CN107563359A - Recognition of face temperature analysis generation method is carried out for dense population - Google Patents
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- 230000008921 facial expression Effects 0.000 claims description 6
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Abstract
The present invention proposes one kind and carries out recognition of face temperature analysis generation method for dense population, including：S1, characteristics of human body's image in dense population and face characteristic image are acquired respectively by image capture module, establish and initially sentence the disconnected model of screening, so as to extract personnel's attribute into crowded region.
Description
Technical field
The present invention relates to big data analysis field, more particularly to one kind to carry out recognition of face temperature analysis for dense population
Generation method.
Background technology
Today's society personnel transfer is frequent, and on market, station, airport etc., stream of people's close quarters has substantial amounts of video monitor
Equipment, but be only for carrying out close quarters simple IMAQ, follow-up classification and differentiation are not carried out to image,
But due to crowded complicated in social life, it is necessary to rationally be advised to the personnel and place in the crowded region that comes in and goes out
Draw, take corresponding management and configuration, so that the food and drink in crowded region, plugging into traffic and gateway can rationally match somebody with somebody
Put, after great amount of images characteristic information is obtained, prior art can not be sorted out or sorted out inaccuracy to it, cause the later stage
When carrying out crowded region division, data sample reference can not be provided, this just needs those skilled in the art badly and solved accordingly
Technical problem.
The content of the invention
It is contemplated that at least solving technical problem present in prior art, one kind is especially innovatively proposed for close
Collection crowd carries out recognition of face temperature analysis generation method.
In order to realize the above-mentioned purpose of the present invention, recognition of face temperature is carried out for dense population the invention provides one kind
Generation method is analyzed, including：
S1, characteristics of human body's image in dense population and face characteristic image are adopted respectively by image capture module
Collection, establish and initially sentence the disconnected model of screening, so as to extract personnel's attribute into crowded region.
Described carries out recognition of face temperature analysis generation method for dense population, it is preferred that the S1 includes：
S1-1, it is assumed that same user into crowded region be all new user, if wherein including attendant or
The personnel frequently passed in and out, in this model it is not intended that, because after the enough sample of collection, attendant or frequently
The quantity of the personnel of disengaging can be ignored, and be left from crowded region, is set as that corresponding personnel's certification terminates, passes through
The image information for obtaining image capture module is judged characteristics of human body's image in image and face characteristic image, sets figure
It is original according to coordinate [x, y] by basic point of the coordinate [x, y] as image as data message coordinate [x, y] progress image acquisition
Point sets scanning weight respectively
Wherein p is that the number in image obtains the factor, carries out extraction of square root computing to four orientation of [x, y] coordinate, n is just
Integer, nvalidFor the efficiently individual quantity decision threshold of acquisition, h (i, j) is the characteristics of human body's image i and face obtained in an orientation
Characteristic image j number,
S1-2, if characteristics of human body's image weights vector acquired in an orientation is bi=A (c-w) × (cw), A are
Human body foundation characteristic c and hand strap article characteristics w probability of occurrence value, cw are that human body foundation characteristic and hand strap article characteristics are common
The definition value of appearance；It is f to obtain an orientation face characteristic image weights vectorj=B × (CT), B are to obtain face characteristic
Probable value, C are face expressive features set, and T is the statistics coefficient for the unit area that face is identified as work(；Wherein C=
{smile,openmouth,downhead,uphead,weeping,halfface}
S1-3, ensure to obtain the stability of information, according to biAnd fjVector value choose multizone sample calculated,
Then preliminary screening formula is passed throughPreliminary screening is carried out to image,
Wherein, λ4For the calculating parameter of j-th of human face expression set of comprehensive i-th of characteristics of human body image in image, β4For in image
The match parameter of j-th of human face expression set of comprehensive i-th of characteristics of human body image, Li,jTotally go out for the personnel in image
Occurrence number, Qi,jFor the conditional probability value in the crowded region during preliminary screening in image, σ2(i, j) is crowded
The judgement Extreme Parameters of area people dense degree, Pi,jFor history demographic's numerical value of stream of people's close quarters；
S1-4, after judging by above-mentioned primary dcreening operation, classification judgement is carried out to characteristics of image, by different face expressive features collection
The view data for closing C carries out model judgement；The histogram of effective characteristics of human body's image is extracted, constructs texture information, people is obtained and connects
Each property value in expressive features set,
Smile property value Csmile=Σjj·δxj·δyj, wherein δxjAnd δyjRespectively the X-axis smile characteristics factor and Y-axis are special
Levy the factor；
Open one's mouth property value Copenmouth=Σjj·τxjτyj, wherein τxjAnd τyjRespectively X-axis is opened one's mouth characterization factor and Y-axis
Mouth characterization factor；
Bow property value Cdownhead=Σjj·βxjβyj, wherein βxjAnd βyjRespectively X-axis bow characterization factor and Y-axis it is low
Head characterization factor；
New line property value Cuphead=∑jj·εxj·εyj, wherein εxjAnd εyjRespectively X-axis new line characterization factor and Y-axis lift
Head characterization factor；
Sobbing property valueWhereinWithRespectively X-axis sobbing characterization factor and Y
Axle sobbing characterization factor；
Side face property value Chalfface=∑jj·μxj·μyj, wherein μxjAnd μyjRespectively X-axis side face characterization factor and Y-axis
Side face characterization factor；
Primary dcreening operation is repeated, until caused repetitive rate rising after, terminate S1-1 to S1-3 the step of.
In summary, by adopting the above-described technical solution, the beneficial effects of the invention are as follows：
After the present invention to image by being acquired, according to the facial information of personnel and crowded region is passed in and out
The bodily form and wearing difference are classified, perfect so as to which the corresponding auxiliary facility in the crowded region is carried out, and are passed through
The sorter model is classified, and consuming system resource is small, saves time overhead, so as to provide conjunction for stream of people's close quarters
The allocation plan of reason, be advantageous to personnel and dredge and personnel re-assignment.
The additional aspect and advantage of the present invention will be set forth in part in the description, and will partly become from the following description
Obtain substantially, or recognized by the practice of the present invention.
Brief description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment
Substantially and it is readily appreciated that, wherein：
Fig. 1 is general illustration of the present invention.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, is only used for explaining the present invention, and is not considered as limiting the invention.
In the description of the invention, it is to be understood that term " longitudinal direction ", " transverse direction ", " on ", " under ", "front", "rear",
The orientation or position relationship of the instruction such as "left", "right", " vertical ", " level ", " top ", " bottom ", " interior ", " outer " is based on accompanying drawing institutes
The orientation or position relationship shown, it is for only for ease of the description present invention and simplifies description, rather than instruction or the dress for implying meaning
Put or element there must be specific orientation, with specific azimuth configuration and operation, therefore it is not intended that to limit of the invention
System.
In the description of the invention, unless otherwise prescribed with limit, it is necessary to explanation, term " installation ", " connected ",
" connection " should be interpreted broadly, for example, it may be mechanical connection or electrical connection or the connection of two element internals, can
To be to be joined directly together, can also be indirectly connected by intermediary, for the ordinary skill in the art, can basis
Concrete condition understands the concrete meaning of above-mentioned term.
As shown in figure 1, the inventive method comprises the following steps：
S1, characteristics of human body's image in dense population and face characteristic image are adopted respectively by image capture module
Collection, establish and initially sentence the disconnected model of screening, so as to extract personnel's attribute into crowded region；
S2, after being judged according to initial screening judgment models characteristics of human body's image and face characteristic image, it will leave
The personnel in crowded region carry out matching collection again, by grader distinguish region that corresponding dense population reached or
The respective nodes that person leaves, so as to be pushed to terminal.
S1-1, it is assumed that same user into crowded region be all new user, if wherein including attendant or
The personnel frequently passed in and out, in this model it is not intended that, because after the enough sample of collection, attendant or frequently
The quantity of the personnel of disengaging can be ignored, and be left from crowded region, is set as that corresponding personnel's certification terminates, passes through
The image information for obtaining image capture module is judged characteristics of human body's image in image and face characteristic image, sets figure
It is original according to coordinate [x, y] by basic point of the coordinate [x, y] as image as data message coordinate [x, y] progress image acquisition
Point sets scanning weight respectively
Wherein p is that the number in image obtains the factor, carries out extraction of square root computing to four orientation of [x, y] coordinate, n is just
Integer, nvalidFor the efficiently individual quantity decision threshold of acquisition, h (i, j) is the characteristics of human body's image i and face obtained in an orientation
Characteristic image j number,
S1-2, if characteristics of human body's image weights vector acquired in an orientation is bi=A (c-w) × (cw), A are people
Body foundation characteristic c and hand strap article characteristics w probability of occurrence value, cw are that human body foundation characteristic and hand strap article characteristics go out jointly
Existing definition value；It is f to obtain an orientation face characteristic image weights vectorj=B × (CT), B are that acquisition face characteristic is general
Rate value, C are face expressive features set, and T is the statistics coefficient for the unit area that face is identified as work(；Wherein C=smile,
openmouth,downhead,uphead,weeping,halfface}
S1-3, ensure to obtain the stability of information, according to biAnd fjVector value choose multizone sample calculated,
Then preliminary screening formula is passed throughPreliminary screening is carried out to image,
Wherein, λ4For the calculating parameter of j-th of human face expression set of comprehensive i-th of characteristics of human body image in image, β4For in image
The match parameter of j-th of human face expression set of comprehensive i-th of characteristics of human body image, Li,jTotally go out for the personnel in image
Occurrence number, Qi,jFor the conditional probability value in the crowded region during preliminary screening in image, σ2(i, j) is crowded
The judgement Extreme Parameters of area people dense degree, Pi,jFor history demographic's numerical value of stream of people's close quarters；
S1-4, after judging by above-mentioned primary dcreening operation, classification judgement is carried out to characteristics of image, by different face expressive features collection
The view data for closing C carries out model judgement；The histogram of effective characteristics of human body's image is extracted, constructs texture information, people is obtained and connects
Each property value in expressive features set,
Smile property value Csmile=∑jj·δxj·δyj, wherein δxjAnd δyjRespectively the X-axis smile characteristics factor and Y-axis are special
Levy the factor；
Open one's mouth property value Copenmouth=∑jj·τxjτyj, wherein τxjAnd τyjRespectively X-axis is opened one's mouth characterization factor and Y-axis
Mouth characterization factor；
Bow property value Cdownhead=∑jj·βxjβyj, wherein βxjAnd βyjRespectively X-axis bow characterization factor and Y-axis it is low
Head characterization factor；
New line property value Cuphead=∑jj·εxj·εyj, wherein εxjAnd εyjRespectively X-axis new line characterization factor and Y-axis lift
Head characterization factor；
Sobbing property valueWhereinWithRespectively X-axis sobbing characterization factor and Y
Axle sobbing characterization factor；
Side face property value Chalfface=Σjj·μxj·μyj, wherein μxjAnd μyjRespectively X-axis side face characterization factor and Y-axis
Side face characterization factor；
Primary dcreening operation is repeated, until caused repetitive rate rising after, terminate S1-1 to S1-3 the step of；
S2-1, whole crowded region image data is divided, forms besel sequence pair (M1,M2),(M2,
M3),...,(Mn-1,Mn)；The hand-held object boundary of characteristics of human body's image is positioned, since the initial frame head portion of video image；Positioning
The access border of some characteristics of human body's image, from the crowded area that video image tail search characteristics of human body's image occurs
The relevant position in domain, and judge the position that characteristics of human body's image occurs, residence time, and whether do shopping or hold
Article；
S2-2, by besel sequence pair being compared crawl, before and after judgement one characteristics of human body's image of frame of video and
The change degree of face characteristic image
Wherein, wherein | Ei,jLn+Ei,jMn| it is inquiry feature L to be matchednWith besel image MnSimilarity, E representatives
Close quarters matching amount of images is flowed, S represents the interference set for influenceing characteristics of human body's image and face characteristic image, and s, t is just
Integer, s, t value are different, and its minimum value is 1, and maximum occurrences are the characteristics of human body's image matched in matching characteristics of image figure
With face characteristic image number；ωi,jThe weight of degree of correlation total degree, K are matched for face expressive features set CiTo be crowded
Region carries out the penalty factor of characteristics of human body's image error matching, and z and d represent collection set and the people of characteristics of human body's image respectively
The collection set of the next besel of body characteristicses image,
The change degree is subjected to information matches with the crowded regional location residing for corresponding image capture module, obtained
The positive correlation conditional function of crowded regional location and change degree
Wherein, Y (x, y) and Z (x, y) represents to lack between characteristics of human body's image and face characteristic image coordinate point (x, y) respectively
The interaction relationship of mistake, ηiAnd σjCharacteristics of human body's image judgment threshold and face characteristic image judgment threshold are represented respectively, and it is
Positive number in open interval (0,1), rx,yRepresent similar with face characteristic image to characteristics of human body's image of coordinate (x, y) opening position
Degree judges the factor,
S2-3, according to incidence relation between the characteristics of human body's image and face characteristic image of each individual of definition, according to pass
Connection relation produces the non-dominant individual collections of different degree of correlation grades to the degree of correlation and data relevancy ranking is inquired about, according to people
Non-dominant individual amount in body characteristicses image and face characteristic image gradation, sequence number grade it is small to big order from the degree of correlation, such as
Fruit is not matched in the outlet of each stream of people's close quarters with characteristics of human body's image and face characteristic image any feature
Correlation chart picture, step S2-1 is performed, if corresponding crowded regional location obtains correlation chart picture and in relevant position
Signature is carried out, performs step S2-4；
S2-4, crowded zonelog is set, the attribute information in the crowded region is extracted according to user's request, is entered
Row Similarity Measure, similarity is inquired about using characteristics of human body's figure Similarity Measure, calculated and looked into using face characteristic image similarity
Similarity is ask, until daily record similarity and inquiry similarity convergence；The characteristics of human body of acquiescence is balanced by using matching weight α
Image and face characteristic image correlativity and user define degree of correlation weighing result value D [i, j]=maxFi,j(1-α)·P(i,
j)+α·P(i,j,rx,y)+minFi,jWherein, maxFi,jThe maximum of the change degree of characteristics of human body's image and face characteristic image,
minFi,jThe minimum value of the change degree of characteristics of human body's image and face characteristic image, P (i, j) are stream of people's close quarters initial decision
Decision value, P (i, j, rx,y) it is that stream of people's close quarters result judges decision value, rx,yRepresent the human body to coordinate (x, y) opening position
Characteristic image and face characteristic image similarity judge the factor, and wherein initial decision decision value is according to history feature view data
The initial decision of close quarters is carried out, judges that decision value is after being optimized after judging by S2-1 to S2-4 for result
Judge decision value.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment or example of the present invention.In this manual, to the schematic representation of above-mentioned term not
Necessarily refer to identical embodiment or example.Moreover, specific features, structure, material or the feature of description can be any
One or more embodiments or example in combine in an appropriate manner.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of departing from the principle and objective of the present invention a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is limited by claim and its equivalent.
Claims (2)
1. a kind of carry out recognition of face temperature analysis generation method for dense population, it is characterised in that including：
S1, characteristics of human body's image in dense population and face characteristic image are acquired respectively by image capture module, built
It is vertical initially to sentence the disconnected model of screening, so as to extract personnel's attribute into crowded region.
2. according to claim 1 carry out recognition of face temperature analysis generation method for dense population, it is characterised in that
The S1 includes：
S1-1, it is assumed that same user into crowded region be all new user, if wherein including attendant or frequently
The personnel of disengaging, in this model it is not intended that, because after the enough sample of collection, attendant or frequently disengaging
The quantity of personnel can be ignored, left from crowded region, be set as that corresponding personnel's certification terminates, pass through acquisition
The image information of image capture module is judged characteristics of human body's image in image and face characteristic image, sets picture number
Image acquisition is carried out according to information coordinate [x, y], is origin point according to coordinate [x, y] by basic point of the coordinate [x, y] as image
Weight She Zhi not scanned
Wherein p is that the number in image obtains the factor, carries out extraction of square root computing to four orientation of [x, y] coordinate, n is just whole
Number, nvalidFor the efficiently individual quantity decision threshold of acquisition, h (i, j) is the characteristics of human body's image i and face spy obtained in an orientation
Image j number is levied,
S1-2, if characteristics of human body's image weights vector acquired in an orientation is bi=A (c-w) × (cw), A are human body bases
Plinth feature c and hand strap article characteristics w probability of occurrence value, cw are determining of occurring jointly of human body foundation characteristic and hand strap article characteristics
Justice value；It is f to obtain an orientation face characteristic image weights vectorj=B × (CT), B are to obtain face characteristic probable value, C
For face expressive features set, T is the statistics coefficient for the unit area that face is identified as work(；Wherein C=smile,
openmouth,downhead,uphead,weeping,halfface}
S1-3, ensure to obtain the stability of information, according to biAnd fjVector value choose multizone sample calculated, then
Pass through preliminary screening formulaPreliminary screening is carried out to image, its
In, λ4For the calculating parameter of j-th of human face expression set of comprehensive i-th of characteristics of human body image in image, β4To be complete in image
The match parameter of j-th of human face expression set of i-th of orientation characteristics of human body's image, Li,jTotally occur for the personnel in image
Number, Qi,jFor the conditional probability value in the crowded region during preliminary screening in image, σ2(i, j) is stream of people compact district
The judgement Extreme Parameters of domain densely populated place degree, Pi,jFor history demographic's numerical value of stream of people's close quarters；
S1-4, after judging by above-mentioned primary dcreening operation, classification judgement is carried out to characteristics of image, by different face expressive features set C
View data carry out model judgement；The histogram of effective characteristics of human body's image is extracted, constructs texture information, people is obtained and connects expression
Each property value in characteristic set,
Smile property value Csmile=∑jj·δxj·δyj, wherein δxjAnd δyjRespectively the X-axis smile characteristics factor and Y-axis feature because
Son；
Open one's mouth property value Copenmouth=∑jj·τxjτyj, wherein τxjAnd τyjRespectively X-axis opens one's mouth characterization factor and Y-axis is opened one's mouth spy
Levy the factor；
Bow property value Cdownhead=∑jj·βxjβyj, wherein βxjAnd βyjRespectively X-axis bows characterization factor and Y-axis is bowed spy
Levy the factor；
New line property value Cuphead=∑jj·εxj·εyj, wherein εxjAnd εyjRespectively X-axis new line characterization factor and Y-axis come back special
Levy the factor；
Sobbing property valueWhereinWithRespectively X-axis sobbing characterization factor and Y-axis are cried
Tears characterization factor；
Side face property value Chalfface=∑jj·μxj·μyj, wherein μxjAnd μyjRespectively X-axis side face characterization factor and Y-axis side face
Characterization factor；
Primary dcreening operation is repeated, until caused repetitive rate rising after, terminate S1-1 to S1-3 the step of.
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