CN105574512A - Method and device for processing image - Google Patents
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Abstract
The invention relates to a method and device for processing an image, and belongs to the technical field of image processing. The method includes the steps of: obtaining a cluster set which includes at least two face images; a face recognition algorithm is adopted to obtain first characteristic values of the at least two face images; and according to the first characteristic values of the at least two face images, splitting the cluster set. The method obtains the first characteristic values of the at least two face images in the cluster set by adoption of the face recognition algorithm, splits the cluster set according to the first characteristic values of the at least two face images, utilizes the first characteristic values to correct the cluster set including face images of different people, overcomes limitation of the degree of accuracy of a clustering algorithm, improves the degree of accuracy of a clustering result, and brings good user experience.
Description
Technical field
The disclosure relates to technical field of image processing, particularly relates to a kind of method and apparatus of image procossing.
Background technology
Recognition of face is a kind of biological identification technology carrying out identification based on the face feature information of people.Utilize face recognition technology, the piece identity in photo can be identified, and according to the piece identity in photo, the photo belonging to same person is referred in one bunch.
In the process of recognition of face, hierarchical clustering algorithm can be adopted to judge, and whether two faces belong to same person: calculate the distance between face feature vector corresponding to two facial images; The distance relatively calculated and the size of distance threshold; If the threshold value calculated is not more than distance threshold, then judge that two facial images belong to same person; If the threshold value calculated is greater than distance threshold, then judge that two facial images do not belong to same person.
But, due to the restriction of clustering algorithm self accuracy, judge that in fact two facial images belonging to same person may belong to different people, the limited accuracy of cluster result, poor user experience.
Summary of the invention
For overcoming in correlation technique the problem of the limited accuracy that there is cluster result, the disclosure provides a kind of method and apparatus of image procossing.
According to the first aspect of disclosure embodiment, a kind of method of image procossing is provided, comprises:
Obtain a cluster set, described cluster set comprises at least two width facial images;
Adopt the First Eigenvalue of at least two width facial images described in face recognition algorithms acquisition;
According to the First Eigenvalue of described at least two width facial images, described cluster set is split.
By the First Eigenvalue adopting face recognition algorithms to obtain at least two width facial images in a cluster set, and according to the First Eigenvalue of at least two width facial images, this cluster set is split, the First Eigenvalue is utilized to correct the cluster set comprising the facial image belonging to different people, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
In a kind of possible implementation of first aspect, the type of described the First Eigenvalue comprises at least one in the sex of personage belonging to facial image, age, race.
Sex, age, race are the distinctive features of face, these features of a people immobilize, utilize these distinctive features, the mistake be aggregated in by the facial image belonging to different people in one bunch can be corrected, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
Alternatively, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Linear discriminate analysis LDA algorithm is adopted to determine the sex of described each width facial image affiliated personage separately to each width facial image respectively.
Adopt LDA algorithm to realize the determination of personage's sex belonging to facial image, algorithm is ripe, accuracy rate is high, cost is low.
Alternatively, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Principal component analysis (PCA) PCA method is adopted to extract described each width facial image image feature value separately to each width facial image respectively;
According to described each width facial image described image feature value separately, least square regression algorithm is used to calculate the age of described each width facial image affiliated personage separately.
Adopt the determination at personage's age belonging to PCA and least square regression algorithm realization facial image, algorithm is ripe, accuracy rate is high, cost is low.
Alternatively, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Extract the features of skin colors value of each width facial image respectively;
According to the features of skin colors value of described each width facial image, support vector machines is adopted to determine the race of described each width facial image affiliated personage separately.
Adopt SVM to realize the determination of personage race belonging to facial image, algorithm is ripe, accuracy rate is high, cost is low.
In the another kind of possible implementation of first aspect, the described the First Eigenvalue according to described at least two width facial images, splits described cluster set, comprising:
When the First Eigenvalue of described at least two width facial image same types is different or difference exceedes setting range, described cluster set is split as at least two cluster set, and the identical or difference of the First Eigenvalue of each width facial image same type in each cluster set after fractionation is in setting range.
Consider that in face recognition process, the determination of eigenwert exists certain error, therefore facial image eigenwert difference or difference being exceeded setting range is split as at least two cluster set, to correct the mistake be referred to by the facial image belonging to different people in a cluster set, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
In another possible implementation of first aspect, described method also comprises:
Obtain several photos;
Adopt Face datection algorithm, from each photo, obtain facial image;
Adopt hierarchical clustering algorithm, cluster is carried out to the facial image obtained, obtains at least one cluster set.
Carry out Face datection and cluster by comparison film, obtain required cluster set.
According to the second aspect of disclosure embodiment, a kind of device of image procossing is provided, comprises:
First acquisition module, for obtaining a cluster set, described cluster set comprises at least two width facial images;
Second acquisition module, for adopting the First Eigenvalue of at least two width facial images described in face recognition algorithms acquisition;
Split module, for the First Eigenvalue according to described at least two width facial images, described cluster set is split.
In a kind of possible implementation of second aspect, the type of described the First Eigenvalue comprises at least one in the sex of personage belonging to facial image, age, race.
Alternatively, described second acquisition module is used for,
Linear discriminate analysis LDA algorithm is adopted to determine the sex of described each width facial image affiliated personage separately to each width facial image respectively.
Alternatively, described second acquisition module is used for,
Principal component analysis (PCA) PCA method is adopted to extract described each width facial image image feature value separately to each width facial image respectively;
According to described each width facial image described image feature value separately, least square regression algorithm is used to calculate the age of described each width facial image affiliated personage separately.
Alternatively, described second acquisition module is used for,
Extract the features of skin colors value of each width facial image respectively;
According to the features of skin colors value of described each width facial image, support vector machines is adopted to determine the race of described each width facial image affiliated personage separately.
In the another kind of possible implementation of second aspect, described fractionation module is used for,
When the First Eigenvalue of described at least two width facial image same types is different or difference exceedes setting range, described cluster set is split as at least two cluster set, and the identical or difference of the First Eigenvalue of each width facial image same type in each cluster set after fractionation is in setting range.
In another possible implementation of second aspect, described device also comprises:
3rd acquisition module, for obtaining several photos;
4th acquisition module, for adopting Face datection algorithm, obtains facial image from each photo;
Cluster module, for adopting hierarchical clustering algorithm, carrying out cluster to the facial image obtained, obtaining at least one cluster set.
According to the third aspect of disclosure embodiment, a kind of device of image procossing is provided, comprises:
Processor;
For the storer of storage of processor executable instruction;
Wherein, described processor is configured to:
Obtain a cluster set, described cluster set comprises at least two width facial images;
Adopt the First Eigenvalue of at least two width facial images described in face recognition algorithms acquisition;
According to the First Eigenvalue of described at least two width facial images, described cluster set is split.
The technical scheme that embodiment of the present disclosure provides can comprise following beneficial effect: by the First Eigenvalue adopting face recognition algorithms to obtain at least two width facial images in a cluster set, and according to the First Eigenvalue of at least two width facial images, this cluster set is split, the First Eigenvalue is utilized to correct the cluster set comprising the facial image belonging to different people, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
Should be understood that, it is only exemplary and explanatory that above general description and details hereinafter describe, and can not limit the disclosure.
Accompanying drawing explanation
Accompanying drawing to be herein merged in instructions and to form the part of this instructions, shows embodiment according to the invention, and is used from instructions one and explains principle of the present invention.
Fig. 1 a-Fig. 1 d is the application scenarios figure of the method for a kind of image procossing according to an exemplary embodiment;
Fig. 2 is the process flow diagram of the method for a kind of image procossing according to an exemplary embodiment;
Fig. 3 is the process flow diagram of the method for a kind of image procossing according to an exemplary embodiment;
Fig. 4 is the schematic diagram of a kind of Face datection result according to an exemplary embodiment;
Fig. 5 is the block diagram of the device of a kind of image procossing according to an exemplary embodiment;
Fig. 6 is the block diagram of the device of a kind of image procossing according to an exemplary embodiment;
Fig. 7 is the block diagram of the device of a kind of image procossing according to an exemplary embodiment.
Embodiment
Here will be described exemplary embodiment in detail, its sample table shows in the accompanying drawings.When description below relates to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawing represents same or analogous key element.Embodiment described in following exemplary embodiment does not represent all embodiments consistent with the present invention.On the contrary, they only with as in appended claims describe in detail, the example of apparatus and method that aspects more of the present invention are consistent.
First composition graphs 1a-Fig. 1 d simply introduces the application scenarios of the method for the image procossing that disclosure embodiment provides below.
Along with popularizing of the mobile terminal such as smart mobile phone, panel computer, increasing user's choice for use mobile terminal is taken pictures, and mobile terminal local terminal or mobile terminal store a large amount of photo by the high in the clouds that network connects, as shown in Figure 1a.In these photos, that some is taken is user oneself; That some is taken is other people, as the relatives, friend, colleague etc. of user; Also some shooting is the group photo of at least two people, as the good fortune of the whole family; Also some shooting does not have personage, as landscape, the scenic spots and historical sites etc.
Conveniently user searches and uses, mobile terminal self or by server by the photo of storage according to shooting personage sort out formation photograph album, each photograph album comprises all photos of a personage, personage belonging to the photo that each photograph album comprises is different, millet " face photograph album " as shown in Figure 1 b.
Wherein, all photos of a personage can be shown in each photograph album, as illustrated in figure 1 c; Also can show the facial image of a personage in all photos, as shown in Figure 1 d, increase the interest of photo, promote Consumer's Experience.
Fig. 2 is the process flow diagram of the method for a kind of image procossing according to an exemplary embodiment, and as shown in Figure 2, the method for this image procossing is used for, in mobile terminal, comprising the following steps.
In step S101, obtain a cluster set.
In the present embodiment, the cluster set obtained in this step S101 comprises at least two width facial images.In conjunction with application scenarios of the present disclosure, the cluster set of acquisition closes the photograph album coming from the photo classification formation that mobile terminal stores.
In step s 102, face recognition algorithms is adopted to obtain the First Eigenvalue of at least two width facial images.
It should be noted that, if the arithmetic capability of mobile terminal is powerful, then step S102 can, by mobile terminal complete independently, realize simple and convenient; If the arithmetic capability of mobile terminal is inadequate, then step S102 can be completed by server by mobile terminal, namely mobile terminal uploads the First Eigenvalue of at least two width facial images that cluster set merging reception server is determined to server, reduces the requirement to mobile terminal, reduces and realize cost.
In step s 103, according to the First Eigenvalue of at least two width facial images, this cluster set is split.
The First Eigenvalue of disclosure embodiment by adopting face recognition algorithms to obtain at least two width facial images in a cluster set, and according to the First Eigenvalue of at least two width facial images, this cluster set is split, the First Eigenvalue is utilized to correct the cluster set comprising the facial image belonging to different people, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
Fig. 3 is the process flow diagram of the method for a kind of image procossing according to an exemplary embodiment, and as shown in Figure 3, the method for this image procossing is used for, in mobile terminal, comprising the following steps.
In step s 201, several photos are obtained.
From disclosure application scenarios part, the photo stored is formed photograph album according to personage's cluster of shooting by mobile terminal, each photograph album comprises all photos of a personage, namely mobile terminal can carry out full dose cluster in the starting stage to all photos, and carries out increment cluster in follow-up phase to newly-increased photo.The photo obtained in step S201 is all photos in the starting stage before cluster, also can be the newly-increased photo in follow-up phase before cluster.
In actual applications, mobile terminal is called the application program (application of " photo " usually by name, be called for short app) be in charge of the cluster situation of all photos in mobile terminal and photo, therefore directly can obtain photo from " photo " app.
In step S202, adopt Face datection algorithm, from each photo, obtain facial image.
In the present embodiment, facial image is the minimum image that photo comprises whole face, the image in rectangle frame as shown in Figure 4.
In a kind of implementation of the present embodiment, this step S202 can comprise:
Extract the haar eigenwert of a photo;
According to the haar eigenwert extracted, from this photo, determine facial image.
Easily know, some features of face simply can be described by rectangular characteristic, as darker than the color of cheek in eyes, and bridge of the nose both sides are darker than bridge of the nose color, and face is darker etc. than face ambient color.Haar eigenwert by value be feature templates words spoken by an actor from offstage look rectangular pixels and deduct black rectangle pixel and, reflect the grey scale change situation of image, thus can determine whether as facial image according to haar eigenwert.Wherein, feature templates can be the combination of left side white rectangle and right side white rectangle, the combination of upside white rectangle and downside black rectangle, the combination of both sides white rectangle and middle black rectangle, the combination etc. of upper left side and lower right side white rectangle and upper right side and lower left side black rectangle.
In actual applications, other Face datection algorithm also can be adopted from a photo to obtain facial image, as Adaboost algorithm, will not enumerate at this.
It should be noted that, if a photo is the group photo of at least two people, then correspondingly can determine at least two width facial images from this photo in step S202.
In step S203, adopt hierarchical clustering algorithm, cluster is carried out to the facial image obtained, obtains at least one cluster set.
In the present embodiment, this step S203 can comprise:
The each width facial image obtained is classified as separately a cluster set, obtains some cluster set;
Calculate the distance between each cluster set;
When distance between two cluster set is not more than setting threshold value, a cluster set is merged in two cluster set, and judge that the cluster set after merging is the need of again merging;
When all cluster set distance between any two is all greater than setting threshold value, be the some cluster set obtained by current some cluster set cooperations.
Such as, before once merging, a cluster set comprises facial image A and facial image B, and another cluster set comprises facial image C and facial image D, and another cluster set comprises facial image E and facial image F.After once merging, a cluster set comprises facial image A, facial image B, facial image C and facial image D, and another cluster set comprises facial image E and facial image F.Now calculate the distance between all kinds of facial image again, determine whether, by facial image A, facial image B, facial image C, facial image D, to merge into a class with facial image E, facial image F.
It should be noted that, when the photo obtained is the photo in the starting stage before cluster, the some cluster set obtained are exactly the cluster set of all independent classification, to carry out full dose cluster.Such as, in step S202, get N width facial image, then obtain N number of cluster set.
When the photo obtained is the newly-increased photo in follow-up phase before cluster, each width facial image obtained is classified as separately a cluster set, obtains some cluster set, can comprise:
The each width facial image obtained is classified as separately a cluster set;
The cluster set sorted out separately and the cluster set carrying out cluster are unified, as the some cluster set obtained.
Namely the some cluster set obtained, except comprising the cluster set of all independent classification, also comprise the cluster set carrying out cluster, to carry out increment cluster.Such as, in step S202, get M width facial image, L the cluster set that before adding, cluster obtains, then obtain (M+L) individual cluster set.
Alternatively, calculate the distance between each cluster set, can comprise:
Extract the textural characteristics value of each width facial image in two cluster set respectively;
According to the textural characteristics value extracted, calculate the distance between each width facial image in two cluster set;
Minimum value, mean value or maximal value in all distances that seletion calculation goes out, as the distance between two cluster set.
Such as, a cluster set comprises facial image A and facial image B, another cluster set comprises facial image C and facial image D, the distance calculated between facial image A and facial image C is 0.3, distance between facial image A and facial image D is 0.7, distance between facial image B and facial image C is 0.5, and the distance between facial image B and facial image D is 0.1.If selection minimum value, then using 0.1 as the distance between two cluster set; If selection mean value, then by (0.3+0.7+0.5+0.1)/4=0.4 as the distance between two cluster set; As selected maximal value, then using 0.7 as the distance between two cluster set.
In above-mentioned implementation, the distance between each width facial image in two cluster set refers to, each width facial image in two cluster set in a cluster set, and the distance between each width facial image of another cluster set in two cluster set.Such as, a cluster set comprises facial image A and facial image B, another cluster set comprises facial image C and facial image D, then calculate the distance between facial image A and facial image C, distance, the distance between facial image B and facial image C and the distance between facial image B and facial image D between facial image A and facial image D.
Understandably, each cluster set distance between any two can be calculated respectively in the manner described above.
Preferably, extract the textural characteristics value of each width facial image in two cluster set respectively, can comprise:
Gabor wavelet conversion is adopted to carry out feature extraction to a width facial image, and the vectorial textural characteristics value as this width facial image of Gabor characteristic that will calculate.
Preferably, extract the textural characteristics value of each width facial image in two cluster set respectively, can comprise:
Local binary patterns (LocalBinaryPatterns is called for short LBP) operator is adopted to calculate the relation of each pixel and its surrounding pixel to a width facial image, and using the textural characteristics value of the proper vector of formation as this width facial image.
It should be noted that, respectively to each width facial image in two cluster set according to any one texture feature extraction value in above-mentioned two kinds of modes, the textural characteristics value of the face images in two cluster set can be extracted.In actual applications, further feature extraction algorithm also can be adopted to extract the textural characteristics value of each width facial image respectively, will not enumerate at this.
Preferably, according to the textural characteristics value extracted, calculate the distance between each width facial image in two cluster set, can comprise:
The textural characteristics value of extraction is brought in Euclidean distance formula, calculates the distance between the width facial image in a cluster set and the width facial image in another cluster set.
Preferably, according to the textural characteristics value extracted, calculate the distance between each width facial image in two cluster set, can comprise:
The textural characteristics value of extraction is brought in cosine similarity formula, using the value of 1-cosine similarity as the distance between the width facial image in a cluster set and the width facial image in another cluster set.
It should be noted that, respectively according to any one in above-mentioned two kinds of modes, distance is calculated to each width facial image in two cluster set, the distance between each width facial image in a cluster set and each width facial image in another cluster set can be calculated.In actual applications, other also can be adopted to calculate the distance between two width facial images apart from account form, will not enumerate at this.
In addition, in the present embodiment, just according to the distance of facial image, cluster is carried out to facial image, in actual applications, cluster can also be carried out according to auxiliary realization of the information such as the shooting time of photo to facial image.
In step S204, obtain a cluster set.
In the present embodiment, the cluster set obtained in this step S204 comprises at least two width facial images.Easily know, the cluster set of acquisition closes and comes from least one the cluster set obtained in step S203.In actual applications, all cluster set comprising at least two width facial images that can obtain in step S204 all perform step S204-step S206, are only combined into example with a cluster set in the present embodiment, not as to restriction of the present disclosure.
In step S205, face recognition algorithms is adopted to obtain the First Eigenvalue of at least two width facial images.
Alternatively, the type of the First Eigenvalue can comprise at least one in the sex of personage belonging to facial image, age, race.
Understandably, sex, age, race are the distinctive features of face, these features of a people immobilize, utilize these distinctive features, the mistake be aggregated in by the facial image belonging to different people in one bunch can be corrected, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
Preferably, when the type of the First Eigenvalue comprises the sex of personage belonging to facial image, this step S205 can comprise:
Linear discriminate analysis (LinearDiscriminantAnalysis is called for short LDA) algorithm is adopted to determine the sex of each width facial image affiliated personage separately to each width facial image respectively.
Preferably, when the type of the First Eigenvalue comprises the sex of personage belonging to facial image, this step S205 can comprise:
Principal component analysis (PCA) (PrincipalComponentAnalysis is called for short PCA) method is adopted to extract each width facial image image feature value separately to each width facial image respectively;
According to each width facial image image feature value separately, least square regression algorithm is used to calculate the age of each width facial image affiliated personage separately.
Preferably, when the type of the First Eigenvalue comprises the sex of personage belonging to facial image, this step S205 can comprise:
Extract the features of skin colors value of each width facial image respectively;
According to the features of skin colors value of each width facial image, support vector machine (SupportVectorMachine is called for short SVM) is adopted to determine the race of personage belonging to each width facial image separately.
In step S206, according to the First Eigenvalue of at least two width facial images, this cluster set is split.
In the present embodiment, this step S206 can comprise:
When the First Eigenvalue of at least two width facial image same types is different or difference exceedes setting range, this cluster set is split as at least two cluster set, and the identical or difference of the First Eigenvalue of each width facial image same type in each cluster set after fractionation is in setting range.
In actual applications, consider that in face recognition process, the determination of eigenwert exists certain error, therefore facial image eigenwert difference or difference being exceeded setting range is split as at least two cluster set, to correct the mistake be referred to by the facial image belonging to different people in a cluster set, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
Alternatively, when the type of the First Eigenvalue comprises at least two kinds in the sex of personage belonging to facial image, age, race, this cluster set is split as at least two cluster set, can comprises:
Successively for the type of often kind of the First Eigenvalue, this cluster set is split as at least two cluster set, each width facial image in each cluster set after fractionation for the First Eigenvalue of type identical or difference in setting range.
Preferably, when the type of the First Eigenvalue comprises the sex of personage belonging to facial image, age, Zhong Zushi, successively for the type of often kind of the First Eigenvalue, this cluster set is split as at least two cluster set, comprises:
According to the order at sex, race, age successively for the type of often kind of the First Eigenvalue, this cluster set is split as at least two cluster set.
Such as, when the type of the First Eigenvalue comprises the sex of personage belonging to facial image, if it is woman's facial image for man's facial image and eigenwert that this cluster set comprises eigenwert, then by eigenwert for man's facial image is split as a cluster set, eigenwert is split as a cluster set for woman's facial image.
When the type of the First Eigenvalue comprises personage belonging to facial image ethnic, if it is in the facial image of black race at least two kinds that this cluster set comprises eigenwert is white facial image, eigenwert is yellow facial image and eigenwert, be then that white facial image is split as a cluster set by eigenwert, eigenwert is that the facial image of yellow is split as a cluster set, and eigenwert is that the facial image of black race is split as a cluster set.
When the type of the First Eigenvalue comprises the age of personage belonging to facial image, if this cluster set comprises two facial images that eigenwert difference is greater than setting range (as-10 ~ 10), then be split as a cluster set by a facial image and with the facial image that its eigenwert difference is no more than setting range half (-5 ~ 5), be split as a cluster set by another facial image and with the facial image that its eigenwert difference is no more than setting value half.
It should be noted that, the present embodiment is only introduced the cluster of facial image, in actual applications, mobile terminal is after cluster, can the face images of same cluster set be referred in a photograph album, simultaneously because facial image comes from photo, therefore the photo at the face images place in same cluster set is also referred in this photograph album, for user provides facial image and photo two kinds of display modes, wherein the display mode of facial image can as shown in Figure 1 d, and the display mode of photo can be as illustrated in figure 1 c.
The First Eigenvalue of disclosure embodiment by adopting face recognition algorithms to obtain at least two width facial images in a cluster set, and according to the First Eigenvalue of at least two width facial images, this cluster set is split, the First Eigenvalue is utilized to correct the cluster set comprising the facial image belonging to different people, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
Fig. 5 is the block diagram of the device of a kind of image procossing according to an exemplary embodiment, and with reference to Fig. 5, this device comprises the first acquisition module 301, second acquisition module 302 and splits module 303.
This first acquisition module 301 is configured to acquisition cluster set, and this cluster set comprises at least two width facial images.
This second acquisition module 302 is configured to adopt face recognition algorithms to obtain the First Eigenvalue of at least two width facial images.
This fractionation module 303 is configured to the First Eigenvalue according at least two width facial images, splits this cluster set.
The First Eigenvalue of disclosure embodiment by adopting face recognition algorithms to obtain at least two width facial images in a cluster set, and according to the First Eigenvalue of at least two width facial images, this cluster set is split, the First Eigenvalue is utilized to correct the cluster set comprising the facial image belonging to different people, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
Fig. 6 is the block diagram of the device of a kind of image procossing according to an exemplary embodiment, and with reference to Fig. 6, this device comprises the first acquisition module 401, second acquisition module 402 and splits module 403.
This first acquisition module 401 is configured to acquisition cluster set, and this cluster set comprises at least two width facial images.
This second acquisition module 402 is configured to adopt face recognition algorithms to obtain the First Eigenvalue of at least two width facial images.
This fractionation module 403 is configured to the First Eigenvalue according at least two width facial images, splits this cluster set.
In a kind of implementation of the present embodiment, the type of the First Eigenvalue can comprise at least one in the sex of personage belonging to facial image, age, race.
Alternatively, this second acquisition module 402 can be configured to adopt LDA algorithm to determine the sex of each width facial image affiliated personage separately to each width facial image respectively.
Alternatively, this second acquisition module 402 can be configured to adopt PCA method to extract each width facial image image feature value separately to each width facial image respectively; According to each width facial image image feature value separately, least square regression algorithm is used to calculate the age of each width facial image affiliated personage separately.
Alternatively, this second acquisition module 402 can be configured to the features of skin colors value extracting each width facial image respectively; According to the features of skin colors value of each width facial image, support vector machines is adopted to determine the race of each width facial image affiliated personage separately.
In the another kind of implementation of the present embodiment, this fractionation module 403 can be configured to the First Eigenvalue when at least two width facial image same types different or difference exceedes setting range time, this cluster set is split as at least two cluster set, and the identical or difference of the First Eigenvalue of each width facial image same type in each cluster set after fractionation is in setting range.
In another implementation of the present embodiment, this device can also comprise the 3rd acquisition module 404, the 4th acquisition module 405 and cluster module 406.
3rd acquisition module 404 is configured to obtain several photos.
4th acquisition module 405 is configured to adopt Face datection algorithm, from each photo, obtain facial image.
This cluster module 406 is configured to adopt hierarchical clustering algorithm, carries out cluster, obtain at least one cluster set to the facial image obtained.
The First Eigenvalue of disclosure embodiment by adopting face recognition algorithms to obtain at least two width facial images in a cluster set, and according to the First Eigenvalue of at least two width facial images, this cluster set is split, the First Eigenvalue is utilized to correct the cluster set comprising the facial image belonging to different people, overcome the restriction of clustering algorithm self accuracy, improve the accuracy of cluster result, Consumer's Experience is good.
About the device in above-described embodiment, wherein the concrete mode of modules executable operations has been described in detail in about the embodiment of the method, will not elaborate explanation herein.
Fig. 7 is the block diagram of the device 800 of a kind of image procossing according to an exemplary embodiment.Such as, device 800 can be mobile phone, computing machine, digital broadcast terminal, messaging devices, game console, tablet device, Medical Devices, body-building equipment, personal digital assistant etc.
With reference to Fig. 7, device 800 can comprise following one or more assembly: processing components 802, storer 804, electric power assembly 806, multimedia groupware 808, audio-frequency assembly 810, the interface 812 of I/O (I/O), sensor module 814, and communications component 816.
The integrated operation of the usual control device 800 of processing components 802, such as with display, call, data communication, camera operation and record operate the operation be associated.Processing components 802 can comprise one or more processor 820 to perform instruction, to complete all or part of step of above-mentioned method.In addition, processing components 802 can comprise one or more module, and what be convenient between processing components 802 and other assemblies is mutual.Such as, processing components 802 can comprise multi-media module, mutual with what facilitate between multimedia groupware 808 and processing components 802.
Storer 804 is configured to store various types of data to be supported in the operation of equipment 800.The example of these data comprises for any application program of operation on device 800 or the instruction of method, contact data, telephone book data, message, picture, video etc.Storer 804 can be realized by the volatibility of any type or non-volatile memory device or their combination, as static RAM (SRAM), Electrically Erasable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory EPROM (EPROM), programmable read only memory (PROM), ROM (read-only memory) (ROM), magnetic store, flash memory, disk or CD.
The various assemblies that electric power assembly 806 is device 800 provide electric power.Electric power assembly 806 can comprise power-supply management system, one or more power supply, and other and the assembly generating, manage and distribute electric power for device 800 and be associated.
Multimedia groupware 808 is included in the screen providing an output interface between described device 800 and user.In certain embodiments, screen can comprise liquid crystal display (LCD) and touch panel (TP).If screen comprises touch panel, screen may be implemented as touch-screen, to receive the input signal from user.Touch panel comprises one or more touch sensor with the gesture on sensing touch, slip and touch panel.Described touch sensor can the border of not only sensing touch or sliding action, but also detects the duration relevant to described touch or slide and pressure.In certain embodiments, multimedia groupware 808 comprises a front-facing camera and/or post-positioned pick-up head.When equipment 800 is in operator scheme, during as screening-mode or video mode, front-facing camera and/or post-positioned pick-up head can receive outside multi-medium data.Each front-facing camera and post-positioned pick-up head can be fixing optical lens systems or have focal length and optical zoom ability.
Audio-frequency assembly 810 is configured to export and/or input audio signal.Such as, audio-frequency assembly 810 comprises a microphone (MIC), and when device 800 is in operator scheme, during as call model, logging mode and speech recognition mode, microphone is configured to receive external audio signal.The sound signal received can be stored in storer 804 further or be sent via communications component 816.In certain embodiments, audio-frequency assembly 810 also comprises a loudspeaker, for output audio signal.
I/O interface 812 is for providing interface between processing components 802 and peripheral interface module, and above-mentioned peripheral interface module can be keyboard, some striking wheel, button etc.These buttons can include but not limited to: home button, volume button, start button and locking press button.
Sensor module 814 comprises one or more sensor, for providing the state estimation of various aspects for device 800.Such as, sensor module 814 can detect the opening/closing state of equipment 800, the relative positioning of assembly, such as described assembly is display and the keypad of device 800, the position of all right pick-up unit 800 of sensor module 814 or device 800 1 assemblies changes, the presence or absence that user contacts with device 800, the temperature variation of device 800 orientation or acceleration/deceleration and device 800.Sensor module 814 can comprise proximity transducer, be configured to without any physical contact time detect near the existence of object.Sensor module 814 can also comprise optical sensor, as CMOS or ccd image sensor, for using in imaging applications.In certain embodiments, this sensor module 814 can also comprise acceleration transducer, gyro sensor, Magnetic Sensor, pressure transducer or temperature sensor.
Communications component 816 is configured to the communication being convenient to wired or wireless mode between device 800 and other equipment.Device 800 can access the wireless network based on communication standard, as WiFi, 2G or 3G, or their combination.In one exemplary embodiment, communications component 816 receives from the broadcast singal of external broadcasting management system or broadcast related information via broadcast channel.In one exemplary embodiment, described communications component 816 also comprises near-field communication (NFC) module, to promote junction service.Such as, can based on radio-frequency (RF) identification (RFID) technology in NFC module, Infrared Data Association (IrDA) technology, ultra broadband (UWB) technology, bluetooth (BT) technology and other technologies realize.
In the exemplary embodiment, device 800 can be realized, for performing said method by one or more application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing appts (DSPD), programmable logic device (PLD) (PLD), field programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic components.
In the exemplary embodiment, additionally provide a kind of non-transitory computer-readable recording medium comprising instruction, such as, comprise the storer 804 of instruction, above-mentioned instruction can perform said method by the processor 820 of device 800.Such as, described non-transitory computer-readable recording medium can be ROM, random access memory (RAM), CD-ROM, tape, floppy disk and optical data storage devices etc.
A kind of non-transitory computer-readable recording medium, when the instruction in described storage medium is performed by the processor of mobile terminal, make mobile terminal can perform a kind of method of image procossing, described method comprises:
Obtain a cluster set, described cluster set comprises at least two width facial images;
Adopt the First Eigenvalue of at least two width facial images described in face recognition algorithms acquisition;
According to the First Eigenvalue of described at least two width facial images, described cluster set is split.
In a kind of implementation of the present embodiment, the type of described the First Eigenvalue comprises at least one in the sex of personage belonging to facial image, age, race.
Alternatively, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Linear discriminate analysis LDA algorithm is adopted to determine the sex of described each width facial image affiliated personage separately to each width facial image respectively.
Alternatively, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Principal component analysis (PCA) PCA method is adopted to extract described each width facial image image feature value separately to each width facial image respectively;
According to described each width facial image described image feature value separately, least square regression algorithm is used to calculate the age of described each width facial image affiliated personage separately.
Alternatively, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Extract the features of skin colors value of each width facial image respectively;
According to the features of skin colors value of described each width facial image, support vector machines is adopted to determine the race of described each width facial image affiliated personage separately.
In the another kind of implementation of the present embodiment, the described the First Eigenvalue according to described at least two width facial images, splits described cluster set, comprising:
When the First Eigenvalue of described at least two width facial image same types is different or difference exceedes setting range, described cluster set is split as at least two cluster set, and the identical or difference of the First Eigenvalue of each width facial image same type in each cluster set after fractionation is in setting range.
In another implementation of the present embodiment, described method also comprises:
Obtain several photos;
Adopt Face datection algorithm, from each photo, obtain facial image;
Adopt hierarchical clustering algorithm, cluster is carried out to the facial image obtained, obtains at least one cluster set.
Those skilled in the art, at consideration instructions and after putting into practice invention disclosed herein, will easily expect other embodiment of the present invention.The application is intended to contain any modification of the present invention, purposes or adaptations, and these modification, purposes or adaptations are followed general principle of the present invention and comprised the undocumented common practise in the art of the disclosure or conventional techniques means.Instructions and embodiment are only regarded as exemplary, and true scope of the present invention and spirit are pointed out by claim below.
Should be understood that, the present invention is not limited to precision architecture described above and illustrated in the accompanying drawings, and can carry out various amendment and change not departing from its scope.Scope of the present invention is only limited by appended claim.
Claims (15)
1. a method for image procossing, is characterized in that, comprising:
Obtain a cluster set, described cluster set comprises at least two width facial images;
Adopt the First Eigenvalue of at least two width facial images described in face recognition algorithms acquisition;
According to the First Eigenvalue of described at least two width facial images, described cluster set is split.
2. method according to claim 1, is characterized in that, the type of described the First Eigenvalue comprises at least one in the sex of personage belonging to facial image, age, race.
3. method according to claim 2, is characterized in that, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Linear discriminate analysis LDA algorithm is adopted to determine the sex of described each width facial image affiliated personage separately to each width facial image respectively.
4. method according to claim 2, is characterized in that, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Principal component analysis (PCA) PCA method is adopted to extract described each width facial image image feature value separately to each width facial image respectively;
According to described each width facial image described image feature value separately, least square regression algorithm is used to calculate the age of described each width facial image affiliated personage separately.
5. method according to claim 2, is characterized in that, the First Eigenvalue of at least two width facial images described in described employing face recognition algorithms obtains, comprising:
Extract the features of skin colors value of each width facial image respectively;
According to the features of skin colors value of described each width facial image, support vector machines is adopted to determine the race of described each width facial image affiliated personage separately.
6. the method according to any one of claim 1-5, is characterized in that, the described the First Eigenvalue according to described at least two width facial images, splits described cluster set, comprising:
When the First Eigenvalue of described at least two width facial image same types is different or difference exceedes setting range, described cluster set is split as at least two cluster set, and the identical or difference of the First Eigenvalue of each width facial image same type in each cluster set after fractionation is in setting range.
7. the method according to any one of claim 1-5, is characterized in that, described method also comprises:
Obtain several photos;
Adopt Face datection algorithm, from each photo, obtain facial image;
Adopt hierarchical clustering algorithm, cluster is carried out to the facial image obtained, obtains at least one cluster set.
8. a device for image procossing, is characterized in that, comprising:
First acquisition module, for obtaining a cluster set, described cluster set comprises at least two width facial images;
Second acquisition module, for adopting the First Eigenvalue of at least two width facial images described in face recognition algorithms acquisition;
Split module, for the First Eigenvalue according to described at least two width facial images, described cluster set is split.
9. device according to claim 8, is characterized in that, the type of described the First Eigenvalue comprises at least one in the sex of personage belonging to facial image, age, race.
10. device according to claim 9, is characterized in that, described second acquisition module is used for,
Linear discriminate analysis LDA algorithm is adopted to determine the sex of described each width facial image affiliated personage separately to each width facial image respectively.
11. devices according to claim 9, is characterized in that, described second acquisition module is used for,
Principal component analysis (PCA) PCA method is adopted to extract described each width facial image image feature value separately to each width facial image respectively;
According to described each width facial image described image feature value separately, least square regression algorithm is used to calculate the age of described each width facial image affiliated personage separately.
12. devices according to claim 9, is characterized in that, described second acquisition module is used for,
Extract the features of skin colors value of each width facial image respectively;
According to the features of skin colors value of described each width facial image, support vector machines is adopted to determine the race of described each width facial image affiliated personage separately.
13. devices according to Claim 8 described in-12 any one, it is characterized in that, described fractionation module is used for,
When the First Eigenvalue of described at least two width facial image same types is different or difference exceedes setting range, described cluster set is split as at least two cluster set, and the identical or difference of the First Eigenvalue of each width facial image same type in each cluster set after fractionation is in setting range.
14. devices according to Claim 8 described in-12 any one, it is characterized in that, described device also comprises:
3rd acquisition module, for obtaining several photos;
4th acquisition module, for adopting Face datection algorithm, obtains facial image from each photo;
Cluster module, for adopting hierarchical clustering algorithm, carrying out cluster to the facial image obtained, obtaining at least one cluster set.
The device of 15. 1 kinds of image procossing, is characterized in that, comprising:
Processor;
For the storer of storage of processor executable instruction;
Wherein, described processor is configured to:
Obtain a cluster set, described cluster set comprises at least two width facial images;
Adopt the First Eigenvalue of at least two width facial images described in face recognition algorithms acquisition;
According to the First Eigenvalue of described at least two width facial images, described cluster set is split.
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