit 4 is based on the IP network protocol and establishes a network connection through a wireless network module 41 to ensure stable data transmission between various components in the access control system.

[0187] In this example, based on the IP network protocol, a network connection is established through the wireless network module 41 to ensure stable data transmission between the components in the access control system. The specific steps involved are:

[0188] S9.1. Use the IP network protocol to establish a network connection with the other components of the access control system through the wireless network module 41 to ensure stable data transmission;

[0189] In this example, the wireless network module 41 includes a wireless communication module 411 and a network interface module 412 ;

[0190] Wherein, the wireless communication module 411 is used for sending and receiving wireless signals;

[0191] The network interface module 412 is used to manage network connections and process data packets in the network protocol stack.

[0192] Specifically, the wireless communication module 411 and the network interface module 412 in the wireless network module 41 jointly ensure efficient, secure and stable wireless data transmission between the various components in the system. The wireless communication module 411 is responsible for sending and receiving wireless signals, allowing different parts of the access control system to communicate without physical connections, enhancing the flexibility and deployment convenience of the system. The network interface module 412 manages the entire network connection and processes data packets in the network protocol stack, including data packaging, encryption, transmission, and decryption and parsing after reception, to ensure the security and integrity of data transmission. The two work together to not only support real-time data exchange, but also monitor and maintain the health of the network, promptly detect and resolve potential network problems, thereby ensuring the stable operation and efficient management of the access control system. This mechanism is crucial for achieving seamless integration and remote management of intelligent access control systems.

[0193] S9.2. Pack, encrypt, and send data exchanged between units. Simultaneously, receive, decrypt, and parse data packets from other units.

[0194] S9.3. Monitor the data of each unit and keep them synchronized;

[0195] S9.4. Monitor the status of network connections, detect and resolve network failures, and ensure the normal operation of the system.

[0196] Specifically, the stable data transmission mechanism established through the IP network protocol and wireless network module 41 ensures efficient and secure data exchange between various components within the system. Specifically, this mechanism not only uses the IP network protocol to establish a stable wireless connection to ensure reliable data transmission, but also packages and encrypts the exchanged data to ensure data security and privacy. At the same time, it continuously monitors the data synchronization status between each unit and monitors the network connection status in real time, capable of promptly detecting and resolving potential network failures, thereby maintaining the normal operation and responsiveness of the system. This process is crucial for achieving intelligent management of the access control system and providing uninterrupted security, ensuring the smooth operation of every link, from user information management to facial recognition verification.

[0197] The basic principles, main features, and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above-described embodiments. The above-described embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention claimed.

Claims

1. Intelligent access control management system based on face recognition, characterized by: include A user management unit (1), the user management unit (1) managing and maintaining personal information and access rights of authorized users based on the information management module (11); A face recognition unit (2), based on the user information of the user management unit (1), captures the facial image of the person entering or leaving through the portrait acquisition module (21), introduces an aerosol influence variable for data processing, and performs identity authentication through a convolutional neural network algorithm that introduces an illumination variable; An access control unit (3), the access control unit (3) controls access control via a control module (31) based on the result of identity verification by the face recognition unit (2); A communication integration unit (4), wherein the communication integration unit (4) establishes a network connection through a wireless network module (41) based on an IP network protocol to ensure stable data transmission between various components in the access control system; The specific steps of introducing the aerosol influence variable to process the facial image captured by the portrait acquisition module (21) and obtain the facial feature vector are as follows: Input facial image: ； in, represents the input facial image; Indicates the height of the facial image; Indicates the width of the facial image; Indicates the number of channels; represents the set of real numbers; Normalize the facial image: ； in, represents the normalized facial image; represents the mean of facial images; represents the standard deviation of facial images; The expression for correcting the posture by the least squares method is: ； in, Represents posture parameters; represents the function that transforms the key points from the world coordinate system to the image coordinate system; Indicates the position of the reference key point; Indicates the number of key points; Represents the key point index variable; Represents the coordinates of the key point in the world coordinate system; According to the estimated posture parameters , apply the affine transformation matrix Correct the face image to a frontal perspective; The transformed image is: ； in, represents the transformed facial image; represents the affine transformation matrix; Considering that the aerosol influence variable has an impact on the accuracy of the facial feature vector, the aerosol influence variable is introduced to optimize the facial feature vector; Among them, aerosol-affecting variables include aerosol concentration; Using the Aerosol Concentration Estimation Network The corrected image Evaluate the degree of its influence by aerosol and obtain the aerosol concentration value; ； in, Indicates the concentration of aerosol; According to the concentration of aerosol Dynamically adjust feature maps , generate the compensated feature map ; ； in, Represents the parameters of the compensation network; represents a fully connected network; The compensated feature map Convert to a fixed-length vector ; ； in, Represents high-dimensional features of images; Indicates that the compensated feature map is at position The values of all channels at ; Indicates all channels; Indicates the height of the feature map; Indicates the width of the feature map; A variable representing the height index of the feature map; The width index variable representing the feature map; Normalize the eigenvector and output the final eigenvector: ； in, Represents the high-dimensional features of the image after normalization; express The Euclidean norm of .

2. The smart access control intelligent management system based on face recognition according to claim 1 is characterized by: The user management unit (1) includes an information management module (11); The information management module (11) is used to manage user information data and admit new user information data, and to set different access rights for each user according to the user's role.

3. The smart access control intelligent management system based on face recognition according to claim 1 is characterized by: The face recognition unit (2) captures the facial image of the person entering or leaving through the portrait acquisition module (21), introduces the aerosol influence variable for data processing, and performs identity authentication through the convolutional neural network algorithm that introduces the illumination variable. The specific steps involved are: S3.

1. When a person approaches the access control, the portrait acquisition module (21) captures the facial image of the person entering or leaving the access control; Among them, facial images include facial contours, facial features, and skin color; S3.2, introducing the aerosol influence variable to process the facial image captured by the portrait acquisition module (21) to obtain a facial feature vector; S3.

3. Analyze facial feature vectors using a convolutional neural network algorithm that introduces illumination variables. S3.4, comparing the analysis result with the facial data of the authorized user stored in the user management unit (1) to determine whether there is a match; The authorized user facial data includes the recorded facial image and the recorded user information data; S3.

5. Regardless of the verification result, the face recognition unit (2) will record the verification event for subsequent query and audit.

4. The smart access control intelligent management system based on face recognition according to claim 3 is characterized by: In S3.1, the portrait acquisition module (21) includes an infrared sensor module (211) and a camera module (212); The infrared sensor module (211) is used to determine whether a person is approaching the access control system; The camera module (212) is used to capture a facial image of a person when the infrared sensor module (211) is triggered.

5. The smart access control intelligent management system based on face recognition according to claim 4 is characterized by: In S3.3, the specific process of analyzing the facial feature vector using the convolutional neural network algorithm that introduces the illumination variable is as follows: Use the final feature vector in S3.2 ; Define the Euclidean distance similarity metric: ； in, Represents the feature vector and The Euclidean distance between Represents element index variable; represents the dimension of the feature vector; represents the Euclidean distance; The loss function of the final feature vector is obtained: ； in, represents the loss function of the final feature vector; represents the sample label; Indicates boundary value; Considering that the illumination variable will affect the robustness of the feature vector, the illumination variable is introduced Optimize; Among them, the illumination variable Including light intensity and light direction ; ; Lighting variables Perform lighting compensation: ; in, Represents the feature vector after illumination compensation; Represents the parameters of the illumination compensation network; A fully connected network representing illumination compensation; The definition of Euclidean distance similarity metric after introducing the illumination variable: ； in, Represents the feature vector and The Euclidean distance between The loss function of the feature vector after illumination compensation is obtained: ； in, Represents the loss function of the feature vector after illumination compensation.

6. The smart access control intelligent management system based on face recognition according to claim 1 is characterized by: The specific steps involved in the access control unit (3) controlling the access control via the control module (31) are: S7.

1. Receive the result of identity authentication from the face recognition unit (2); S7.2, when the verification result is passed, the control module (31) sends an opening signal to the access control hardware to allow the personnel to pass; S7.3, when the verification fails, the access control is kept closed by the control module (31); S7.

4. After the access control action is completed, a confirmation message is sent to the face recognition unit (2).

7. The intelligent access control management system based on face recognition according to claim 6 is characterized by: The control module (31) includes a verification module (311) and a switch module (312); The verification module (311) is used to make a judgment based on the verification result and then transmit an instruction to the switch module (312); The switch module (312) is used to control the switch of the access control.

8. The smart access control intelligent management system based on face recognition according to claim 1 is characterized by: The communication integration unit (4) establishes a network connection through the wireless network module (41) based on the IP network protocol to ensure stable data transmission between the components in the access control system. The specific steps involved are: S9.

1. Use the IP network protocol to establish a network connection with the remaining components of the access control system through the wireless network module (41) to ensure stable data transmission; S9.

2. Pack, encrypt, and send data exchanged between units. Simultaneously, receive, decrypt, and parse data packets from other units. S9.

3. Monitor the data of each unit and keep them synchronized; S9.

4. Monitor the status of network connections, detect and resolve network failures, and ensure the normal operation of the system.

9. The intelligent access control management system based on face recognition according to claim 8 is characterized by: In S9.1, the wireless network module (41) includes a wireless communication module (411) and a network interface module (412); Wherein, the wireless communication module (411) is used for sending and receiving wireless signals; The network interface module (412) is used to manage network connections and process data packets in the network protocol stack.

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