CN113971831A - Dynamically updated face recognition method and device and electronic equipment - Google Patents
Dynamically updated face recognition method and device and electronic equipment Download PDFInfo
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Abstract
The application discloses a dynamic update face recognition method, a dynamic update face recognition device and electronic equipment, wherein the method comprises the following steps: acquiring system configuration parameters which comprise face recognition algorithm type identifiers; calling a corresponding face recognition algorithm according to the face recognition algorithm type identifier, and extracting features of a face picture to be detected to obtain a face feature vector; calling a data linked list corresponding to the face recognition algorithm type identification from a database, wherein different face image identifications and face feature values obtained by extracting features of different face images based on the face recognition algorithm corresponding to the face recognition algorithm type identification are stored in the data linked list; traversing the face characteristic values in the data linked list, comparing the face characteristic values with the face characteristic vectors respectively, calculating the similarity, and outputting the face image with the maximum similarity and the corresponding personnel information; the invention is convenient for updating and upgrading the face recognition algorithm and can adapt to the rapid development and replacement of the face recognition technology.
Description
Technical Field
The present application relates to the field of face recognition technologies, and in particular, to a dynamically updated face recognition method, device and electronic device.
Background
In recent years, with rapid development of technologies such as computer vision technology, big data, artificial intelligence, machine learning and the like, face recognition technology has exploded growth in various countries, and great convenience is brought to work and life of people. The face recognition technology is a biometric technology for performing identification based on facial feature information of a person. The method comprises the steps of collecting images or video streams containing human faces by using a camera or a camera, automatically detecting and tracking the human faces in the images, and further identifying and judging the faces of the detected human faces. The face recognition system mainly comprises four components, which are respectively: the method comprises the steps of face image acquisition and detection, face image preprocessing, face image feature extraction and face image matching and identification.
The feature extraction of the face image and the matching and identification of the face image are the key points of the application of the face identification technology. The face image feature extraction is to extract a string of numbers for representing face information. The face image matching and identification is to search and match the extracted face image feature data with a face feature data template stored in a database and output the result obtained by matching. This requires the use of face recognition algorithms, which are the basis of face recognition technology. The face recognition algorithms are various, and with the deep technical research, more face recognition algorithms are applied in the market. Therefore, the face recognition system needs to be compatible with various face recognition algorithms, so that the face recognition algorithms can be updated quickly and applied to actual products.
However, the face recognition systems on the market currently use a specific face recognition algorithm. The face feature extraction and the comparative analysis thereof are based on a single face model, the compatibility of a face recognition algorithm is not considered, the face recognition algorithm is inconvenient to update and upgrade, and the rapid development and replacement of the face recognition technology cannot be adapted.
Disclosure of Invention
The invention provides a dynamic update face recognition method, a dynamic update face recognition device and electronic equipment, aiming at solving the problems of single face recognition method and inconvenient update of the existing face recognition system.
To achieve the above object, according to an aspect of the present invention, there is provided a dynamically updated face recognition method, including the steps of:
acquiring system configuration parameters which comprise face recognition algorithm type identifiers;
calling a corresponding face recognition algorithm according to the face recognition algorithm type identifier, and extracting features of a face picture to be detected to obtain a face feature vector;
calling a data linked list corresponding to the face recognition algorithm type identification from a database, wherein different face images and face characteristic values obtained by carrying out characteristic extraction on the different face images based on the face recognition algorithm corresponding to the face recognition algorithm type identification are stored in the data linked list;
and traversing the face characteristic values in the data linked list, comparing the face characteristic values with the face characteristic vectors respectively, calculating the similarity, and outputting the face image with the maximum similarity and the corresponding personnel information.
Preferably, the above-mentioned dynamically updated face recognition method further includes:
acquiring an updated face recognition algorithm, wherein the updated face recognition algorithm has a corresponding face recognition algorithm type identifier;
extracting all face images from a database, and performing feature re-extraction on all face images based on an updated face recognition algorithm to obtain an updated value of facial features;
and associating the face recognition algorithm type identification and the face image identification corresponding to the updated face recognition algorithm and the face feature updating value corresponding to the face recognition algorithm, and storing the face recognition algorithm type identification and the face image identification in a database.
Preferably, in the above dynamically updated face recognition method, the system configuration parameters further include a calculation mode corresponding to a face recognition algorithm, a face feature value type, and a length parameter.
Preferably, in the above dynamically updated face recognition method, different face recognition algorithms form a dynamic link library in the form of plug-ins;
taking the type identification of the face recognition algorithm as an index to call a corresponding face recognition algorithm from the dynamic link library, and extracting the features of a face picture to be detected; alternatively, the first and second electrodes may be,
and obtaining an updated face recognition algorithm from the dynamic link library by taking the face recognition algorithm type identification as an index, and performing feature re-extraction on all face images extracted from the database.
According to a second aspect of the present invention, there is also provided a dynamically updated face recognition apparatus, comprising:
the system comprises a configuration module, a face recognition module and a display module, wherein the configuration module is used for acquiring system configuration parameters which comprise face recognition algorithm type identifiers;
the face data extraction module is used for calling a corresponding face recognition algorithm according to the face recognition algorithm type identifier, and extracting the features of a face picture to be detected to obtain a face feature vector;
the face data comparison module is used for calling a data linked list corresponding to the face recognition algorithm type identification from a database, wherein different face image identifications and face characteristic values obtained by extracting the characteristics of different face images based on the face recognition algorithm corresponding to the face recognition algorithm type identification are stored in the data linked list; traversing the face characteristic values in the data linked list, comparing the face characteristic values with the face characteristic vectors respectively and calculating the similarity;
and the result output module is used for acquiring the calculation result of the face data comparison module and outputting the face image with the maximum similarity and the corresponding personnel information.
Preferably, the above-mentioned face recognition device with dynamic update further comprises a feature re-extraction module;
the feature re-extraction module is used for acquiring an updated face recognition algorithm, and the updated face recognition algorithm has a corresponding face recognition algorithm type identifier; extracting all face images from a database, and performing feature re-extraction on all face images based on an updated face recognition algorithm to obtain an updated value of facial features;
and associating the face recognition algorithm type identification, the face image identification and the face feature updating value corresponding to the updated face recognition algorithm, and storing the face recognition algorithm type identification, the face image identification and the face feature updating value in a database.
Preferably, in the above dynamically updated face recognition apparatus, the system configuration parameters further include a calculation mode corresponding to a face recognition algorithm, a face feature value type, and a length parameter.
Preferably, in the above dynamically updated face recognition apparatus, different face recognition algorithms form a dynamically linked library in the form of plug-in.
Preferably, in the above dynamically updated face recognition apparatus, the face data extraction module takes the face recognition algorithm type identifier as an index to invoke a corresponding face recognition algorithm from the dynamic link library, and performs feature extraction on a face picture to be detected;
and the feature re-extraction module takes the type identification of the face recognition algorithm as an index to acquire an updated face recognition algorithm from the dynamic link library and performs feature re-extraction on all face images extracted from the database.
According to a third aspect of the present invention, there is also provided an electronic device comprising at least one processing unit, and at least one memory unit, wherein the memory unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of any of the above-mentioned methods.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the system is compatible with various different face recognition algorithms, the face data extraction module and the face data comparison module can obtain face recognition algorithm information currently used by the system through the configuration module, corresponding face recognition algorithms can be called through identifying different face recognition algorithm types and calculation modes, corresponding face characteristic value extraction and comparison processes are executed, accordingly, compared face similarity results are obtained, and the purpose of dynamically controlling the face recognition algorithms is achieved. The characteristic re-extraction module is used for assisting the updating of the human face characteristic value data when the human face recognition algorithm is updated, so that the human face recognition system can conveniently and directly compare the new human face characteristic value data in the database when a new human face recognition algorithm is started, and the consistency of comparison data is ensured.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a logic block diagram of a dynamically updated face recognition apparatus provided in this embodiment;
fig. 2 is a schematic diagram of a logical structure of a feature re-extraction module according to the present embodiment;
fig. 3 is a flowchart illustrating a start-up process of the face recognition apparatus according to the present embodiment;
fig. 4 is a flowchart illustrating a face recognition process according to the present embodiment;
fig. 5 is a flowchart of feature re-extraction relating to the present embodiment;
fig. 6 is a schematic flow chart of a dynamically updated face recognition method according to this embodiment;
fig. 7 is a logic block diagram of the computer device provided in the present embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The terms "first," "second," "third," and the like in the description and claims of this application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Fig. 1 is a logic block diagram of a dynamically updated face recognition apparatus provided in this embodiment, where the apparatus may be implemented in software and/or hardware, and may be integrated on an electronic device; referring to fig. 1, the device comprises a configuration module, a face data extraction module, a face data comparison module, a result output module and a database;
the configuration module is used for acquiring system configuration parameters, and the system configuration parameters comprise face recognition algorithm type identifiers;
in the embodiment, various face recognition algorithms are pre-built in the system, the different face recognition algorithms form a dynamic link library in a plug-in mode, and each face recognition algorithm has a unique corresponding face recognition algorithm type identifier; the executable code for the different face recognition algorithms is located in a DLL file that contains one or more functions that have been compiled, linked and stored separately from the process in which they are used. Updates can be more easily applied to the corresponding modules of the respective face recognition algorithms using a dynamically linked library without affecting other parts.
The configuration module is responsible for acquiring configuration parameters of system operation, mainly comprising parameters of face recognition algorithm type, calculation mode, face characteristic value type and length, log information, statistical information, heartbeat time and the like, and respectively providing the configuration parameters to the face data extraction module and the face data comparison module. When the face recognition system is started, the system configuration file is read through the configuration module, and the face recognition algorithm is determined according to the face recognition algorithm configuration information in the configuration file.
The face data extraction module is used for calling a corresponding face recognition algorithm according to the face recognition algorithm type identification, and extracting the features of a face picture to be detected to obtain a face feature vector;
in this embodiment, the face data extraction module is responsible for receiving a face image data stream collected and sent by a face image, and extracting face characteristic value data from the image.
After determining the face recognition algorithm to be used according to the face recognition algorithm type identification in the system configuration parameters, the face data extraction module takes the face recognition algorithm type identification as an index to call the corresponding face recognition algorithm from the dynamic link library, and performs feature extraction on a face picture to be detected to obtain a face feature vector;
in this embodiment, a plug-in directory is constructed to store face recognition algorithm information in the dynamic link library, and the plug-in directory takes the face recognition algorithm type identifier as an index, so that the face data extraction module can quickly search and call a function of a required face recognition algorithm from the dynamic link library.
The face data comparison module is used for calling a data linked list corresponding to the face recognition algorithm type identification from a database, wherein different face image identifications and face characteristic values obtained by extracting the characteristics of different face images based on the face recognition algorithm corresponding to the face recognition algorithm type identification are stored in the data linked list; traversing the face characteristic values in the data linked list, comparing the face characteristic values with the face characteristic vectors respectively and calculating the similarity;
in this embodiment, a pre-constructed database stores data linked lists associated with each face recognition algorithm, each data linked list correspondingly stores different face image identifiers, and face feature values obtained by extracting features of different face images based on a face recognition algorithm corresponding to a certain face recognition algorithm type identifier.
After the face data comparison module obtains the face recognition algorithm type identification sent by the configuration module, a data chain table corresponding to the matched face recognition algorithm is searched from a database according to the face recognition algorithm type identification, then face image identifications in the data chain table are traversed, corresponding face characteristic values are obtained, comparison is respectively carried out with face characteristic vectors output by the face data extraction module, and the similarity of the face is calculated.
And the result output module is used for acquiring the calculation result of the face data comparison module and outputting the face image with the maximum similarity and the corresponding personnel information.
In this embodiment, the result output module is responsible for analyzing and processing the similarity result obtained by comparing the face data with the face data, and counting the most similar face pictures and the personal information thereof according to the requirements of the upper application.
Further, the above-mentioned human face recognition device of dynamic update also includes the characteristic reextraction module;
the feature re-extraction module is a functional module for assisting the updating of the face feature value data when the face recognition algorithm is updated, and is used for acquiring the updated face recognition algorithm, and the updated face recognition algorithm has a corresponding face recognition algorithm type identifier; extracting all face images from a database, and performing feature re-extraction on all face images based on an updated face recognition algorithm to obtain an updated value of facial features; and associating the face recognition algorithm type identification, the face image identification and the face feature updating value corresponding to the updated face recognition algorithm, and storing the face recognition algorithm type identification, the face image identification and the face feature updating value in a database.
If the face feature value data is changed due to the change of the face recognition algorithm, the face feature value data stored in the database originally is extracted by the previous face recognition algorithm and cannot be used in the face comparison recognition process of a new algorithm, so that the feature value needs to be extracted again. Therefore, before the new face recognition algorithm is applied to the face data extraction module and the face data comparison module, the feature re-extraction module is required to re-extract feature values of the face pictures stored in the database to obtain new face feature value data, and the new face feature value data is stored in the database.
The characteristic value re-extraction module is responsible for constructing a new face characteristic value structure before updating the face recognition algorithm, re-extracting the face characteristic value data of the face picture stored in the database, and then storing the new characteristic value data to the corresponding position of the database. In this embodiment, the feature re-extraction module obtains an updated face recognition algorithm from the dynamic link library by using the face recognition algorithm type identifier as an index, and performs feature re-extraction on all face images extracted from the database.
FIG. 2 is a schematic diagram of the logical structure of a feature re-extraction module according to the present invention; as shown in fig. 2, the feature re-extraction module includes three parts, namely a database interface, a face image reading module, and a face feature value extraction module. The database interface is responsible for providing inquiry and updating functions of the database face picture and related data thereof. The face image reading module is responsible for reading face image data obtained from the database. The face characteristic value extraction module is responsible for extracting a face characteristic value from the face picture data and writing new face characteristic value data into a corresponding position of the database through a database interface.
As an optional embodiment, the above-mentioned dynamically updated face recognition apparatus further includes a face data storage module; the face data storage module is used as an interactive bridge between the face data extraction module, the face data comparison module and the database and is mainly responsible for storing face characteristic values extracted from a face picture by the face data extraction module in the database and carrying out classification management; and searching a data linked list corresponding to the matched face recognition algorithm from the database according to the face recognition algorithm type identifier sent by the configuration module and sending the data linked list to the face data comparison module.
In the scheme, various different face recognition algorithms are pre-built in the system, the face data extraction module and the face data comparison module can obtain face recognition algorithm information currently used by the system through the configuration module, corresponding face recognition algorithms can be called through recognizing different face recognition algorithm types and calculation modes, and corresponding face characteristic value extraction and comparison processes are executed, so that a compared face similarity result is obtained, and the purpose of dynamically controlling the face recognition algorithms is achieved.
Fig. 3 is a flowchart illustrating a start-up process of the face recognition apparatus according to this embodiment.
The process begins at step 301.
Step 303 the configuration module notifies the face data extraction module of the face recognition algorithm information.
Step 304 the face data extraction module determines a face feature value extraction algorithm for the face picture.
Step 305 the configuration module informs the face data storage module of the face feature value information.
Step 306 the face data storage module determines the storage structure and data information of the face feature values.
Step 307 the configuration module notifies the face data comparison module of the face recognition algorithm information and the face feature value information.
Step 308 the face data comparison module determines the data structure of the face feature values and its comparison recognition algorithm.
Step 309 begins the heartbeat timer and the statistics timer according to the configuration file and the system is started.
Fig. 4 is a flowchart illustrating a face recognition process according to this embodiment.
Step 401 begins the process.
And step 406, the result analysis output module counts the most similar human face pictures and the personnel information thereof according to the requirements of the upper application to output the result.
Step 407 ends the process.
Fig. 5 is a flowchart of feature re-extraction according to the present embodiment.
The process begins at step 501.
Step 502 traverses the face picture data needing face feature value updating in the database through the database interface, and transmits the data to the face picture reading module.
Step 504 is to extract the face characteristic value data in the face picture through a new face recognition algorithm, and store the new face characteristic value data into the face characteristic value position corresponding to the face picture in the database through the database interface.
Step 505 ends the process.
Fig. 6 is a schematic flow chart of a dynamically updated face recognition method provided in this embodiment, and as shown in fig. 6, the method includes the following steps:
601, acquiring system configuration parameters, wherein the system configuration parameters comprise face recognition algorithm type identification, a calculation mode corresponding to a face recognition algorithm, a face characteristic value type and a length parameter;
in this example, various face recognition algorithms are pre-built in the system, and the different face recognition algorithms form a dynamic link library in a plug-in mode.
and taking the type identification of the face recognition algorithm as an index to call a corresponding face recognition algorithm from the dynamic link library, and extracting the features of the face picture to be detected.
As a preferred embodiment, the above dynamically updated face recognition method further includes a step of feature re-extraction, which specifically includes:
s1, acquiring an updated face recognition algorithm, wherein the updated face recognition algorithm has a corresponding face recognition algorithm type identifier;
s2, extracting all face images from the database, and re-extracting the features of all face images based on the updated face recognition algorithm to obtain an updated face feature value;
in this example, the face recognition algorithm type identifier is used as an index to obtain an updated face recognition algorithm from the dynamic link library, and feature re-extraction is performed on all face images extracted from the database.
S3 associates the face recognition algorithm type identification, face image identification and face feature update value corresponding to the updated face recognition algorithm, and stores the face recognition algorithm type identification, face image identification and face feature update value in the database.
It should be noted that although in the above-described embodiments, the operations of the methods of the embodiments of the present specification are described in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the steps depicted in the flowcharts may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
The present embodiment further provides an electronic device, as shown in fig. 7, which includes at least one processor and at least one memory, where the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned dynamically updated face recognition method, and specific steps are not described herein again; in this embodiment, the types of the processor and the memory are not particularly limited, for example: the processor may be a microprocessor, digital information processor, on-chip programmable logic system, or the like; the memory may be volatile memory, non-volatile memory, a combination thereof, or the like.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing terminal, display, etc.), with one or more terminals that enable a user to interact with the electronic device, and/or with any terminals (e.g., network card, modem, etc.) that enable the electronic device to communicate with one or more other computing terminals. Such communication may be through an input/output (I/O) interface. Also, the electronic device may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network, such as the internet) via the Network adapter.
The present embodiment also provides a computer readable medium, which stores a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device is caused to execute the steps of the above-mentioned dynamically updated face recognition method. Types of computer readable media include, but are not limited to, storage media such as SD cards, usb disks, fixed hard disks, removable hard disks, and the like.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A dynamically updated face recognition method is characterized by comprising the following steps:
acquiring system configuration parameters which comprise face recognition algorithm type identifiers;
calling a corresponding face recognition algorithm according to the face recognition algorithm type identifier, and extracting features of a face picture to be detected to obtain a face feature vector;
calling a data linked list corresponding to the face recognition algorithm type identification from a database, wherein different face image identifications and face characteristic values obtained by carrying out characteristic extraction on different face images based on the face recognition algorithm corresponding to the face recognition algorithm type identification are stored in the data linked list;
and traversing the face characteristic values in the data linked list, comparing the face characteristic values with the face characteristic vectors respectively, calculating the similarity, and outputting the face image with the maximum similarity and the corresponding personnel information.
2. The dynamically updated face recognition method of claim 1, further comprising:
acquiring an updated face recognition algorithm, wherein the updated face recognition algorithm has a corresponding face recognition algorithm type identifier;
extracting all face images from a database, and performing feature re-extraction on all face images based on an updated face recognition algorithm to obtain an updated value of facial features;
and associating the face recognition algorithm type identification and the face image identification corresponding to the updated face recognition algorithm and the face feature updating value corresponding to the face recognition algorithm, and storing the face recognition algorithm type identification and the face image identification in a database.
3. The method of claim 2, wherein the system configuration parameters further include a calculation method corresponding to a face recognition algorithm, a face feature value type, and a length parameter.
4. A dynamically updated face recognition method as claimed in claim 1 or 3, characterized in that different face recognition algorithms form a dynamic link library in the form of plug-ins;
taking the type identification of the face recognition algorithm as an index to call a corresponding face recognition algorithm from the dynamic link library, and extracting the features of a face picture to be detected; alternatively, the first and second electrodes may be,
and obtaining an updated face recognition algorithm from the dynamic link library by taking the face recognition algorithm type identification as an index, and performing feature re-extraction on all face images extracted from the database.
5. A dynamically updated face recognition device, comprising:
the system comprises a configuration module, a face recognition module and a display module, wherein the configuration module is used for acquiring system configuration parameters which comprise face recognition algorithm type identifiers;
the face data extraction module is used for calling a corresponding face recognition algorithm according to the face recognition algorithm type identifier, and extracting the features of a face picture to be detected to obtain a face feature vector;
the face data comparison module is used for calling a data linked list corresponding to the face recognition algorithm type identification from a database, wherein different face image identifications and face characteristic values obtained by extracting the characteristics of different face images based on the face recognition algorithm corresponding to the face recognition algorithm type identification are stored in the data linked list; traversing the face characteristic values in the data linked list, comparing the face characteristic values with the face characteristic vectors respectively and calculating the similarity;
and the result output module is used for acquiring the calculation result of the face data comparison module and outputting the face image with the maximum similarity and the corresponding personnel information.
6. The dynamically updated face recognition device of claim 1, further comprising a feature re-extraction module;
the feature re-extraction module is used for acquiring an updated face recognition algorithm, and the updated face recognition algorithm has a corresponding face recognition algorithm type identifier; extracting all face images from a database, and performing feature re-extraction on all face images based on an updated face recognition algorithm to obtain an updated value of facial features;
and associating the face recognition algorithm type identification, the face image identification and the face feature updating value corresponding to the updated face recognition algorithm, and storing the face recognition algorithm type identification, the face image identification and the face feature updating value in a database.
7. The dynamically updated face recognition device as claimed in claim 2, wherein said system configuration parameters further include a calculation mode corresponding to a face recognition algorithm, a face feature value type and a length parameter.
8. A dynamically updated face recognition device as claimed in claim 1 or 3, wherein different face recognition algorithms form a dynamically linked library in the form of plug-ins.
9. The dynamically updated face recognition device of claim 8,
the face data extraction module takes the face recognition algorithm type identification as an index to call a corresponding face recognition algorithm from the dynamic link library, and performs feature extraction on a face picture to be detected;
and the feature re-extraction module takes the type identification of the face recognition algorithm as an index to acquire an updated face recognition algorithm from the dynamic link library and performs feature re-extraction on all face images extracted from the database.
10. An electronic device, comprising at least one processing unit and at least one memory unit, wherein the memory unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of the method according to any one of claims 1 to 4.
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| CN115238324A (en) * | 2022-07-22 | 2022-10-25 | 珠海市鸿瑞信息技术股份有限公司 | Computer protection system and method based on management and use audit security |
| CN117409470A (en) * | 2023-12-15 | 2024-01-16 | 千巡科技(深圳)有限公司 | Face recognition feature data dynamic matching method, system, device and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115238324A (en) * | 2022-07-22 | 2022-10-25 | 珠海市鸿瑞信息技术股份有限公司 | Computer protection system and method based on management and use audit security |
| CN115238324B (en) * | 2022-07-22 | 2023-03-28 | 珠海市鸿瑞信息技术股份有限公司 | Computer protection system and method based on management use audit safety |
| CN117409470A (en) * | 2023-12-15 | 2024-01-16 | 千巡科技(深圳)有限公司 | Face recognition feature data dynamic matching method, system, device and medium |
| CN117409470B (en) * | 2023-12-15 | 2024-03-15 | 千巡科技(深圳)有限公司 | Face recognition feature data dynamic matching method, system, device and medium |
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