CN111062339A - Face recognition method, device, equipment and storage medium based on block chain - Google Patents
Face recognition method, device, equipment and storage medium based on block chain Download PDFInfo
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Abstract
The embodiment of the invention discloses a face recognition method, a face recognition device, face recognition equipment and a storage medium based on a block chain, wherein the method comprises the steps of respectively obtaining face images through three cameras; sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network; and sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction to generate a face identification result containing a face position. The scheme improves the safety of face recognition, eliminates potential safety hazards, is high in recognition efficiency and has strong flexibility.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a face recognition method, a face recognition device, face recognition equipment and a storage medium based on a block chain.
Background
Face recognition is a biometric technology for identifying an identity based on facial feature information of a person, such as a series of related technologies, which are generally called face recognition and face recognition, for example, a camera or a video camera is used to collect an image or a video stream containing a face, and automatically detect and track the face in the image, thereby performing face recognition on the detected face.
In the existing face recognition method, a single-camera mode is adopted, so that the face recognition method is easy to crack by pictures or videos, great potential safety hazards exist, two plane face images are collected by a binocular camera, the potential safety hazards are great, and in addition, the flexibility and the efficiency of face recognition are poor.
Disclosure of Invention
The embodiment of the invention provides a face recognition method, a face recognition device, face recognition equipment and a storage medium based on a block chain, which improve the safety of face recognition, eliminate potential safety hazards, and have high recognition efficiency and stronger flexibility.
In a first aspect, an embodiment of the present invention provides a face recognition method based on a block chain, where the method includes:
respectively acquiring face images through three cameras;
sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network;
and sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction to generate a face identification result containing a face position.
Optionally, the sending the three face images to an edge server includes:
determining three nodes in a block chain network, and respectively sending one of the three face images to one node.
Optionally, the identifying instruction includes any one of a face image enhancement instruction, a face selection instruction, and a face region processing instruction, and the sending the identifying instruction to the edge server includes:
and sending an identification instruction to any node in the block chain network.
Optionally, the edge server performs face image recognition based on the recognition instruction, including:
and the edge server identifies the face image based on the identification instruction and a comparison database stored in the edge server.
Optionally, the edge server performs face image recognition based on the recognition instruction, including:
the edge server performs image recognition based on the recognition instruction to obtain an image feature vector;
sending the image feature vector to an edge server corresponding to another node in the block chain network;
and a comparison database is stored in the edge server corresponding to the other node, and the face image recognition is carried out based on the image characteristic vector and the comparison database.
Optionally, the edge server performs face image recognition based on the recognition instruction, and generates a face recognition result including a face position, including:
the edge server carries out face image recognition based on the recognition instruction to obtain an HOG feature vector corresponding to the face image;
and determining the face position according to the HOG characteristic vector, and generating a face recognition result containing the face position.
Optionally, after generating a face recognition result including a face position, the method further includes:
and if the face recognition result meets the preset recognition condition and the preset position condition, generating verification passing information.
In a second aspect, an embodiment of the present invention further provides a face recognition apparatus based on a block chain, where the apparatus includes:
the face image acquisition module is used for respectively acquiring face images through the three cameras;
the image sending module is used for sending the three face images to the edge servers, wherein each edge server is used as a node in the block chain, and different edge servers form a block chain network;
and the instruction sending module is used for sending an identification instruction to the edge server so as to enable the edge server to carry out face image identification based on the identification instruction and generate a face identification result containing the face position.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for face recognition based on a block chain according to the embodiment of the present invention.
In a fourth aspect, the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the method for recognizing a face based on a block chain according to the present invention.
In the embodiment of the invention, the face images are respectively obtained through three cameras; sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network; and sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction to generate a face identification result containing a face position. The scheme improves the safety of face recognition, eliminates potential safety hazards, is high in recognition efficiency and has strong flexibility.
Drawings
Fig. 1 is a flowchart of a face recognition method based on a block chain according to an embodiment of the present invention;
fig. 2 is a flowchart of another face recognition method based on a block chain according to an embodiment of the present invention;
fig. 3 is a flowchart of another method for face recognition based on a block chain according to an embodiment of the present invention;
fig. 4 is a flowchart of another face recognition method based on a block chain according to an embodiment of the present invention;
fig. 5 is a flowchart of another method for face recognition based on a block chain according to an embodiment of the present invention;
fig. 6 is a flowchart of another method for face recognition based on a block chain according to an embodiment of the present invention;
fig. 7 is a flowchart of another method for face recognition based on a block chain according to an embodiment of the present invention;
fig. 8 is a flowchart of another method for face recognition based on a block chain according to an embodiment of the present invention;
fig. 9 is a flowchart of another method for face recognition based on a block chain according to an embodiment of the present invention;
fig. 10 is a block diagram of a face recognition apparatus based on a block chain according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad invention. It should be further noted that, for convenience of description, only some structures, not all structures, relating to the embodiments of the present invention are shown in the drawings.
Fig. 1 is a flowchart of a face recognition method based on a block chain according to an embodiment of the present invention, where the present embodiment is applicable to a scene in which a face is recognized, for example, a scene during face brushing payment, and the method may be executed by a computing device such as a virtual base station, and specifically includes the following steps:
and step S101, respectively acquiring face images through three cameras.
In one embodiment, the face recognition is performed on the user who performs face brushing shopping, and in the recognition process, the three cameras respectively acquire face images, specifically, the three cameras shoot faces through different angles, for example, the three cameras arranged right in front of the face, in front of the left and in front of the right, respectively shoot the face images to obtain three face images with different angles. In another embodiment, three cameras are integrated in the same device, such as a computing device with triple-camera function, and three face image pictures are obtained by respectively shooting face images through each camera.
Different from the prior art, the prior art adopts single shooting or double shooting, is face recognition based on a plane, is easy to crack and cheat in the payment process, and has larger potential safety hazard.
And S102, sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network.
In one embodiment, the face image shot by the camera is sent to the edge servers, wherein each edge server is used as a node in the block chain, and different edge servers form a block chain network. There are a plurality of different nodes in a blockchain network, i.e. there are a plurality of different edge servers that can perform image recognition calculations separately.
Step S103, sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction and generating a face identification result containing a face position.
In one embodiment, the execution subject virtual base station and the adjacent edge server are both deployed with an edge computing application and provide edge computing capabilities. Specifically, after a physical machine is installed at a base station end, a base station virtual machine system is installed on the physical machine, an RUU (radio frequency unit) system is installed, then link setting and communication testing between adjacent virtual base stations and with a cloud end are carried out, after the testing is finished, edge computing application is deployed in the virtual base stations, and the building of the edge computing virtual base stations is finished. Optionally, a plurality of base station virtual machine systems may be deployed on one physical machine to support different indications.
Furthermore, after the edge servers are built, the edge servers are added into the blockchain network as one blockchain block node, so as to form a blockchain network consisting of a plurality of edge servers as the blockchain block nodes, and the data transmission is performed between the edge servers based on a common identification mechanism, such as pow (workload certification mechanism), pos (equity certification mechanism), Dpos (entrustment equity certification), pool (verification pool common identification mechanism), and the like.
In one embodiment, the virtual base station sends an instruction to the edge server, the edge server performs face image recognition based on the recognition instruction, and since three cameras are used to respectively acquire face images, the face position is further calculated during the recognition process compared with the recognition process of the face image, and a recognition result containing the face position is obtained.
According to the scheme, the three cameras are used for respectively acquiring the face images and sending the three face images to the edge servers, each edge server is used as a node in the block chain, different edge servers form the block chain network, and an identification instruction is sent to the edge servers, so that the edge servers are used for carrying out face image identification based on the identification instruction, and a face identification result containing the face position is generated. In the scheme, the face recognition result comprises the specific position of the face, more parameters are obtained compared with plane recognition, the face recognition safety can be guaranteed through the determination of the face position, the cracking of two-dimensional images such as photos and videos is avoided, the payment safety is guaranteed, meanwhile, the edge server is realized based on a block chain network, and the reliability of the processing result is guaranteed.
Fig. 2 is a flowchart of another block chain-based face recognition method according to an embodiment of the present invention, which provides a specific method for sending three face images to an edge server. As shown in fig. 2, the technical solution is as follows:
step S201, obtaining face images through three cameras respectively.
Step S202, determining three nodes in a block chain network, and respectively sending one of the three face images to one node, wherein each edge server is used as one node in the block chain, and different edge servers form the block chain network.
In one embodiment, when the identification instruction is sent, any three nodes in the blockchain network are determined as edge servers for image processing, illustratively, image 1, image 2 and image 3, image 1 is sent to the edge server 1, image 2 is sent to the edge server 2, image 3 is sent to the edge server 3, the edge server 1 identifies image 1, the edge server 2 identifies image 2, the edge server 3 identifies image 3, and the three images are identified by 3 edge servers, respectively, instead of being identified by the same computing device.
Step S203, sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction to generate a face identification result containing a face position.
According to the scheme, the three nodes in the block chain network are determined, one of the three face images is sent to one node for processing, the image recognition processing load is reduced, the image recognition processing efficiency is improved, and meanwhile, the data safety is guaranteed by performing respective independent recognition through different edge servers.
Fig. 3 is a flowchart of another block chain-based face recognition method according to an embodiment of the present invention, which provides a specific method for sending a recognition instruction to an edge server. As shown in fig. 3, the technical solution is as follows:
and S301, respectively acquiring the face images through the three cameras.
Step S302, the three face images are sent to edge servers, wherein each edge server serves as a node in a block chain, and different edge servers form a block chain network.
Step S303, sending an identification instruction to any node in the block chain network, wherein the identification instruction is used for the edge server to identify the face image based on the identification instruction, and generating a face identification result containing the face position.
In one embodiment, the virtual base station sends an identification instruction to any node in the blockchain network, the any node transmits the identification instruction to the nodes in the whole blockchain network after receiving the identification instruction, for example, the identification instruction is sent to the node corresponding to the edge server 0, the edge server 0 distributes the identification instruction to the edge server 1, the edge server 2 and the edge server 3, and the edge server 1, the edge server 2 and the edge server 3 respectively process the image 1, the image 2 and the image 3.
According to the scheme, the identification instruction is sent to any node in the block chain network to perform instruction distribution and image identification processing, the efficiency of data processing is improved, meanwhile, the instruction does not need to be sent to a plurality of edge servers, the identification instruction can be processed by any node in a unified mode, and the consistency of data processing is guaranteed.
Fig. 4 is a flowchart of another face recognition method based on a block chain according to an embodiment of the present invention, and a specific recognition instruction content is given. As shown in fig. 4, the technical solution is as follows:
step S401, the face images are respectively obtained through the three cameras.
And S402, sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network.
Step S403, sending a face image enhancement instruction to an edge server, where the edge server performs face image enhancement based on the face image enhancement instruction to generate a face recognition result including a face position.
In one embodiment, the virtual base station can send a face image enhancement instruction to any edge server in the block chain network, the virtual base station can perform preliminary identification and judgment on the acquired face image, when the judgment result shows that the image quality does not meet the preset quality condition, the face image enhancement instruction is generated and sent to the edge server, and the edge server performs image enhancement on the face image so as to meet the face identification standard.
According to the scheme, the face image enhancement instruction is sent to the edge server, the edge server is used for enhancing the face image based on the face image enhancement instruction, the face recognition result containing the face position is generated, the instruction mutual transmission and intercommunication can be efficiently realized, meanwhile, the face image can be enhanced, and the face recognition efficiency is improved.
Fig. 5 is a flowchart of another face recognition method based on a block chain according to an embodiment of the present invention, and shows a specific recognition instruction content. As shown in fig. 5, the technical solution is as follows:
and step S501, obtaining face images through three cameras respectively.
And S502, sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network.
Step S503, sending a face selection instruction to an edge server, wherein the face selection instruction is used for the edge server to select a face image based on the face selection instruction, and generating a face recognition result containing a face position.
In an embodiment, the virtual base station may send a face selection instruction to any edge server in the block chain network, the virtual base station may perform preliminary identification and determination on an acquired face image, when it is determined that the image includes a plurality of faces, a face selection instruction is generated and sent to the edge server, the edge server selects the plurality of faces included in the face image to obtain a face to be identified, exemplarily, an area occupied by each face included in the face image is determined, and the face with the largest area is determined as the identified face.
According to the scheme, the face selection instruction is sent to the edge server, the edge server is used for carrying out face selection based on the face selection instruction, a face recognition result containing the face position is generated, mutual transmission and intercommunication of the instruction can be efficiently realized, the problem that a face image contains multiple faces can be solved, and the face recognition efficiency and flexibility are improved.
Fig. 6 is a flowchart of another face recognition method based on a block chain according to an embodiment of the present invention, and a specific recognition instruction content is given. As shown in fig. 6, the technical solution is as follows:
step S601, the face images are respectively obtained through the three cameras.
Step S602, the three face images are sent to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network.
Step S603, sending a face region processing instruction to an edge server, where the edge server performs face image recognition based on the face region processing instruction to generate a face recognition result including a face position.
In an embodiment, the virtual base station may send a face region processing instruction to any edge server in the blockchain network, the virtual base station may perform preliminary identification and determination on the acquired face image, when it is determined that a certain position region of the face image is unclear or blocked by the determination, a face region processing instruction is generated and sent to the edge server, and the edge server performs optimization processing on a face region in the face image, for example, performs image enhancement processing on eyes of a face.
According to the scheme, the face area processing instruction is sent to the edge server, the edge server is used for selecting the face based on the face area processing instruction, and a face recognition result containing the face position is generated, so that the mutual transmission and intercommunication of the instruction can be efficiently realized, meanwhile, the face image can be processed for area processing, and the face recognition efficiency and flexibility are improved.
Fig. 7 is a flowchart of another method for face image recognition based on a block chain according to an embodiment of the present invention, and shows a specific method for face image recognition based on the recognition instruction by an edge server. As shown in fig. 7, the technical solution is as follows:
and step S701, respectively acquiring the face images through the three cameras.
Step S702, the three face images are sent to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network.
Step S703 of sending a face recognition instruction to an edge server, where the edge server performs face image recognition based on the recognition instruction to generate a face recognition result including a face position.
Step S704, the edge server performs image identification based on the identification instruction to obtain an image feature vector, and sends the image feature vector to an edge server corresponding to another node in the block chain network.
Step S705, a comparison database is stored in the edge server corresponding to the other node, and face image recognition is performed based on the image feature vector and the comparison database.
In one embodiment, after obtaining the image feature vector, the edge server performing image processing sends the image feature vector to an edge server corresponding to another node in the block chain network, and a comparison of the identification pictures of the comparison database is stored in the edge server corresponding to the other node, so as to determine whether the edge server is the user.
According to the scheme, the comparison database for comparison is stored in other nodes in the block chain, so that the data security is further ensured.
In another embodiment, the edge server performs face image recognition based on the recognition instruction, including: and the edge server identifies the face image based on the identification instruction and a comparison database stored in the edge server.
Fig. 8 is a flowchart of another block chain-based face recognition method according to an embodiment of the present invention, which provides a specific method for an edge server to perform face image recognition based on the recognition instruction to generate a face recognition result including a face position. As shown in fig. 8, the technical solution is as follows:
and step S801, respectively acquiring the face images through the three cameras.
Step S802, the three face images are sent to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network.
Step S803, sending a face recognition instruction to an edge server, wherein the edge server is used for carrying out face image recognition based on the recognition instruction to generate a face recognition result containing a face position.
Step S804, the edge server carries out face image recognition based on the recognition instruction to obtain an HOG feature vector corresponding to the face image, determines the face position according to the HOG feature vector, and generates a face recognition result containing the face position.
In one embodiment, the face position is determined by identifying the corresponding HOG feature vector of the face image, wherein, the HOG feature is a feature descriptor used for object detection in computer vision and image processing. It constructs features by calculating and counting the histogram of gradient direction of local area of image. Specifically, the determination is performed by the following steps: normalizing the color/Gamma correction image; calculating the size and the direction of the image gradient; performing weight projection on the gradient direction in the cell according to the gradient amplitude; HOG feature vector normalization; the HOG features of all blocks are connected to form the final feature vector.
Specifically, the face position determination process may be:
obtaining an average position of each face key point in a plurality of face key points from three obtained face images, determining the obtained average position of each face key point as an initial position X0 of each face key point in the face images, determining an initial position X0 of each face key point as a coordinate position of each face key point in the face images, obtaining HOG (Histogram of oriented gradients) characteristics at the initial position X0 of each face key point through an SDM (software description model), determining a characteristic vector Y0 according to the HOG characteristics at the initial position X0 of each face key point, calculating a displacement △ X0 from the initial position X0 of each face key point to a tracking position through the characteristic vector Y0 and a specified function, then adding the initial positions X0 and △ X0 of each face key point to obtain an iteration result, and repeating the iteration result in the same way until the obtained iteration result is a fixed value, or determining the change of the obtained iteration result in the face images as the change of the start position of the face images.
According to the scheme, the edge server carries out face image recognition based on the recognition instruction to obtain the HOG feature vector corresponding to the face image, the face position is determined according to the HOG feature vector, and the face recognition result containing the face position is generated.
Fig. 9 is a flowchart of another block chain-based face recognition method according to an embodiment of the present invention, which provides a specific processing method after obtaining a recognition result. As shown in fig. 9, the technical solution is as follows:
step S901, obtaining face images through three cameras respectively.
And S902, sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network.
Step S903, sending a face recognition instruction to an edge server, wherein the edge server is used for carrying out face image recognition based on the recognition instruction and generating a face recognition result containing a face position.
And step S904, if the face recognition result meets the preset recognition condition and the preset position condition, generating verification passing information.
In one embodiment, after the comparison of the comparison database, the face image recognition is confirmed to be successful, and the obtained face position condition meets the preset condition, and the recognition result is determined to be available. The preset position condition comprises that the recognized face is a three-dimensional face, and the face meets the three-dimensional position condition.
According to the scheme, if the face recognition result meets the preset recognition condition and the preset position condition, verification passing information is generated, the success rate of face recognition is guaranteed, and potential safety hazards are eliminated.
Fig. 10 is a block diagram of a face recognition apparatus based on a block chain according to an embodiment of the present invention, where the apparatus is configured to execute the face recognition method based on a block chain according to the embodiment, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 10, the apparatus specifically includes: a face image acquisition module 101, an image sending module 102 and an instruction sending module 103, wherein,
a face image acquisition module 101, configured to acquire face images through three cameras respectively;
the image sending module 102 is configured to send the three face images to edge servers, where each edge server is used as a node in a block chain, and different edge servers form a block chain network;
the instruction sending module 103 is configured to send an identification instruction to an edge server, so that the edge server performs face image identification based on the identification instruction to generate a face identification result including a face position.
According to the scheme, the three cameras are used for respectively acquiring the face images; sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network; and sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction to generate a face identification result containing a face position. The scheme improves the safety of face recognition, eliminates potential safety hazards, is high in recognition efficiency and has strong flexibility.
In a possible embodiment, the image sending module 102 is specifically configured to:
determining three nodes in a block chain network, and respectively sending one of the three face images to one node.
In a possible embodiment, the identification instruction includes any one of a face image enhancement instruction, a face selection instruction, and a face region processing instruction, and the image sending module 102 is specifically configured to:
and sending an identification instruction to any node in the block chain network.
In one possible embodiment, the edge server is specifically configured to:
and the edge server identifies the face image based on the identification instruction and a comparison database stored in the edge server.
In one possible embodiment, the edge server is specifically configured to:
the edge server performs image recognition based on the recognition instruction to obtain an image feature vector;
sending the image feature vector to an edge server corresponding to another node in the block chain network;
and a comparison database is stored in the edge server corresponding to the other node, and the face image recognition is carried out based on the image characteristic vector and the comparison database.
In one possible embodiment, the edge server is specifically configured to:
the edge server carries out face image recognition based on the recognition instruction to obtain an HOG feature vector corresponding to the face image;
and determining the face position according to the HOG characteristic vector, and generating a face recognition result containing the face position.
In a possible embodiment, the apparatus further includes a result generation module, configured to generate verification pass information if a face recognition result including a face position satisfies a preset recognition condition and a preset position condition after generating the face recognition result.
Fig. 11 is a schematic structural diagram of an apparatus according to an embodiment of the present invention, as shown in fig. 11, the apparatus includes a processor 201, a memory 202, an input device 203, and an output device 204; the number of the processors 201 in the device may be one or more, and one processor 201 is taken as an example in fig. 11; the processor 201, the memory 202, the input device 203 and the output device 204 in the apparatus may be connected by a bus or other means, for example in fig. 6.
The memory 202 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method for face recognition based on block chains in the embodiment of the present invention. The processor 201 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 202, that is, the above-mentioned face recognition method based on the block chain is realized.
The memory 202 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 202 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 202 may further include memory located remotely from the processor 201, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 203 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the apparatus. The output device 204 may include a display device such as a display screen.
Embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for block chain-based face recognition, the method including:
respectively acquiring face images through three cameras;
sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network;
and sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction to generate a face identification result containing a face position.
From the above description of the embodiments, it is obvious for those skilled in the art that the embodiments of the present invention can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better implementation in many cases. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions to make a computer device (which may be a personal computer, a server, or a network device) perform the methods described in the embodiments of the present invention.
It should be noted that, in the embodiment of the above-mentioned face recognition apparatus based on a block chain, each unit and each module included in the embodiment are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
It should be noted that the foregoing is only a preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will appreciate that the embodiments of the present invention are not limited to the specific embodiments described herein, and that various obvious changes, adaptations, and substitutions are possible, without departing from the scope of the embodiments of the present invention. Therefore, although the embodiments of the present invention have been described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and many other equivalent embodiments may be included without departing from the concept of the embodiments of the present invention, and the scope of the embodiments of the present invention is determined by the scope of the appended claims.
Claims (10)
1. The face recognition method based on the block chain is characterized by comprising the following steps:
respectively acquiring face images through three cameras;
sending the three face images to edge servers, wherein each edge server is used as a node in a block chain, and different edge servers form a block chain network;
and sending an identification instruction to an edge server, wherein the edge server is used for carrying out face image identification based on the identification instruction to generate a face identification result containing a face position.
2. The method of claim 1, wherein sending the three face images to an edge server comprises:
determining three nodes in a block chain network, and respectively sending one of the three face images to one node.
3. The method according to claim 1, wherein the identification instruction comprises any one of a face image enhancement instruction, a face selection instruction and a face region processing instruction, and the sending the identification instruction to the edge server comprises:
and sending an identification instruction to any node in the block chain network.
4. The method of claim 3, wherein the edge server performs facial image recognition based on the recognition instruction, comprising:
and the edge server identifies the face image based on the identification instruction and a comparison database stored in the edge server.
5. The method of claim 3, wherein the edge server performs facial image recognition based on the recognition instruction, comprising:
the edge server performs image recognition based on the recognition instruction to obtain an image feature vector;
sending the image feature vector to an edge server corresponding to another node in the block chain network;
and a comparison database is stored in the edge server corresponding to the other node, and the face image recognition is carried out based on the image characteristic vector and the comparison database.
6. The method of claim 1, wherein the edge server performs face image recognition based on the recognition instruction, and generates a face recognition result including a face position, comprising:
the edge server carries out face image recognition based on the recognition instruction to obtain an HOG feature vector corresponding to the face image;
and determining the face position according to the HOG characteristic vector, and generating a face recognition result containing the face position.
7. The method according to any one of claims 1-6, further comprising, after generating a face recognition result containing a face position:
and if the face recognition result meets the preset recognition condition and the preset position condition, generating verification passing information.
8. Face recognition device based on block chain, its characterized in that includes:
the face image acquisition module is used for respectively acquiring face images through the three cameras;
the image sending module is used for sending the three face images to the edge servers, wherein each edge server is used as a node in the block chain, and different edge servers form a block chain network;
and the instruction sending module is used for sending an identification instruction to the edge server so as to enable the edge server to carry out face image identification based on the identification instruction and generate a face identification result containing the face position.
9. An apparatus, the apparatus comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the blockchain based face recognition method according to any one of claims 1 to 7.
10. A storage medium containing computer executable instructions for performing the blockchain based face recognition method of any one of claims 1 to 7 when executed by a computer processor.
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| CN201911320918.3A CN111062339A (en) | 2019-12-19 | 2019-12-19 | Face recognition method, device, equipment and storage medium based on block chain |
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| CN201911320918.3A CN111062339A (en) | 2019-12-19 | 2019-12-19 | Face recognition method, device, equipment and storage medium based on block chain |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111950416A (en) * | 2020-07-31 | 2020-11-17 | 中国工商银行股份有限公司 | Face recognition method and system based on block chain |
| CN113032594A (en) * | 2021-02-26 | 2021-06-25 | 广东核电合营有限公司 | Label image storage method and device, computer equipment and storage medium |
| CN113610632A (en) * | 2021-08-11 | 2021-11-05 | 中国银行股份有限公司 | Bank outlet face recognition method and device based on block chain |
-
2019
- 2019-12-19 CN CN201911320918.3A patent/CN111062339A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111950416A (en) * | 2020-07-31 | 2020-11-17 | 中国工商银行股份有限公司 | Face recognition method and system based on block chain |
| CN111950416B (en) * | 2020-07-31 | 2023-08-29 | 中国工商银行股份有限公司 | Face recognition method and system based on block chain |
| CN113032594A (en) * | 2021-02-26 | 2021-06-25 | 广东核电合营有限公司 | Label image storage method and device, computer equipment and storage medium |
| CN113032594B (en) * | 2021-02-26 | 2023-12-08 | 广东核电合营有限公司 | Label image storage method, apparatus, computer device and storage medium |
| CN113610632A (en) * | 2021-08-11 | 2021-11-05 | 中国银行股份有限公司 | Bank outlet face recognition method and device based on block chain |
| CN113610632B (en) * | 2021-08-11 | 2024-05-28 | 中国银行股份有限公司 | Bank outlet face recognition method and device based on blockchain |
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Application publication date: 20200424 |