CN112241684A - Face retrieval distributed computing method and system - Google Patents
Face retrieval distributed computing method and system Download PDFInfo
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- CN112241684A CN112241684A CN202010973461.2A CN202010973461A CN112241684A CN 112241684 A CN112241684 A CN 112241684A CN 202010973461 A CN202010973461 A CN 202010973461A CN 112241684 A CN112241684 A CN 112241684A
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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Abstract
The invention discloses a face retrieval distributed computing method, which comprises the steps of receiving a face retrieval task, obtaining retrieval task data and analyzing the task data; carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison; searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID; storing the face data retrieved by the distributed face snapshot library into a distributed storage system; and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system. The invention aims to solve the technical problem of low retrieval and calculation rate caused by the fact that face recognition retrieval is usually carried out in a face library with mass data in the prior art.
Description
Technical Field
The invention relates to the technical field of face recognition, in particular to a face retrieval distributed computing method.
Background
The wisdom community is the organic component of wisdom urban technology, including intelligent building, intelligent home, multiple aspects such as wisdom public service, aim at using multiple means such as pattern recognition, thing networking, mobile internet, cloud technique, big data synthetically, for the resident of community provides safe, convenient, comfortable living and living experience to reinforcing the management and the service of mechanisms such as property, security protection, living committee, public security to the community, reduce manpower and materials cost and promote the managerial efficiency.
The method realizes identity recognition, is the basic requirement of the smart community, and can play an important role in the aspects of access control, personnel and house registration, owner special facilities, service and the like of the smart community. Because video monitoring in the community is rapidly popularized, a plurality of existing video monitoring devices can acquire images rapidly in a remote and user-uncoordinated state, so that the identity of personnel can be confirmed rapidly in a remote manner, intelligent early warning is realized, and therefore the intelligent community can be the best choice to realize identity recognition by adopting a face recognition technology.
Face recognition is a biometric technology for identity recognition based on facial feature information of a person. The technology comprises a series of related technologies, namely face recognition and face recognition, wherein the related technologies comprise the steps of collecting images or video streams containing faces by using a camera or a camera, automatically detecting and tracking the faces in the images, further extracting face feature information of the detected faces, and recognizing the identities of people based on comparison of the face feature information.
Therefore, in view of the fact that the face recognition technology plays a significant role in the layout of the smart city, how to increase the calculation rate of face recognition retrieval is a technical problem that needs to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a face retrieval distributed computing method, and aims to solve the technical problem that in the prior art, face recognition retrieval is usually carried out in a face library with mass data, so that the retrieval computing speed is low.
In order to achieve the above object, the present invention provides a face retrieval distributed computing method, which comprises the following steps:
receiving a face retrieval task, acquiring retrieval task data and analyzing the task data;
carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison;
searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID;
storing the face data retrieved by the distributed face snapshot library into a distributed storage system;
and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system.
Preferably, the distributed computing method for face retrieval further includes the following steps of, before receiving a face retrieval task: and acquiring the face characteristic information in the area range, and performing face filing on the acquired face characteristic information and the characteristic value of the file library by taking the same face as a unit, and giving a file ID to each face.
Preferably, the retrieval task is submitted and transmitted to the back end through a front-end platform, and the back-end algorithm gateway extracts the face feature data according to the feature extraction request.
Preferably, the face retrieval distributed computing method includes the following steps: the retrieval limiting conditions comprise position limiting conditions and time limiting conditions.
Preferably, after the task data is analyzed, the analyzed face picture data and the retrieval limiting conditions are subjected to data assembly, the data are sent to a face retrieval message queue, and the face feature comparison algorithm is waited to call.
Preferably, the face retrieval result output is that the retrieval result of the back-end algorithm gateway is transmitted to the front-end platform and is visually displayed on a page.
Preferably, the distributed computing method for face retrieval adopts a MongoDB database.
In a second aspect of the present invention, a face retrieval distributed computing system is provided, including:
the retrieval task information receiving module: receiving a background face retrieval task;
the retrieval task data analysis module: acquiring retrieval task data and analyzing the task data;
the distributed face archive library special diagnosis comparison module: carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison;
the distributed face snapshot library retrieval module: searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID;
a retrieval result storage module: storing the face data retrieved by the distributed face snapshot library into a distributed storage system;
a message notification module: and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system.
In the invention, by receiving a face retrieval task, retrieval task data is obtained and the task data is analyzed; carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison; searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID; storing the face data retrieved by the distributed face snapshot library into a distributed storage system; and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system. The invention aims to solve the technical problem of low retrieval and calculation rate caused by the fact that face recognition retrieval is usually carried out in a face library with mass data in the prior art.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating a step principle of a distributed computing method for face retrieval according to the present invention;
FIG. 2 is a schematic diagram of a structural principle of a face retrieval distributed computing system according to the present invention;
fig. 3 is a schematic diagram of internal data flow of a face retrieval distributed computing system according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an embodiment, and referring to fig. 1, fig. 1 is a schematic diagram illustrating a step principle of a face retrieval distributed computing method provided by the invention.
As shown in fig. 1, in this embodiment, a face retrieval distributed computing method includes the following steps:
receiving a face retrieval task, acquiring retrieval task data and analyzing the task data;
carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison;
searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID;
storing the face data retrieved by the distributed face snapshot library into a distributed storage system;
and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system.
It should be noted that the face retrieval distributed computing method further includes a step of performing face profiling before receiving a face retrieval task: and acquiring the face characteristic information in the area range, and performing face filing on the acquired face characteristic information and the characteristic value of the file library by taking the same face as a unit, and giving a file ID to each face.
In this embodiment, the retrieval task is submitted and transmitted to the back end through the front-end platform, and the back-end algorithm gateway extracts the face feature data according to the feature extraction request. The retrieval task comprises face picture data and retrieval limiting conditions, wherein: the retrieval limiting conditions comprise position limiting conditions and time limiting conditions. And after the task data is analyzed, performing data assembly on the analyzed face picture data and the retrieval limiting conditions, sending the data to a face retrieval message queue, and waiting for calling of a face feature comparison algorithm. The face retrieval result output is to transmit the retrieval result of the back-end algorithm gateway to the front-end platform and to be displayed visually on the page.
Specifically, the distributed storage system adopts a MongoDB database.
In this embodiment, as shown in fig. 2, a face retrieval distributed computing system is provided, where the face retrieval distributed computing system includes:
the retrieval task information receiving module: receiving a background face retrieval task;
the retrieval task data analysis module: acquiring retrieval task data and analyzing the task data;
the distributed face archive library special diagnosis comparison module: carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison;
the distributed face snapshot library retrieval module: searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID;
a retrieval result storage module: storing the face data retrieved by the distributed face snapshot library into a distributed storage system;
a message notification module: and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system.
For a more clear description of the principle of the steps of the present invention, the principle is now described with reference to fig. 2, which is as follows:
the distributed face retrieval service mainly comprises: the system comprises a retrieval task information receiving service, a retrieval task data analysis service, a distributed face archive characteristic comparison service, a distributed face snapshot library retrieval service, a retrieval result storage service and a task retrieval completion message notification service, wherein the functions of the modules are as follows:
receiving service function of retrieval task information: receiving a face retrieval task sent by a background;
the retrieval task data analysis service function comprises the following steps: and after the retrieval task data is acquired, analyzing the task data.
The distributed face archive feature comparison service function comprises: the face feature data in the retrieval task and the face feature data in the face archive library are compared in a distributed mode, the pressure of algorithm service GPU is greatly reduced, the cost of the GPU is reduced, meanwhile, the calculation speed is greatly improved, and then the corresponding face archive ID is obtained through distributed face feature comparison.
The distributed face snapshot library retrieval service function is as follows: after the face archive ID acquired by the distributed face archive feature comparison service, the face archive ID is used for searching corresponding face data in the distributed face snapshot library, and the feature value in the search task and the snapshot library are not used for comparative search, so that the search speed and the search efficiency are greatly improved.
The retrieval result storage service function: and storing the face data retrieved by the distributed face snapshot library into the mongoDB.
The task retrieval completion message notification service function comprises the following steps: and after the retrieval task is executed, returning a task execution completion notification message to notify the background service to acquire a face retrieval result from the mongdb.
In the traditional face retrieval service, according to the general face retrieval logic, the face feature to be retrieved and the face feature of the snapshot library (huge) are compared in a face feature calculation mode, so that retrieval comparison and condition screening are needed to be carried out in a large amount of time, however, before the distributed face retrieval, a program carries out face filing on data after face information acquisition, a face file is built on the same face, when the distributed face retrieval is carried out, the face feature to be retrieved and the face file library are used for carrying out distributed calculation comparison (the calculation amount is greatly reduced, meanwhile, the calculation efficiency is greatly improved in the distributed calculation mode), the face file ID can be quickly positioned, the face information is retrieved through the face file ID to the snapshot library, the face retrieval efficiency can be greatly improved, the face retrieval accuracy is also improved while the query efficiency is greatly improved, the method greatly reduces a large amount of time cost and labor cost required by the previous face retrieval, and greatly improves the production efficiency.
As shown in fig. 3, the flow of the distributed face retrieval service in the present embodiment is described as follows:
1. firstly, the front end submits face picture data and other attribute data to the back end, the back end sends a feature extraction request to an algorithm gateway, and the algorithm gateway requests the algorithm for extracting the face feature after receiving the request.
2. After successfully extracting the face features by the algorithm, returning the face features to the algorithm gateway, returning the algorithm gateway to the corresponding topic, and after monitoring the feature return by the program, sending the face retrieval message queue after finishing the assembly of the face task data to be retrieved.
3. After receiving the kafka retrieval queue information, the face retrieval program firstly analyzes the received json data, then obtains the feature data in the retrieval task, then loads the face archive, compares the face features to be retrieved with the face features in the face archive, obtains the face archive ID after comparison, and then retrieves the face snapshot data from the face snapshot library through the face archive ID.
4. When searching in the face snapshot library, besides searching the face archive ID, the related searching conditions are added to search together.
5. After the retrieval is finished, the retrieval result of face snapshot retrieval is stored in mongoDB, and then the information of task retrieval completion is sent to topic.
After monitoring the information in the topic which is successfully retrieved, the background program queries the retrieved result data through the task ID, and displays the retrieved result to a page, thereby completing the flow of face retrieval.
In the embodiment, by receiving a face retrieval task, retrieving task data and analyzing the task data; carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison; searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID; storing the face data retrieved by the distributed face snapshot library into a distributed storage system; and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system. The invention aims to solve the technical problem of low retrieval and calculation rate caused by the fact that face recognition retrieval is usually carried out in a face library with mass data in the prior art.
The methods, systems, and modules disclosed herein may be implemented in other ways. For example, the above-described embodiments are merely illustrative, and for example, the division of the modules may be merely a logical division, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be referred to as an indirect coupling or communication connection through some interfaces, systems or modules, and may be in an electrical, mechanical or other form.
The modules described as discrete components may or may not be physically separate, and the components shown as modules may or may not be physical modules, may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A face retrieval distributed computing method is characterized by comprising the following steps:
receiving a face retrieval task, acquiring retrieval task data and analyzing the task data;
carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison;
searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID;
storing the face data retrieved by the distributed face snapshot library into a distributed storage system;
and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system.
2. The distributed computing method for face retrieval according to claim 1, wherein the distributed computing method for face retrieval further comprises the step of face profiling before receiving the face retrieval task: and acquiring the face characteristic information in the area range, and performing face filing on the acquired face characteristic information and the characteristic value of the file library by taking the same face as a unit, and giving a file ID to each face.
3. The distributed computing method for human face retrieval as claimed in claim 1, wherein the retrieval task is submitted and transmitted to the back end through a front end platform, and human face feature data is extracted by a back end algorithm gateway according to the feature extraction request.
4. The distributed computing method for face retrieval according to claim 1, wherein the retrieval task includes face picture data and retrieval defining conditions, wherein: the retrieval limiting conditions comprise position limiting conditions and time limiting conditions.
5. The distributed computing method for face retrieval according to claim 1, wherein after the task data is parsed, the parsed face picture data and retrieval limiting conditions are subjected to data assembly, and the data are sent to a face retrieval message queue to wait for the face feature comparison algorithm to be called.
6. The distributed computing method for face retrieval according to claim 1, wherein the face retrieval result output is obtained by transmitting the retrieval result of the back-end algorithm gateway to the front-end platform and visually displaying the retrieval result on a page.
7. The distributed computing method for face retrieval as claimed in claim 1, wherein the distributed storage system employs a MongoDB database.
8. A face retrieval distributed computing system, the face retrieval distributed computing system comprising:
the retrieval task information receiving module: receiving a background face retrieval task;
the retrieval task data analysis module: acquiring retrieval task data and analyzing the task data;
the distributed face archive library special diagnosis comparison module: carrying out distributed comparison on the face feature data in the retrieval task and the face feature data in a face archive library, and obtaining an archive ID of the face through the distributed face feature comparison;
the distributed face snapshot library retrieval module: searching corresponding face data in a distributed face snapshot library by using the obtained face archive ID;
a retrieval result storage module: storing the face data retrieved by the distributed face snapshot library into a distributed storage system;
a message notification module: and feeding back the retrieval completion notification information, and outputting the face retrieval result in the distributed storage system.
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Application publication date: 20210119 |