CN108875778A - Face cluster method, apparatus, system and storage medium - Google Patents
Face cluster method, apparatus, system and storage medium Download PDFInfo
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- CN108875778A CN108875778A CN201810421711.4A CN201810421711A CN108875778A CN 108875778 A CN108875778 A CN 108875778A CN 201810421711 A CN201810421711 A CN 201810421711A CN 108875778 A CN108875778 A CN 108875778A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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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
Abstract
The present invention provides a kind of face cluster method, apparatus, system and storage medium, the method includes：Determine that cluster mode, the cluster mode describe to obtain a how many images face clusters of progress from the image to be clustered every time based on the quantity of image to be clustered；And cluster mode based on the determination obtains the image progress face cluster of respective numbers from the image to be clustered every time, until the image to be clustered all complete by cluster.The quantity that face cluster method, apparatus, system and storage medium according to an embodiment of the present invention are primarily based on image to be clustered determines cluster mode, it is then based on the cluster mode determined and treats cluster image progress face cluster in batches appropriate, customized face cluster strategy may be implemented, realize flexible and efficient face cluster.
Description
Technical field
The present invention relates to face cluster technical field, relates more specifically to a kind of face cluster method, apparatus, system and deposit
Storage media.
Background technique
Face cluster, such as mobile phone photo album can be used under many scenes at present, it can be same by the method for face cluster
The facial image of one people gathers inside a group.
Existing face cluster method is typically directly directed to the image for needing to cluster and carries out face cluster, is lack of pertinence
Face cluster strategy, so that the mode of face cluster is not flexible, accuracy is low, efficiency is also low.In addition, existing face cluster
Method goes to compare after orienting face in such a way that full dose compares characteristic value, further reduced cluster efficiency.
Summary of the invention
The invention proposes a kind of face cluster scheme, the quantity for being primarily based on image to be clustered determines cluster mode,
It is then based on the cluster mode determined and treats cluster image progress face cluster in batches appropriate, customized people may be implemented
Face cluster strategy, realizes flexible and efficient face cluster.Face cluster scheme proposed by the present invention is briefly described below, more
Details will be described in a specific embodiment in subsequent combination attached drawing.
According to an aspect of the present invention, a kind of face cluster method is provided, the method includes：Based on image to be clustered
Quantity determines that cluster mode, the cluster mode describe to obtain how many images progress from the image to be clustered every time once
Face cluster；And cluster mode based on the determination every time from the image to be clustered obtain respective numbers image into
Row face cluster, until the image to be clustered all complete by cluster.
In one embodiment, in addition to face cluster for the first time, each face cluster combines previous face cluster
Result carry out, wherein：The result of previous face cluster includes the image group of cluster formed after previous face cluster,
It is described to have clustered all images that image group includes each cluster in each cluster；Combination to the result of previous face cluster
Including in conjunction with the subset for having clustered image group, each cluster in the subset includes an image.
In one embodiment, each cluster in the subset includes face top-quality one in the cluster
Image.
In one embodiment, the face cluster is based on the face phase obtained to the image progress Face datection of acquisition
Information is closed, the face relevant information includes the face mark for representing different faces and the face characteristic value of each face.
In one embodiment, include to the combination of the subset for having clustered image group：The image obtained based on this
Face characteristic value to it is described this obtain image carry out face cluster, obtain this acquisition image inside cluster knot
Fruit；By the face characteristic value and the subset of the top-quality representative image of face of each cluster in the internal cluster result
The face characteristic value of middle image is matched；If not being focused to find out matching result from the son, the representative image is made
It is saved in the subset newly to cluster；And if being focused to find out matching result from the son, it is determined that the representative image
Face quality whether be higher than the face quality of found matching result, if it is, replacing institute using the representative image
Matching result is stated to save in the subset for face cluster next time.
In one embodiment, the judge index of the face quality includes at least one of the following：Face fuzziness,
Facial angle, face size and face location, the judge index are based on obtaining when carrying out Face datection to the image of acquisition
Face relevant information and obtain.
In one embodiment, the quantity based on image to be clustered determines that cluster mode includes：If described to poly-
The quantity of class image is less than or equal to first threshold, it is determined that the image for obtaining the first quantity every time carries out a face cluster；Such as
The quantity of image to be clustered described in fruit is greater than the first threshold and is less than or equal to second threshold, it is determined that obtains the second number every time
The image of amount carries out a face cluster；And if the quantity of the image to be clustered is greater than the second threshold, it is determined that
The image for obtaining third quantity every time carries out a face cluster.
According to a further aspect of the invention, a kind of face cluster device is provided, described device includes：Determining module is used for
Determine that cluster mode, the cluster mode describe to obtain from the image to be clustered every time more based on the quantity of image to be clustered
Image is opened less carries out a face cluster；And cluster module, the cluster mode determined for module based on the determination are each
The image that respective numbers are obtained from the image to be clustered carries out face cluster, until the image to be clustered has all clustered
At.
In one embodiment, the cluster module is further used for：In addition to face cluster for the first time, each face is poly-
Class is carried out in conjunction with the result of previous face cluster, wherein：The result of previous face cluster includes that previous face is poly-
The image group of cluster formed after class, it is described to have clustered all images that image group includes each cluster in each cluster；To preceding
The combination of the result of secondary face cluster include in conjunction with the subset for having clustered image group, it is described each poly- in the subset
Class includes an image.
In one embodiment, each cluster in the subset includes face top-quality one in the cluster
Image.
In one embodiment, the face cluster that the cluster module is implemented is based on the image progress people to acquisition
The face relevant information that face detects, the face relevant information include represent different faces face mark and everyone
The face characteristic value of face.
In one embodiment, the cluster module includes to the combination of the subset for having clustered image group：Based on this
The face characteristic value of the image of secondary acquisition carries out face cluster to the image that this is obtained, and obtains the image of this acquisition
Internal cluster result；By the face characteristic value of the top-quality representative image of face of each cluster in the internal cluster result
It is matched with the face characteristic value of image in the subset；It, will be described if not being focused to find out matching result from the son
Representative image saves in the subset as new cluster；And if being focused to find out matching result from the son, it is determined that institute
Whether the face quality for stating representative image is higher than the face quality of found matching result, if it is, using the representative
Image is replaced the matching result and is saved in the subset for face cluster next time.
In one embodiment, the judge index of the face quality includes at least one of the following：Face fuzziness,
Facial angle, face size and face location, the judge index are based on obtaining when carrying out Face datection to the image of acquisition
Face relevant information and obtain.
In one embodiment, the determining module determines that cluster mode includes based on the quantity of image to be clustered：If
The quantity of the image to be clustered is less than or equal to first threshold, it is determined that the image for obtaining the first quantity every time carries out a face
Cluster；If the quantity of the image to be clustered is greater than the first threshold and is less than or equal to second threshold, it is determined that obtain every time
The image of the second quantity is taken to carry out a face cluster；And if the quantity of the image to be clustered is greater than second threshold
Value, it is determined that the image for obtaining third quantity every time carries out a face cluster.
Another aspect according to the present invention provides a kind of face cluster system, and the system comprises storage devices and processing
Device is stored with the computer program run by the processor on the storage device, and the computer program is by the place
Face cluster method described in any of the above embodiments is executed when reason device operation.
According to a further aspect of the present invention, a kind of storage medium is provided, is stored with computer program on the storage medium,
The computer program executes face cluster method described in any of the above embodiments at runtime.
Face cluster method, apparatus, system and storage medium according to an embodiment of the present invention are primarily based on image to be clustered
Quantity determine cluster mode, be then based on the cluster mode determined and treat cluster image to carry out face in batches appropriate poly-
Class may be implemented customized face cluster strategy, realize flexible and efficient face cluster.
Detailed description of the invention
The embodiment of the present invention is described in more detail in conjunction with the accompanying drawings, the above and other purposes of the present invention,
Feature and advantage will be apparent.Attached drawing is used to provide to further understand the embodiment of the present invention, and constitutes explanation
A part of book, is used to explain the present invention together with the embodiment of the present invention, is not construed as limiting the invention.In the accompanying drawings,
Identical reference label typically represents same parts or step.
Fig. 1 shows showing for realizing face cluster method, apparatus according to an embodiment of the present invention, system and storage medium
The schematic block diagram of example electronic equipment；
Fig. 2 shows the schematic flow charts of face cluster method according to an embodiment of the present invention；
Fig. 3 shows the schematic block diagram of face cluster device according to an embodiment of the present invention；And
Fig. 4 shows the schematic block diagram of face cluster system according to an embodiment of the present invention.
Specific embodiment
In order to enable the object, technical solutions and advantages of the present invention become apparent, root is described in detail below with reference to accompanying drawings
According to example embodiments of the present invention.Obviously, described embodiment is only a part of the embodiments of the present invention, rather than this hair
Bright whole embodiments, it should be appreciated that the present invention is not limited by example embodiment described herein.Based on described in the present invention
The embodiment of the present invention, those skilled in the art's obtained all other embodiment in the case where not making the creative labor
It should all fall under the scope of the present invention.
Firstly, describing face cluster method, apparatus, system and storage for realizing the embodiment of the present invention referring to Fig.1
The exemplary electronic device 100 of medium.
As shown in Figure 1, electronic equipment 100 include one or more processors 102, it is one or more storage device 104, defeated
Enter device 106 and output device 108, these components (are not shown by the bindiny mechanism of bus system 110 and/or other forms
It interconnects out).It should be noted that the component and structure of electronic equipment 100 shown in FIG. 1 are illustrative, and not restrictive, root
According to needs, the electronic equipment also can have other assemblies and structure.
The processor 102 can be central processing unit (CPU) or have data-handling capacity and/or instruction execution
The processing unit of the other forms of ability, and the other components that can control in the electronic equipment 100 are desired to execute
Function.
The storage device 104 may include one or more computer program products, and the computer program product can
To include various forms of computer readable storage mediums, such as volatile memory and/or nonvolatile memory.It is described easy
The property lost memory for example may include random access memory (RAM) and/or cache memory (cache) etc..It is described non-
Volatile memory for example may include read-only memory (ROM), hard disk, flash memory etc..In the computer readable storage medium
On can store one or more computer program instructions, processor 102 can run described program instruction, to realize hereafter institute
The client functionality (realized by processor) in the embodiment of the present invention stated and/or other desired functions.In the meter
Can also store various application programs and various data in calculation machine readable storage medium storing program for executing, for example, the application program use and/or
The various data etc. generated.
The input unit 106 can be the device that user is used to input instruction, and may include keyboard, mouse, wheat
One or more of gram wind and touch screen etc..
The output device 108 can export various information (such as image or sound) to external (such as user), and
It may include one or more of display, loudspeaker etc..
Illustratively, the exemplary electronic device for realizing face cluster method and apparatus according to an embodiment of the present invention can
To be implemented such as smart phone, tablet computer etc. mobile terminal.
In the following, face cluster method 200 according to an embodiment of the present invention will be described with reference to Fig. 2.As shown in Fig. 2, face is poly-
Class method 200 may include steps of：
In step S210, determine that cluster mode, the cluster mode describe every time from institute based on the quantity of image to be clustered
It states and obtains a how many images face clusters of progress in image to be clustered.
In an embodiment of the present invention, it can according to need the customized corresponding cluster strategy of magnitude of the image of cluster,
To improve the efficiency of face cluster.Wherein, cluster strategy can be understood as the image to be clustered for different number using different
Cluster mode, cluster mode describes to obtain how many images from image to be clustered every time and carries out a face clusters.
Illustratively, if the quantity of the image to be clustered is less than or equal to first threshold, it is determined that obtain first every time
The image of quantity carries out a face cluster；If the quantity of the image to be clustered is greater than the first threshold and is less than or equal to
Second threshold, it is determined that the image for obtaining the second quantity every time carries out a face cluster；And the if image to be clustered
Quantity be greater than the second threshold, it is determined that every time obtain third quantity image carry out a face cluster.
For example, can obtain 1 image every time if there is 10 images to be clustered and carry out a face cluster；Such as
There are 100 images to be clustered for fruit, can obtain 4 images every time and carry out a face cluster；If there is 1000 to
The image of cluster can obtain 10 images every time and carry out a face cluster；It, can if there is 2000 images to be clustered
A face cluster is carried out to obtain 50 images every time；It is such.
In the examples described above, when there are 100 images to be clustered, if according to existing face cluster method,
This 100 images to be clustered need to be carried out 1:1 comparison, efficiency are very low.And according to an embodiment of the invention, when there are 100
When image to be clustered, by the way of face cluster of every 4 progress, this 100 images are divided into 25 groups, it is subsequent
Group only needs to compare the one or several sheets image in the group of front i.e. without comparing with all images in the group of front pair
Can, because the cluster result of the group of front has generated, if not mutually not at one kind, the quantity for the image for needing to compare
It necessarily reduces, because need to only compare one for same cluster.Therefore, by image to be clustered according to certain cluster
Face cluster, which is conducted batch-wise, in mode can effectively improve the efficiency of face cluster.
Based on the cluster mode determined, cluster image can be treated and carry out face cluster.It continues with reference to Fig. 2, retouches
State the subsequent step of face cluster method 200.
In step S220, cluster mode based on the determination obtains respective numbers from the image to be clustered every time
Image carries out face cluster, until the image to be clustered all complete by cluster.
Based on cluster mode identified in step S210, respective numbers can be obtained from image to be clustered every time
Image carries out face cluster.Herein, respective numbers refer to determined by every time should be from image to be clustered described in cluster mode
The amount of images of middle acquisition.For example, needing to obtain 10 images from image to be clustered every time if determined in step S210
A face cluster is carried out, then can obtain 10 images every time in step S220 and carry out Face datection, obtain this 10 figures
The face relevant information of picture, for carrying out face cluster.
Illustratively, the face relevant information may include the face mark for representing different faces and each face
Face characteristic value.Wherein, face mark can be understood as face ID, in an image, if there is multiple faces, then each
The ID of face is different.The face characteristic value of each face then can be used for comparison matching when face cluster, hereinafter will be detailed
Carefully it is described to.
In an embodiment of the present invention, cluster image progress face is being treated by several times in batches according to identified cluster mode
When cluster, in addition to face cluster for the first time, each face cluster can be carried out in conjunction with the result of previous face cluster,
Wherein：The result of previous face cluster includes the image group of cluster formed after previous face cluster, the dendrogram
As group includes all images of each cluster in each cluster；Combination to the result of previous face cluster includes in conjunction with described
The subset of image group is clustered, each cluster in the subset includes an image.Wherein, image has been clustered to described
The combination of subset of group may include：Face characteristic value based on this image obtained carries out the image that this is obtained
Face cluster obtains the inside cluster result of the image of this acquisition；By the face of each cluster in the internal cluster result
The face characteristic value of top-quality representative image is matched with the face characteristic value of image in the subset；If not from institute
It states son and is focused to find out matching result, then saved the representative image as new cluster in the subset；And if from institute
It states son and is focused to find out matching result, it is determined that whether the face quality of the representative image is higher than the people of found matching result
Face quality saves in the subset if it is, the representative image is used to replace the matching result for next time
Face cluster.
For example, image group can have been clustered for the result formation of face cluster for the first time, it is described at this time to have clustered image group
Show the cluster result of a collection of image obtained for the first time.For example, the cluster result of 10 images obtained for the first time is this 10
Having 6 images in image includes personage A, and having 3 images includes personage B, includes personage C there are also an image, then face for the first time
The image group of cluster formed after cluster may include 3 clusters, respectively represent personage A, B and C, wherein the cluster packet of personage A
6 images are included, the cluster of personage B includes 3 images, and the cluster of personage C includes 1 image.Then, it can be based on having clustered image
Group forms the subset for having clustered image group, to be used in combination when being used for second of face cluster.Illustratively, image group has been clustered
Subset may include 3 cluster, respectively represent personage A, B and C, wherein the cluster of personage A, the cluster of personage B and personage C
Cluster respectively include that (such as the image of representative figure A is the face selected from above-mentioned 6 images in subset for 1 image
Top-quality one, the image of representative figure B is the face top-quality one selected from above-mentioned 3 images in subset
).That is, in secondary face cluster, in the event of personage A, then can by the face characteristic value of personage A with
Face characteristic value in the subset of cluster image group about 1 image of face A, which is compared, can be obtained matching result, and nothing
6 need to be compared, to be greatly improved efficiency.In addition, in secondary face cluster, it can be first in 10 obtained for the second time
It opens and is clustered inside image, for example occur personage A in multiple images, then the top-quality image of face can be made
For representative image with clustered image group subset in image compare, to can further improve cluster efficiency.Obtaining
After secondary face cluster result, can update it is above-mentioned clustered image group, that is, by secondary face cluster result
Being reflected in and having clustered in image group (may be to be added to new image for certain already present cluster, it is also possible to be added to new
Cluster included by image)；Image group has been clustered based on updated, can have been formed as previously described after the update
The subset for clustering image group, for face cluster next time.Similarly, in third time face cluster, and with second
The process of secondary face cluster is similar.And so on, until all images to be clustered complete cluster.
In addition, if not finding matching result in the previous subset for having clustered image group, show to have occurred new
Personage, then can (include top-quality the one of the new persona i.e. in this image obtained by the representative image of the new persona
Image just uses this if only 1 includes the image of the new persona) it is stored in as new cluster and has clustered image
In the subset of group., whereas if matching result is had found in the previous subset for having clustered image group, it is poly- before showing
Include this person in class result, then can determine that the representative image of the personage (includes the quality of the personage in this image obtained
As soon as best image, if only 1 include the personage image if using this) face quality whether compare subset
In the face better quality of matching result found, if it is, using the matching image in representative image replacement subset, instead
Then without the replacement operation.Based on aforesaid operations, the representative figure respectively clustered included in the subset of image group has been clustered
The quality of picture is become better and better, and will play huge facilitation for the promotion of the accuracy of subsequent face cluster.
In the above example, the judge index of face quality may include at least one of the following：Face fuzziness,
Facial angle, face size and face location.These index item can be used alone or in combination.For example, face matter above-mentioned
Amount can preferably indicate that face fuzziness is minimum.For another example, face above-mentioned is best in quality can indicate facial angle in predetermined model
It encloses and face fuzziness is minimum etc..These judge index can be based on the people of acquisition when carrying out Face datection to the image of acquisition
Face relevant information and obtain, such as simultaneously must when Face datection obtains face relevant information in the image above-mentioned to acquisition
To these judge index, these judge index also are included in the face relevant information that Face datection obtains.
In the above example, the case where being likely to occur the not image of face when carrying out face cluster, at this time by this
The image of sample is abandoned or is put into special no face image set.For having clustered image group and having clustered image group
Subset, can also be stored using two structure body units.For example, cache (cache) structure body unit can be used
Image group has been clustered to store, that is, has stored all images comprising face, or only store the face information of these images；It can adopt
The subset for having clustered image group is stored with block (block) structure body unit, that is, stores an only facial image for each cluster, or
Only store the face information of these images.The image that these structure body units are stored can be expressed as include various variables knot
Structure body.For the structural body other than it may include face above-mentioned mark, face characteristic value the two variables, can also include should
The Clustering number of certain face in image, the coordinate of face, face fuzziness, the angle of face, the reason of not being clustered etc..Its
In, the packet number not being clustered can be indicated using 0, the image not being clustered for some reason can also be with aforementioned acquisition
Image participates in face cluster together.
In addition, during stating face cluster method on the implementation, if there is also previous face cluster as a result, can incite somebody to action
It is read into memory to participate in face cluster from physical storage medium；In addition, the face correlation of the image obtained every time is believed
The information for the image cease, not being clustered can be stored in memory, convenient for improving arithmetic speed.
Based on above description, face cluster method according to an embodiment of the present invention is primarily based on the quantity of image to be clustered
It determines cluster mode, is then based on the cluster mode determined and treats cluster image and carry out face cluster in batches appropriate, it can be with
It realizes customized face cluster strategy, realizes flexible and efficient face cluster.
Face cluster method according to an embodiment of the present invention is described above exemplarily.Illustratively, according to the present invention
The face cluster method of embodiment can with memory and processor unit or system in realize.
In addition, face cluster method according to an embodiment of the present invention be deployed to can be convenient smart phone, tablet computer,
In the mobile devices such as personal computer.Alternatively, face cluster method according to an embodiment of the present invention can also be deployed in service
Device end (or cloud).Alternatively, face cluster method according to an embodiment of the present invention can also be deployed in server end with being distributed
At (or cloud) and personal terminal.
The face cluster device of another aspect of the present invention offer is described below with reference to Fig. 3.Fig. 3 shows real according to the present invention
Apply the schematic block diagram of the face cluster device 300 of example.
As shown in figure 3, face cluster device 300 according to an embodiment of the present invention includes determining module 310 and cluster module
320.The modules can execute each step/function of the face cluster method above in conjunction with Fig. 2 description respectively.Below
Only the major function of each module of face cluster device 300 is described, and omits the detail content having been described above.
Determining module 310 is used to determine that cluster mode, the cluster mode describe every time based on the quantity of image to be clustered
How many images are obtained from the image to be clustered carries out a face cluster.Cluster module 320 is for based on the determination
The image that the cluster mode that module determines obtains respective numbers from the image to be clustered every time carries out face cluster, Zhi Daosuo
Stating image to be clustered, all cluster is completed.Determining module 310 and cluster module 320 can be in electronic equipments as shown in Figure 1
102 Running storage device 104 of processor in the program instruction that stores realize.
In an embodiment of the present invention, it can according to need the customized corresponding cluster strategy of magnitude of the image of cluster,
To improve the efficiency of face cluster.Wherein, cluster strategy can be understood as the image to be clustered for different number using different
Cluster mode, cluster mode describes to obtain how many images from image to be clustered every time and carries out a face clusters.
Illustratively, if the quantity of the image to be clustered is less than or equal to first threshold, it is determined that module 310 determines every
The secondary image for obtaining the first quantity carries out a face cluster；If the quantity of the image to be clustered is greater than the first threshold
And it is less than or equal to second threshold, it is determined that module 310 determines that the image for obtaining the second quantity every time carries out a face cluster；With
And if the quantity of the image to be clustered is greater than the second threshold, it is determined that module 310 determines obtains third quantity every time
Image carry out a face cluster.
For example, determining module 310 can determine that cluster module 320 obtains 1 every time if there is 10 images to be clustered
It opens image and carries out a face cluster；If there is 100 images to be clustered, determining module 310 can determine cluster module
320 obtain 4 images every time carries out a face cluster；If there is 1000 images to be clustered, determining module 310 can be with
Determine that cluster module 320 obtains 10 images every time and carries out a face cluster；If there is 2000 images to be clustered, really
Cover half block 310 can determine that cluster module 320 obtains 50 images every time and carries out a face cluster；It is such.
In the examples described above, when there are 100 images to be clustered, if according to existing face cluster mode,
This 100 images to be clustered need to be carried out 1 by cluster module 320:1 comparison, efficiency are very low.And implementation according to the present invention
Example, when there are 100 images to be clustered, cluster module 320 is by the way of face cluster of every 4 progress, by this
100 images are divided into 25 groups, and subsequent group, without comparing with all images in the group of front pair, and only needs to compare front
One or several sheets image in group, because the cluster result of the group of front has generated, if not mutually not at one kind
Words, the quantity for the image for needing to compare necessarily are reduced, because need to only compare one for same cluster.It therefore, will be to
Face cluster, which is conducted batch-wise, according to certain cluster mode in the image of cluster can effectively improve the efficiency of face cluster.
Mode is clustered based on determined by determining module 310, cluster module 320 can be obtained from image to be clustered every time
The image of respective numbers carries out face cluster.Herein, respective numbers refer to described by cluster mode determined by determining module 310
The amount of images that should be obtained from image to be clustered every time of cluster module 320.Such as, if it is determined that module 310 determines each
It needs to obtain 10 images from image to be clustered and carries out a face cluster, then cluster module 320 can obtain 10 every time
Image carries out Face datection, obtains the face relevant information of this 10 images, for carrying out face cluster.
Illustratively, the face relevant information may include the face mark for representing different faces and each face
Face characteristic value.Wherein, face mark can be understood as face ID, in an image, if there is multiple faces, then each
The ID of face is different.The face characteristic value of each face then can be used for comparison matching when face cluster, hereinafter will be detailed
Carefully it is described to.
In an embodiment of the present invention, mode is clustered according to determined by determining module 310 in cluster module 320 to divide in batches
It is secondary treat cluster image carry out face cluster when, in addition to face cluster for the first time, each face cluster can combine it is previous
The result of face cluster carry out, wherein：The result of previous face cluster includes being formed after previous face cluster
Image group is clustered, it is described to have clustered all images that image group includes each cluster in each cluster；To previous face cluster
The combination of result include each cluster in conjunction with the subset for having clustered image group, in the subset include a figure
Picture.Wherein, may include to the combination of the subset for having clustered image group：Face characteristic value based on this image obtained
Face cluster is carried out to the image that this is obtained, obtains the inside cluster result of the image of this acquisition；By the inside
The face of the face characteristic value of the top-quality representative image of the face of each cluster and image in the subset in cluster result
Characteristic value is matched；If not being focused to find out matching result from the son, saved the representative image as new cluster
In the subset；And if being focused to find out matching result from the son, it is determined that the face quality of the representative image is
The no face quality higher than found matching result is protected if it is, replacing the matching result using the representative image
It deposits in the subset for face cluster next time.Cluster mould can be understood in conjunction with the example above with reference to Fig. 2 description
The concrete operations of block 320, for sake of simplicity, details are not described herein again.
In the above-described embodiment, the judge index of face quality may include at least one of the following：Face is fuzzy
Degree, facial angle, face size and face location.These index item can be used alone or in combination.For example, face above-mentioned
It is best in quality to indicate that face fuzziness is minimum.For another example, face above-mentioned is best in quality can indicate facial angle predetermined
Range and face fuzziness are minimum etc..These judge index can be based on acquisition when carrying out Face datection to the image of acquisition
Face relevant information and obtain, such as carried out when Face datection obtains face relevant information simultaneously in the image above-mentioned to acquisition
These judge index are obtained, these judge index also are included in the face relevant information that Face datection obtains.
Based on above description, face cluster device according to an embodiment of the present invention is primarily based on the quantity of image to be clustered
It determines cluster mode, is then based on the cluster mode determined and treats cluster image and carry out face cluster in batches appropriate, it can be with
It realizes customized face cluster strategy, realizes flexible and efficient face cluster.
Fig. 4 shows the schematic block diagram of face cluster system 400 according to an embodiment of the present invention.Face cluster system
400 include storage device 410 and processor 420.
Wherein, the storage of storage device 410 is for realizing the corresponding step in face cluster method according to an embodiment of the present invention
Rapid program code.Program code of the processor 420 for being stored in Running storage device 410, it is real according to the present invention to execute
The corresponding steps of the face cluster method of example are applied, and for realizing the phase in face cluster device according to an embodiment of the present invention
Answer module.
In one embodiment, when said program code is run by processor 420 face cluster system 400 is executed
Following steps：Determine that cluster mode, the cluster mode describe every time from the figure to be clustered based on the quantity of image to be clustered
How many images are obtained as in carries out a face cluster；And cluster mode based on the determination is every time from described to be clustered
The image that respective numbers are obtained in image carries out face cluster, until the image to be clustered all complete by cluster.
In one embodiment, when said program code is run by processor 420 face cluster system 400 is executed
Face cluster in addition to face cluster for the first time, each face cluster is carried out in conjunction with the result of previous face cluster,
Wherein：The result of previous face cluster includes the image group of cluster formed after previous face cluster, the dendrogram
As group includes all images of each cluster in each cluster；Combination to the result of previous face cluster includes in conjunction with described
The subset of image group is clustered, each cluster in the subset includes an image.
In one embodiment, each cluster in the subset includes face top-quality one in the cluster
Image.
In one embodiment, when said program code is run by processor 420 face cluster system 400 is executed
The face cluster be based on carrying out the face relevant information that Face datection obtains, the face correlation letter to the image of acquisition
Breath includes the face mark for representing different faces and the face characteristic value of each face.
In one embodiment, when said program code is run by processor 420 face cluster system 400 is executed
Include to the combination of the subset for having clustered image group：Based on this obtain image face characteristic value to it is described this
The image of acquisition carries out face cluster, obtains the inside cluster result of the image of this acquisition；It will be in the internal cluster result
The face characteristic value of image carries out in the face characteristic value and the subset of the top-quality representative image of the face of each cluster
Matching；If not being focused to find out matching result from the son, the subset is stored in using the representative image as new cluster
In；And if being focused to find out matching result from the son, it is determined that whether the face quality of the representative image, which is higher than, is looked for
The face quality of the matching result arrived, if it is, replacing the matching result using the representative image is stored in the son
It concentrates for face cluster next time.
In one embodiment, the judge index of the face quality includes at least one of the following：Face fuzziness,
Facial angle, face size and face location, the judge index are based on obtaining when carrying out Face datection to the image of acquisition
Face relevant information and obtain.
In one embodiment, when said program code is run by processor 420 face cluster system 400 is executed
The quantity based on image to be clustered determine that cluster mode includes：If the quantity of the image to be clustered is less than or equal to the
One threshold value, it is determined that the image for obtaining the first quantity every time carries out a face cluster；If the quantity of the image to be clustered
Greater than the first threshold and it is less than or equal to second threshold, it is determined that the image for obtaining the second quantity every time carries out a face and gathers
Class；And if the quantity of the image to be clustered is greater than the second threshold, it is determined that obtain the image of third quantity every time
Carry out a face cluster.
In addition, according to embodiments of the present invention, additionally providing a kind of storage medium, storing program on said storage
Instruction, when described program instruction is run by computer or processor for executing the face cluster method of the embodiment of the present invention
Corresponding steps, and for realizing the corresponding module in face cluster device according to an embodiment of the present invention.The storage medium
It such as may include the storage card of smart phone, the storage unit of tablet computer, the hard disk of personal computer, read-only memory
(ROM), Erasable Programmable Read Only Memory EPROM (EPROM), portable compact disc read-only memory (CD-ROM), USB storage,
Or any combination of above-mentioned storage medium.The computer readable storage medium can be one or more computer-readable deposit
Any combination of storage media.
In one embodiment, the computer program instructions may be implemented real according to the present invention when being run by computer
Each functional module of the face cluster device of example is applied, and/or face cluster according to an embodiment of the present invention can be executed
Method.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor
It manages device and executes following steps：Determine that cluster mode, the cluster mode describe every time from described based on the quantity of image to be clustered
How many images are obtained in image to be clustered carries out a face cluster；And cluster mode based on the determination is every time from institute
The image progress face cluster that respective numbers are obtained in image to be clustered is stated, until the image to be clustered all complete by cluster.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor
The face cluster of device execution is managed in addition to face cluster for the first time, each face cluster combines the result of previous face cluster
Come carry out, wherein：The result of previous face cluster includes the image group of cluster formed after previous face cluster, it is described
Cluster image group includes all images of each cluster in each cluster；Combination to the result of previous face cluster includes knot
The subset for having clustered image group is closed, each cluster in the subset includes an image.
In one embodiment, each cluster in the subset includes face top-quality one in the cluster
Image.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor
The face cluster that reason device executes is based on the face relevant information obtained to the image progress Face datection of acquisition, the people
Face relevant information includes the face mark for representing different faces and the face characteristic value of each face.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor
Reason device execute include to the combination of the subset for having clustered image group：Face characteristic value pair based on this image obtained
The image that this is obtained carries out face cluster, obtains the inside cluster result of the image of this acquisition；The inside is gathered
The face characteristic value of the top-quality representative image of the face of each cluster and the face of image in the subset are special in class result
Value indicative is matched；If not being focused to find out matching result from the son, it is stored in the representative image as new cluster
In the subset；And if being focused to find out matching result from the son, it is determined that whether the face quality of the representative image
Higher than the face quality of the matching result found, saved if it is, replacing the matching result using the representative image
In the subset for face cluster next time.
In one embodiment, the judge index of the face quality includes at least one of the following：Face fuzziness,
Facial angle, face size and face location, the judge index are based on obtaining when carrying out Face datection to the image of acquisition
Face relevant information and obtain.
In one embodiment, the computer program instructions make computer or place when being run by computer or processor
The quantity based on image to be clustered that reason device executes determines that cluster mode includes：If the quantity of the image to be clustered is small
In equal to first threshold, it is determined that the image for obtaining the first quantity every time carries out a face cluster；If the figure to be clustered
The quantity of picture is greater than the first threshold and is less than or equal to second threshold, it is determined that the image for obtaining the second quantity every time carries out one
Secondary face cluster；And if the quantity of the image to be clustered is greater than the second threshold, it is determined that obtain third number every time
The image of amount carries out a face cluster.
Each module in face cluster device according to an embodiment of the present invention can pass through people according to an embodiment of the present invention
The processor computer program instructions that store in memory of operation of face cluster electronic equipment realize, or can be in basis
The computer instruction stored in the computer readable storage medium of the computer program product of the embodiment of the present invention is transported by computer
It is realized when row.
Face cluster method, apparatus, system and storage medium according to an embodiment of the present invention are primarily based on image to be clustered
Quantity determine cluster mode, be then based on the cluster mode determined and treat cluster image to carry out face in batches appropriate poly-
Class may be implemented customized face cluster strategy, realize flexible and efficient face cluster.
Although describing example embodiment by reference to attached drawing here, it should be understood that above example embodiment are only exemplary
, and be not intended to limit the scope of the invention to this.Those of ordinary skill in the art can carry out various changes wherein
And modification, it is made without departing from the scope of the present invention and spiritual.All such changes and modifications are intended to be included in appended claims
Within required the scope of the present invention.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
The scope of the present invention.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, apparatus embodiments described above are merely indicative, for example, the division of the unit, only
Only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can be tied
Another equipment is closed or is desirably integrated into, or some features can be ignored or not executed.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that implementation of the invention
Example can be practiced without these specific details.In some instances, well known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this specification.
Similarly, it should be understood that in order to simplify the present invention and help to understand one or more of the various inventive aspects,
To in the description of exemplary embodiment of the present invention, each feature of the invention be grouped together into sometimes single embodiment, figure,
Or in descriptions thereof.However, the method for the invention should not be construed to reflect following intention：It is i.e. claimed
The present invention claims features more more than feature expressly recited in each claim.More precisely, such as corresponding power
As sharp claim reflects, inventive point is that the spy of all features less than some disclosed single embodiment can be used
Sign is to solve corresponding technical problem.Therefore, it then follows thus claims of specific embodiment are expressly incorporated in this specific
Embodiment, wherein each, the claims themselves are regarded as separate embodiments of the invention.
It will be understood to those skilled in the art that any combination pair can be used other than mutually exclusive between feature
All features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed any method
Or all process or units of equipment are combined.Unless expressly stated otherwise, this specification (is wanted including adjoint right
Ask, make a summary and attached drawing) disclosed in each feature can be replaced with an alternative feature that provides the same, equivalent, or similar purpose.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included certain features rather than other feature, but the combination of the feature of different embodiments mean it is of the invention
Within the scope of and form different embodiments.For example, in detail in the claims, embodiment claimed it is one of any
Can in any combination mode come using.
Various component embodiments of the invention can be implemented in hardware, or to run on one or more processors
Software module realize, or be implemented in a combination thereof.It will be understood by those of skill in the art that can be used in practice
Microprocessor or digital signal processor (DSP) realize some or all of some modules according to an embodiment of the present invention
Function.The present invention is also implemented as some or all program of device (examples for executing method as described herein
Such as, computer program and computer program product).It is such to realize that program of the invention can store in computer-readable medium
On, or may be in the form of one or more signals.Such signal can be downloaded from an internet website to obtain, or
Person is provided on the carrier signal, or is provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability
Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real
It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch
To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame
Claim.
The above description is merely a specific embodiment or to the explanation of specific embodiment, protection of the invention
Range is not limited thereto, and anyone skilled in the art in the technical scope disclosed by the present invention, can be easily
Expect change or replacement, should be covered by the protection scope of the present invention.Protection scope of the present invention should be with claim
Subject to protection scope.
Claims (10)
1. a kind of face cluster method, which is characterized in that the method includes：
Determine that cluster mode, the cluster mode describe to obtain from the image to be clustered every time based on the quantity of image to be clustered
How many images are taken to carry out a face cluster；And
It is poly- that the image that cluster mode based on the determination obtains respective numbers from the image to be clustered every time carries out face
Class, until the image to be clustered all complete by cluster.
2. the method according to claim 1, wherein each face cluster is equal in addition to face cluster for the first time
It is carried out in conjunction with the result of previous face cluster, wherein：
The result of previous face cluster includes the image group of cluster formed after previous face cluster, described to have clustered image
Group includes all images of each cluster in each cluster；
Combination to the result of previous face cluster includes the institute in conjunction with the subset for having clustered image group, in the subset
Stating each cluster includes an image.
3. according to the method described in claim 2, it is characterized in that, each cluster in the subset includes in the cluster
The top-quality image of face.
4. according to the method in claim 2 or 3, which is characterized in that the face cluster be based on the image to acquisition into
The face relevant information that row Face datection obtains, the face relevant information include representing the face mark of different faces and every
The face characteristic value of a face.
5. according to the method described in claim 4, it is characterized in that, including to the combination of the subset for having clustered image group：
Face characteristic value based on this image obtained carries out face cluster to the image that this is obtained, and obtains this and obtains
The inside cluster result of the image taken；
By the face characteristic value of the top-quality representative image of face of each cluster and the son in the internal cluster result
The face characteristic value of image is concentrated to be matched；
If not being focused to find out matching result from the son, the subset is stored in using the representative image as new cluster
In；And
If being focused to find out matching result from the son, it is determined that whether the face quality of the representative image, which is higher than, is found
The face quality of matching result saves in the subset if it is, replacing the matching result using the representative image
For face cluster next time.
6. the method according to claim 3 or 5, which is characterized in that the judge index of the face quality includes in following
At least one of：Face fuzziness, facial angle, face size and face location, the judge index are based on to acquisition
Image carry out Face datection when acquisition face relevant information and obtain.
7. method according to claim 1 or 2, which is characterized in that the quantity based on image to be clustered determines cluster
Mode includes：
If the quantity of the image to be clustered is less than or equal to first threshold, it is determined that the image for obtaining the first quantity every time carries out
Face cluster；
If the quantity of the image to be clustered is greater than the first threshold and is less than or equal to second threshold, it is determined that obtain every time
The image of second quantity carries out a face cluster；And
If the quantity of the image to be clustered is greater than the second threshold, it is determined that the image for obtaining third quantity every time carries out
Face cluster.
8. a kind of face cluster device, which is characterized in that described device includes：
Determining module, for determining that cluster mode, the cluster mode describe every time from described based on the quantity of image to be clustered
How many images are obtained in image to be clustered carries out a face cluster；And
Cluster module, the cluster mode determined for module based on the determination obtain accordingly from the image to be clustered every time
The image of quantity carries out face cluster, until the image to be clustered all complete by cluster.
9. a kind of face cluster system, which is characterized in that the system comprises storage device and processor, on the storage device
It is stored with the computer program run by the processor, the computer program is executed when being run by the processor as weighed
Benefit requires face cluster method described in any one of 1-7.
10. a kind of storage medium, which is characterized in that be stored with computer program, the computer program on the storage medium
The face cluster method as described in any one of claim 1-7 is executed at runtime.
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