CN108197608A - Face identification method, device, robot and storage medium - Google Patents
Face identification method, device, robot and storage medium Download PDFInfo
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- CN108197608A CN108197608A CN201810103742.5A CN201810103742A CN108197608A CN 108197608 A CN108197608 A CN 108197608A CN 201810103742 A CN201810103742 A CN 201810103742A CN 108197608 A CN108197608 A CN 108197608A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- 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
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/94—Hardware or software architectures specially adapted for image or video understanding
- G06V10/95—Hardware or software architectures specially adapted for image or video understanding structured as a network, e.g. client-server architectures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- 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
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- 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
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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Abstract
The present invention relates to intelligent robot technology fields, provide a kind of face identification method, device, robot and storage medium, the method includes：The initial information of face to be identified is obtained, wherein, initial information includes face picture, picture collection time, picture collection place and the face characteristic value of face to be identified；Initial information is sent to background server, so that background server carries out feature recognition according to face characteristic value to face picture, is successfully followed by receiving and exporting face recognition result identifying.The present invention to video flowing progress image analysis in robotic end by obtaining face picture and carrying out face characteristic to face picture to extract, on the one hand it avoids a large amount of Video stream information being transmitted to background server, alleviate network pressure, on the other hand the pressure of background server is alleviated in robotic end progress feature extraction, improves the speed of recognition of face.
Description
Technical field
The present invention relates to intelligent robot technology field, in particular to a kind of face identification method, device, machine
People and storage medium.
Background technology
With the development of the city, people need the occasion of authentication more and more in life, work, simple identity
Identification card can not meet demand.At present, in miscellaneous certification, the authentication method of recognition of face is integrated with artificial intelligence
A variety of professional techniques such as energy, machine recognition, machine learning, model theory, expert system, Computer Vision, while need to combine
The theory of median processing is the more recent application of living things feature recognition with realizing, current robot carries out master during recognition of face
By facial image to be identified, either video is sent to cloud platform or the enterprising pedestrian's face feature extraction of background server and spy
Sign identification, obtains recognition result and is sent to robot, this method network delay it is big or network load it is high when, meeting
Lead to server recognition of face heavy operation load, so as to influence recognition of face speed and user experience.
Invention content
The embodiment of the present invention is designed to provide a kind of face identification method, device, robot and storage medium, to
Improve the above problem.
To achieve these goals, technical solution used in the embodiment of the present invention is as follows：
In a first aspect, an embodiment of the present invention provides a kind of face identification method, applied to robot, robot and backstage
Server-side communicates to connect, and background server is communicated to connect with terminal, the method includes：Obtain the initial letter of face to be identified
Breath, wherein, initial information includes face picture, picture collection time, picture collection place and the face characteristic of face to be identified
Value；Initial information is sent to background server, so that background server carries out feature according to face characteristic value to face picture
Identification, and face recognition result is obtained after identifying successfully, wherein, face recognition result includes the face picture after identification, figure
The identity information of piece acquisition time, picture collection place and face to be identified；Receive the recognition of face knot of background server feedback
Fruit is simultaneously exported.
Second aspect, the embodiment of the present invention additionally provide a kind of face identification device, and described device is obtained including initial information
Modulus block, initial information sending module and recognition result receiving module.Wherein, initial information acquisition module is to be identified for obtaining
The initial information of face, wherein, the face picture of initial information including face to be identified, picture collection time, picture collection
Point and face characteristic value；Initial information sending module is used to initial information being sent to background server, so that background server
Feature recognition is carried out, and obtain face recognition result after identifying successfully to face picture according to face characteristic value, wherein, face
Recognition result includes the identity information of the face picture after identification, picture collection time, picture collection place and face to be identified；
Recognition result receiving module is used to receive the face recognition result of background server feedback and be exported.
The third aspect, the embodiment of the present invention additionally provide a kind of robot, and the robot includes：One or more processing
Device；Memory, for storing one or more programs, when one or more of programs are held by one or more of processors
During row so that one or more of processors realize above-mentioned face identification method.
Fourth aspect, the embodiment of the present invention additionally provide a kind of computer readable storage medium, are stored thereon with computer
Program, the computer program realize above-mentioned face identification method when being executed by processor.
Compared with the prior art, a kind of face identification method provided in an embodiment of the present invention, device, robot and storage are situated between
Matter first, obtains the initial information of face to be identified, wherein, initial information includes the face picture of face to be identified, picture is adopted
Collect time, picture collection place and face characteristic value；Then, initial information is sent to background server, so that background service
End carries out feature recognition, and obtain face recognition result after identifying successfully according to face characteristic value to face picture, wherein, people
Face recognition result includes the identity letter of the face picture after identification, picture collection time, picture collection place and face to be identified
Breath；Finally, it receives the face recognition result of background server feedback and is exported.Compared with prior art, the present invention passes through
Video flowing progress image analysis is obtained face picture and carries out face characteristic to face picture to extract in robotic end, one
Aspect avoids a large amount of Video stream information being transmitted to background server, network pressure is alleviated, on the other hand in robotic end
The pressure that feature extraction alleviates background server is carried out, improves the speed of recognition of face.
For the above objects, features and advantages of the present invention is enable to be clearer and more comprehensible, special embodiment below, and appended by cooperation
Attached drawing is described in detail below.
Description of the drawings
It in order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range, for those of ordinary skill in the art, without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 shows face identification method application scenarios schematic diagram provided in an embodiment of the present invention.
Fig. 2 shows robot block diagrams provided in an embodiment of the present invention.
Fig. 3 shows face identification method flow chart provided in an embodiment of the present invention.
Fig. 4 is the sub-step flow chart of the step S101 shown in Fig. 3.
Fig. 5 shows the block diagram of face identification device provided in an embodiment of the present invention.
Unit block diagrams of the Fig. 6 for initial information acquisition module in the face identification device shown in Fig. 5.
Icon：100- robots；300- background servers；400- terminals；101- memories；102- storage controls；
103- processors；104- Peripheral Interfaces；105- photographic devices；106- laser reading devices；107- output devices；200- faces are known
Other device；201- initial information acquisition modules；202- initial information sending modules；203- recognition result receiving modules；2011- schemes
As analytic unit；2012- feature extraction units；2013- places generation unit.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Ground describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be configured to arrange and design with a variety of different herein.Cause
This, the detailed description of the embodiment of the present invention to providing in the accompanying drawings is not intended to limit claimed invention below
Range, but it is merely representative of the selected embodiment of the present invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Go out all other embodiments obtained under the premise of creative work, shall fall within the protection scope of the present invention.
It should be noted that：Similar label and letter represents similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need to that it is further defined and explained in subsequent attached drawing.Meanwhile the present invention's
In description, term " first ", " second " etc. are only used for distinguishing description, and it is not intended that instruction or hint relative importance.
Please refer to Fig. 1, Fig. 1 shows robot 100 provided in an embodiment of the present invention, background server 300, terminal 400
Interaction schematic diagram.Background server 300 can be communicated by network with robot 100 and terminal 400, to realize machine
The face characteristic value that people 100 will be obtained by carrying out image analysis and feature extraction to collected video flowing, and pass through network
Background server 300 is sent to, to realize that background server 300 carries out feature recognition to face characteristic value, when identifying successfully
Recognition result is sent to robot 100 and terminal 400 by network, to realize that robot 100 and terminal 400 will identifications
As a result it exports.
In embodiments of the present invention, background server 300 may be, but not limited to, in property server, property server
Virtual machine etc. can provide that either virtual machine has the entity of identical function or virtual server-side with the server.Terminal
400 may be, but not limited to, smart mobile phone, tablet computer, PC (personal computer, PC), server etc.
Deng.The operating system of terminal 400 may be, but not limited to, Android (Android) system, IOS (iPhone operating
System) system, Windows phone systems, Windows systems, linux system etc..
Fig. 2 is please referred to, Fig. 2 shows the block diagrams of robot 100 provided in an embodiment of the present invention.The robot
100 include face identification device 200, memory 101, storage control 102, processor 103, Peripheral Interface 104, photographic device
105th, laser reading devices 106 and output device 107.
Memory 101, storage control 102, processor 103, Peripheral Interface 104, photographic device 105, laser acquisition dress
Put 106 and 107 each element of output device be directly or indirectly electrically connected between each other, to realize the transmission of data or interaction.
It is electrically connected for example, these elements can be realized between each other by one or more communication bus or signal wire.Robot 100 wraps
Include at least one can be stored in memory 101 or be solidificated in the robot in the form of software or firmware (firmware)
Software function module in 100 operating system (Robot Operating System, ROS).Processor 103 is deposited for performing
The executable module stored in reservoir 101, such as software function module and computer program included by face identification device 200
Deng.
Wherein, memory 101 may be, but not limited to, random access memory (Random Access Memory,
RAM), read-only memory (Read Only Memory, ROM), programmable read only memory (Programmable Read-Only
Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory, EPROM),
Electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only Memory, EEPROM) etc..
Wherein, for memory 101 for storing program, the processor 103 performs described program after execute instruction is received.
Processor 103 can be a kind of IC chip, have signal handling capacity.Above-mentioned processor 103 can be with
It is general processor, including central processing unit (Central Processing Unit, CPU), network processing unit (Network
Processor, NP), speech processor and video processor etc.；Can also be digital signal processor, application-specific integrated circuit,
Field programmable gate array either other programmable logic device, discrete gate or transistor logic, discrete hardware components.
It can realize or perform disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor can be
Microprocessor or the processor 103 can also be any conventional processors etc..
Peripheral Interface 104 by various input/output devices (such as photographic device 105, laser reading devices 106, output fill
It puts 107) coupled to the processor 103 and memory 101.In some embodiments, Peripheral Interface 104, processor 103 with
And storage control 102 can be realized in one single chip.In some other example, they can be respectively by independent chip
It realizes.
Photographic device 105 is used to capture the video flowing of face to be identified, and photographic device 105 may be, but not limited to, camera shooting
Head, video camera etc. have the function of the picture pick-up device for acquiring video flowing.
Laser reading devices 106 are used to acquire the spatial information and geographical location information in the space at 100 place of robot, swash
It is that pulse lidar and CW lider etc. can obtain spatial information that optical acquisition device 106, which may be, but not limited to,
Or the laser collecting device in geographical location.
Output device 107 show that output device 107 can be with for that will be exported to the recognition result of face to be identified
It is, but be not limited to display screen, sound-producing device, flasher etc. to represent the information output device of recognition result.
First embodiment
Fig. 3 is please referred to, Fig. 3 shows the processing of the recognition of face provided in an embodiment of the present invention applied to robot 100
Method flow diagram.Processing method includes the following steps：
Step S101 obtains the initial information of face to be identified, wherein, initial information includes the face figure of face to be identified
Piece, picture collection time, picture collection place and face characteristic value.
In embodiments of the present invention, face to be identified is present in collected using the photographic device 105 of robot 100
In video flowing, the face picture of face to be identified is obtained by carrying out image analysis to the video flowing, for same person
Face can only obtain a most clearly face picture.The picture collection time can be with it is most clear described in the video flowing
The face picture corresponding time, picture collection place be robot 100 in the picture collection time in the space pre-established
Relative position in map.
Fig. 4 is please referred to, step S101 can include following sub-step：
Sub-step S1011, obtains the video flowing of photographic device acquisition, and carries out image analysis, obtains face to be identified
Face picture and picture collection time.
In embodiments of the present invention, obtain photographic device 105 to face to be identified carry out camera shooting collect include treat
It identifies the video flowing of face, detects the position of each face in each frame in video flowing, for same person, intercept one clearly
Face picture, as the face picture of a face to be identified, the picture collection time can be with the face figure of face to be identified
The acquisition time of the corresponding picture frame of piece.
Sub-step S1012 carries out face characteristic extraction to face picture, obtains the face characteristic value of face picture；
In embodiments of the present invention, face characteristic extraction is that the process of feature modeling is carried out to face picture, for example, face
The method of feature extraction can be according to the shape description of human face and they the distance between characteristic contributed to
The characteristic of face classification, characteristic component generally include Euclidean distance, curvature and angle between characteristic point etc..Face is by eye
Eyeball, nose, mouth, chin etc. are locally formed, and to these local and structural relation between them geometric descriptions, can be used as identification people
The important feature of face, these geometric descriptions can be used as face characteristic value.
Sub-step S1013 obtains the current location of robot, and according to current location generation picture collection place.
In embodiments of the present invention, the current spatial location of robot 100 is obtained, and determines current spatial location advance
Relative position in the space map of foundation, the relative position are picture collection place.
As a kind of embodiment, the current location of robot 100 is obtained, and according to current location generation picture collection
The method of point can include：
First, the current spatial location of the robot 100 is obtained, for example, being sent out by laser reading devices 106 to surrounding
Laser is penetrated, laser is encountered surrounding objects and returned, and can calculate the distance of robot 100 and periphery object, according to the distance
It can determine the current spatial position of robot 100.Secondly, according to current spatial location, in the space map pre-established
Determine the picture collection place of robot 100, wherein, space map is the sky acquired according to the laser reading devices 106
Between information establish, finally, determine that relative position of the current spatial location of robot 100 in the map of space is that picture is adopted
Collect place.
Initial information is sent to background server by step S102, so that background server foundation face characteristic value is to people
Face picture carries out feature recognition, and obtains face recognition result after identifying successfully, wherein, after face recognition result includes identification
Face picture, the picture collection time, picture collection place and face to be identified identity information.
In embodiments of the present invention, after obtaining initial information by step S101, initial information is sent to background service
End 300, background server 300 carry out feature recognition, and obtain people after identifying successfully according to face characteristic value to face picture
Face recognition result.
As a kind of embodiment, background server 300 carries out feature recognition according to face characteristic value to face picture, and
The method that face recognition result is obtained after identifying successfully can include：
Background server 300 and face database access connection, and face database is according to the field of 100 practical application of robot
Scape can be different database, the face database of face database, fugitive criminal such as the resident population in public security system,
Face database of examinee etc..
First, target face template is determined in the face database pre-established according to face characteristic value, wherein, people
Face database includes multiple face templates and multiple identity informations corresponding with each face template, the human face data pre-established
Library can with but be not limited in the face database server-side that connection is accessed with background server 300, such as face database also may be used
To be pre-stored in background server 300.
Secondly, the face picture of face to be identified is compared with the target face template, when face picture and mesh
When mark face template is consistent, face picture, picture collection time, picture collection place and and target person according to face to be identified
The corresponding identity information of face template, obtains face recognition result, and face template is to contain the face of face characteristic value to be abstracted mould
Type contains multiple face templates and multiple template characteristic value one-to-one with each face template and more in face database
A identity information a, for example, face template corresponds to a template characteristic value confirmation and an identity information.Using face characteristic value as
Search key filters out the target face characteristic value consistent with face characteristic value, when face figure from multiple template characteristic value
When piece is consistent with target face template, will identity information corresponding with target face template, face picture, the picture collection time and
Picture collection place is merged into face recognition result, wherein, the similarity of face picture and target face template reaches default threshold
During value, judgement face picture is consistent with target face template.
Finally, face recognition result is sent to robot 100 and terminal 400 by background server 300, and terminal 400 will
Face recognition result export, the mode of output can be but be not limited to display screen show, send out voice prompt and prompting glisten
Deng.
Step S103 receives the face recognition result of background server feedback and is exported.
In embodiments of the present invention, the mode of face recognition result output can be but be not limited to display screen and show, send out
Voice prompt and prompting flash of light etc..
In embodiments of the present invention, first, the initial information of face to be identified is obtained, wherein, initial information includes waiting to know
Face picture, picture collection time, picture collection place and the face characteristic value of others' face, robot 100 is to photographic device 105
The video flowing of acquisition carries out image analysis and obtains face picture and face picture progress face characteristic is extracted, and on the one hand keeps away
Exempt from a large amount of Video stream information being transmitted to background server 300, alleviate network pressure, another aspect robot 100 carries out
Feature extraction alleviates the pressure of background server 300, improves the speed of recognition of face；Secondly, initial information is sent to
Background server 300, so that background server 300 carries out feature recognition, and identifying according to face characteristic value to face picture
Face recognition result is obtained after success, wherein, face recognition result includes the face picture after identification, picture collection time, figure
The identity information of piece collecting location and face to be identified；Finally, the face recognition result that background server 300 is fed back is received to go forward side by side
Recognition result is sent to robot 100 and exported, can made in robot 100 by row output, 300 one side of background server
Neighbouring user can obtain face recognition result at once, and recognition result on the other hand is sent to terminal 400 exports, can
So that the user being not currently near robot 100 can also obtain face recognition result in time, make the applied field of robot 100
Scape is more flexible, user experience also more hommization.
Second embodiment
Fig. 5 is please referred to, Fig. 5 shows the block diagram of face identification device 200 provided in an embodiment of the present invention.Face
Identification device 200 is applied to robot 100, including initial information acquisition module 201, initial information sending module 202；Identification
As a result receiving module 203.
Initial information acquisition module 201, for obtaining the initial information of face to be identified, wherein, initial information includes treating
Identify face picture, picture collection time, picture collection place and the face characteristic value of face.
In the embodiment of the present invention, initial information acquisition module 201 can be used for performing step S101.
Fig. 6 is please referred to, Fig. 6 shows for the box of initial information acquisition module 201 in the face identification device 200 shown in Fig. 5
It is intended to.Initial information acquisition module 201 includes image analyzing unit 2011, feature extraction unit 2012 and place generation unit
2013。
Image analyzing unit 2011 for obtaining the video flowing of photographic device acquisition, and carries out image analysis, obtains waiting to know
The face picture of others' face and picture collection time.
In embodiments of the present invention, image analyzing unit 2011 can be used for performing sub-step S1011.
Feature extraction unit 2012, for carrying out face characteristic extraction to face picture, the face for obtaining face picture is special
Value indicative.
In embodiments of the present invention, feature extraction unit 2012 can be used for performing sub-step S1012.
Place generation unit 2013 for obtaining the current location of robot, and generates picture collection according to current location
Place.
In embodiments of the present invention, place generation unit 2013 can be used for performing sub-step S1013.
Initial information sending module 202, for initial information to be sent to background server, so that background server foundation
Face characteristic value carries out feature recognition to face picture, and obtains face recognition result after identifying successfully, wherein, recognition of face
As a result include the identity information of the face picture after identification, picture collection time, picture collection place and face to be identified.
In the embodiment of the present invention, initial information sending module 202 can be used for performing step S102.
Recognition result receiving module 203, for receiving the face recognition result of background server feedback and being exported.
In the embodiment of the present invention, recognition result receiving module 203 can be used for performing step S103.
The embodiment of the present invention further discloses a kind of computer readable storage medium, is stored thereon with computer program, described
The face identification method that present invention discloses is realized when computer program is performed by processor 103.
In conclusion a kind of face identification method provided by the invention, device, robot and storage medium, the method
Including：The initial information of face to be identified is obtained, wherein, initial information includes the face picture of face to be identified, picture collection
Time, picture collection place and face characteristic value；Initial information is sent to background server, so that background server is according to people
Face characteristic value carries out feature recognition to face picture, and obtains face recognition result after identifying successfully, wherein, recognition of face knot
Fruit includes the identity information of the face picture after identification, picture collection time, picture collection place and face to be identified；After reception
The face recognition result of platform server-side feedback is simultaneously exported.Compared with prior art, the present invention by robotic end to regarding
Frequency stream carries out image analysis and obtains face picture and face picture progress face characteristic is extracted, and on the one hand avoiding will be a large amount of
Video stream information be transmitted to background server, alleviate network pressure, on the other hand robotic end carry out feature extraction subtract
The light pressure of background server improves the speed of recognition of face, meanwhile, background server sends face recognition result
To robot and terminal, no matter whether user near robot, can receive face recognition result, make robot in time
Application scenarios are more flexible, user experience also more hommization.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and block diagram in attached drawing
Show the device of multiple embodiments according to the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.In this regard, each box in flow chart or block diagram can represent the one of a module, program segment or code
Part, a part for the module, program segment or code include one or more and are used to implement holding for defined logic function
Row instruction.It should also be noted that at some as in the realization method replaced, the function that is marked in box can also be to be different from
The sequence marked in attached drawing occurs.For example, two continuous boxes can essentially perform substantially in parallel, they are sometimes
It can perform in the opposite order, this is depended on the functions involved.It is it is also noted that every in block diagram and/or flow chart
The combination of a box and the box in block diagram and/or flow chart can use function or the dedicated base of action as defined in performing
It realizes or can be realized with the combination of specialized hardware and computer instruction in the system of hardware.
In addition, each function module in each embodiment of the present invention can integrate to form an independent portion
Point or modules individualism, can also two or more modules be integrated to form an independent part.
If the function is realized in the form of software function module and is independent product sale or in use, can be with
It is stored in a computer read/write memory medium.Based on such understanding, technical scheme of the present invention is substantially in other words
The part contribute to the prior art or the part of the technical solution can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, is used including some instructions so that a computer equipment (can be
People's computer, server or network equipment etc.) perform all or part of the steps of the method according to each embodiment of the present invention.
And aforementioned storage medium includes：USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic disc or CD.It needs
Illustrate, herein, relational terms such as first and second and the like be used merely to by an entity or operation with
Another entity or operation distinguish, and without necessarily requiring or implying between these entities or operation, there are any this realities
The relationship or sequence on border.Moreover, term " comprising ", "comprising" or its any other variant are intended to the packet of nonexcludability
Contain so that process, method, article or equipment including a series of elements not only include those elements, but also including
It other elements that are not explicitly listed or further includes as elements inherent to such a process, method, article, or device.
In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including the element
Process, method, also there are other identical elements in article or equipment.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, that is made any repaiies
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.It should be noted that：Similar label and letter exists
Similar terms are represented in following attached drawing, therefore, once being defined in a certain Xiang Yi attached drawing, are then not required in subsequent attached drawing
It is further defined and is explained.
Claims (10)
1. a kind of face identification method, which is characterized in that applied to robot, the robot and background server communication link
It connects, and the background server is communicated to connect with terminal, the method includes：
The initial information of face to be identified is obtained, wherein, the initial information includes the face picture of face to be identified, picture is adopted
Collect time, picture collection place and face characteristic value；
The initial information is sent to the background server, so that the background server is according to the face characteristic value pair
The face picture carries out feature recognition, and obtains face recognition result after identifying successfully, wherein, the face recognition result
Including after identification face picture, the picture collection time, picture collection place and the face to be identified identity information；
It receives the face recognition result of the background server feedback and is exported.
2. the method as described in claim 1, which is characterized in that the robot includes photographic device,
The step of initial information of the acquisition face to be identified, includes：
The video flowing of the photographic device acquisition is obtained, and carries out image analysis, obtains the face picture and figure of face to be identified
Piece acquisition time；
Face characteristic extraction is carried out to the face picture, obtains the face characteristic value of the face picture；
The current location of the robot is obtained, and the picture collection place is generated according to the current location.
3. method as claimed in claim 2, which is characterized in that the robot further includes laser reading devices；
The current location for obtaining the robot, and the step of generate the picture collection place according to the current location
Including：
Obtain the current spatial location of the robot；
According to the current spatial location, the picture collection of the robot is determined in the space map pre-established
Point, wherein, the space map is that the spatial information acquired according to the laser reading devices is established.
4. the method as described in claim 1, which is characterized in that the background server is according to the face characteristic value to described
Face picture carries out feature recognition, and includes obtain face recognition result after identifying successfully the step of：
Target face template is determined in the face database pre-established according to the face characteristic value, wherein, the people
Face database includes multiple face templates and multiple identity informations corresponding with each face template；
The face picture of the face to be identified and the target face template are compared, when the face picture with it is described
When target face template is consistent, according to the face picture of the face to be identified, the picture collection time, picture collection place and with
The corresponding identity information of the target face template, obtains face recognition result；
The face recognition result is sent to the robot and the terminal by the background server.
5. method as claimed in claim 4, which is characterized in that the face database further includes multiple template characteristic value, institute
Multiple template characteristic value is stated to correspond with multiple face templates；
It is described to determine to include the step of target face template in the face database pre-established according to the face characteristic value：
Using the face characteristic value as search key, filtered out from the multiple template characteristic value confirmation and the face characteristic
It is worth consistent target face characteristic value.
6. a kind of face identification device, which is characterized in that applied to robot, the robot and background server communication link
It connects, described device includes：
Initial information acquisition module, for obtaining the initial information of face to be identified, wherein, the initial information includes to be identified
Face picture, picture collection time, picture collection place and the face characteristic value of face；
Initial information sending module, for the initial information to be sent to the background server, so that the background service
End carries out feature recognition, and recognition of face knot is obtained after identifying successfully according to the face characteristic value to the face picture
Fruit, wherein, the face recognition result includes the face picture after identification, picture collection time, picture collection place and described
The identity information of face to be identified；
Recognition result receiving module, for receiving the face recognition result of the background server feedback and being exported.
7. device as claimed in claim 6, which is characterized in that the robot includes photographic device, and the initial information obtains
Modulus block includes：
Image analyzing unit for obtaining the video flowing of the photographic device acquisition, and carries out image analysis, obtains people to be identified
The face picture of face and picture collection time；
Feature extraction unit, for carrying out face characteristic extraction to the face picture, the face for obtaining the face picture is special
Value indicative；
Place generation unit for obtaining the current location of the robot, and generates the picture according to the current location
Collecting location.
8. device as claimed in claim 7, which is characterized in that the robot further includes laser reading devices, the place
Generation unit is specifically used for：
Obtain the current spatial location of the robot；
According to the current spatial location, the picture collection of the robot is determined in the space map pre-established
Point, wherein, the space map is that the spatial information acquired according to the laser reading devices is established.
9. a kind of robot, which is characterized in that the robot includes：
One or more processors；
Memory, for storing one or more programs, when one or more of programs are by one or more of processors
During execution so that one or more of processors realize the method as described in any one of claim 1-5.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program quilt
The method as described in any one of claim 1-5 is realized when processor performs.
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| CN201810103742.5A CN108197608A (en) | 2018-02-01 | 2018-02-01 | Face identification method, device, robot and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109376016A (en) * | 2018-10-29 | 2019-02-22 | 安徽智传科技有限公司 | A kind of the recognition of face efficiency improvement method and system of multithreading |
| WO2020037896A1 (en) * | 2018-08-21 | 2020-02-27 | 平安科技（深圳）有限公司 | Facial feature value extraction method and device, computer apparatus, and storage medium |
| CN111353357A (en) * | 2019-01-31 | 2020-06-30 | 杭州海康威视数字技术股份有限公司 | Face modeling system, method and device |
| CN112036242A (en) * | 2020-07-28 | 2020-12-04 | 重庆锐云科技有限公司 | Face picture acquisition method and device, computer equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794458A (en) * | 2015-05-07 | 2015-07-22 | 北京丰华联合科技有限公司 | Fuzzy video person identifying method |
| CN107067504A (en) * | 2016-12-25 | 2017-08-18 | 北京中海投资管理有限公司 | A kind of recognition of face safety-protection system and a suspect's detection and method for early warning |
| CN107278369A (en) * | 2016-12-26 | 2017-10-20 | 深圳前海达闼云端智能科技有限公司 | Method, device and the communication system of people finder |
| CN107578024A (en) * | 2017-09-15 | 2018-01-12 | 赵立峰 | A kind of face identification system |
-
2018
- 2018-02-01 CN CN201810103742.5A patent/CN108197608A/en not_active Withdrawn
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794458A (en) * | 2015-05-07 | 2015-07-22 | 北京丰华联合科技有限公司 | Fuzzy video person identifying method |
| CN107067504A (en) * | 2016-12-25 | 2017-08-18 | 北京中海投资管理有限公司 | A kind of recognition of face safety-protection system and a suspect's detection and method for early warning |
| CN107278369A (en) * | 2016-12-26 | 2017-10-20 | 深圳前海达闼云端智能科技有限公司 | Method, device and the communication system of people finder |
| CN107578024A (en) * | 2017-09-15 | 2018-01-12 | 赵立峰 | A kind of face identification system |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020037896A1 (en) * | 2018-08-21 | 2020-02-27 | 平安科技（深圳）有限公司 | Facial feature value extraction method and device, computer apparatus, and storage medium |
| CN109376016A (en) * | 2018-10-29 | 2019-02-22 | 安徽智传科技有限公司 | A kind of the recognition of face efficiency improvement method and system of multithreading |
| CN111353357A (en) * | 2019-01-31 | 2020-06-30 | 杭州海康威视数字技术股份有限公司 | Face modeling system, method and device |
| CN111353357B (en) * | 2019-01-31 | 2023-06-30 | 杭州海康威视数字技术股份有限公司 | Face modeling system, method and device |
| CN112036242A (en) * | 2020-07-28 | 2020-12-04 | 重庆锐云科技有限公司 | Face picture acquisition method and device, computer equipment and storage medium |
| CN112036242B (en) * | 2020-07-28 | 2023-07-21 | 重庆锐云科技有限公司 | Face picture acquisition method and device, computer equipment and storage medium |
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