CN102999751A - Scale-invariant feature transform (SIFT) feature based method for identifying eyebrows - Google Patents

Scale-invariant feature transform (SIFT) feature based method for identifying eyebrows Download PDF

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Publication number
CN102999751A
CN102999751A CN201310003415XA CN201310003415A CN102999751A CN 102999751 A CN102999751 A CN 102999751A CN 201310003415X A CN201310003415X A CN 201310003415XA CN 201310003415 A CN201310003415 A CN 201310003415A CN 102999751 A CN102999751 A CN 102999751A
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Prior art keywords
eyebrow
zone
similarity
sift
same person
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Chinese (zh)
Inventor
曹杰
许野平
方亮
刘辰飞
张传峰
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SHANDONG SYNTHESIS ELECTRONIC TECHNOLOGY Co Ltd
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SHANDONG SYNTHESIS ELECTRONIC TECHNOLOGY Co Ltd
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Abstract

The invention discloses an SIFT feature based method for identifying eyebrows. The method comprises the steps of a). collecting face pictures; b). selecting eyebrow regions A; c). dividing eyebrow regions into M subregions; d). obtaining SIFT feature matrixes A<j>(m<j>, w) of subregions; e). obtaining SIFT feature matrixes A1<j>(m<j>, w) and A2<j>(n<j>, w) of eyebrow regions of two pictures, and calculating similarities S<j> of corresponding subregions; f). conducting statistics on probability distribution that whether eyebrows belong to the same person or not; and g). obtaining P(same) according to a Bayes formula to judge whether two comparison pictures originate from the same person. The method for identifying eyebrows is characterized in that rotation and scale changes of pictures are kept unchanged. SIFT features are used for identifying eyebrows, the influence of illumination and posture changes can be effectively reduced, and no human is involved.

Description

A kind of eyebrow recognition method based on the SIFT feature
Technical field
The present invention relates to a kind of eyebrow recognition method based on the SIFT feature, relate to the Digital Image Processing application.
Background technology
Eyebrow is feature important in recognition of face, than people's face further feature, has better stability and otherness, but usually is subject to the impact of the factors such as illumination, attitude.
(publication number: 1645406) the RGB color component of each pixel of use eyebrow is poor is basis of characterization to patent " authentication identifying method based on eyebrow recognition ".The method is subject to the impact of illumination and attitude larger, and particularly, under the sidelight condition, recognition effect is poor.
Patent " eyebrow image identifying method based on the subregion coupling " (publication number: 101901353A) the manual pure eyebrow image of choosing each user, template as each user, use eyebrow image to be identified to carry out convolution algorithm with the eyebrow template of having preserved successively, obtain being identified after similarity.Although the method can reduce the some effects of illumination and attitude, the process of choosing by hand pure eyebrow image is very complicated loaded down with trivial details.
Summary of the invention
The present invention, in order to overcome the shortcoming of above-mentioned technical matters, provides a kind of eyebrow recognition method based on the SIFT feature that can effectively reduce illumination, attitude variable effect.
Eyebrow recognition method based on the SIFT feature of the present invention, its special feature is, comprises the following steps: a). gather human face photo, the establishment capacity is The human face photo storehouse, wherein The number of people in face database,
Figure 201310003415X100002DEST_PATH_IMAGE006
Mean the
Figure 201310003415X100002DEST_PATH_IMAGE008
The human face photo of individual under different shooting conditions, wherein 0<
Figure 73162DEST_PATH_IMAGE008
≤ B). choose the eyebrow zone
Figure 201310003415X100002DEST_PATH_IMAGE010
, choose human face photo
Figure 201310003415X100002DEST_PATH_IMAGE012
The eyebrow zone
Figure 583089DEST_PATH_IMAGE010
, the zone that utilizes it to calculate as the human face photo similarity; C). divide subregion, by the eyebrow zone Be divided into
Figure 201310003415X100002DEST_PATH_IMAGE014
Sub regions, subregion is used
Figure 201310003415X100002DEST_PATH_IMAGE016
Mean; Common factor between different subregions can be sky, can not be also empty; Indicate The of zone
Figure 201310003415X100002DEST_PATH_IMAGE018
Sub regions, 0<
Figure 445948DEST_PATH_IMAGE018
≤
Figure 644848DEST_PATH_IMAGE014
D). obtain the SIFT eigenmatrix, utilize the SIFT algorithm to obtain the eyebrow zone
Figure 251410DEST_PATH_IMAGE010
The SIFT eigenmatrix of sub regions
Figure 201310003415X100002DEST_PATH_IMAGE020
, Mean of regional A The characteristic point number that sub regions is extracted,
Figure 201310003415X100002DEST_PATH_IMAGE024
The dimension that means the SIFT eigenmatrix;
Figure 362214DEST_PATH_IMAGE020
Mean that line number is
Figure 823282DEST_PATH_IMAGE022
, columns is
Figure 190810DEST_PATH_IMAGE024
Matrix; E). calculate the similarity of two human face photos, for two human face photos
Figure 201310003415X100002DEST_PATH_IMAGE026
With , all according to step b), c) and d) obtain respectively the SIFT eigenmatrix
Figure 201310003415X100002DEST_PATH_IMAGE030
With
Figure 201310003415X100002DEST_PATH_IMAGE032
Calculate the matrix of corresponding subregion
Figure 744020DEST_PATH_IMAGE030
With
Figure 956826DEST_PATH_IMAGE032
Similarity between any two row, form matrix by all similarity values
Figure 201310003415X100002DEST_PATH_IMAGE034
, and definition ,
Figure 201310003415X100002DEST_PATH_IMAGE038
Mean photo
Figure 965409DEST_PATH_IMAGE026
With
Figure 612160DEST_PATH_IMAGE028
The similarity of corresponding j sub regions, its size is got matrix The maximum of middle all elements; F). the probability distribution of statistics similarity, with photo eyebrow zone in twos The similarity of subregion be sample, the similarity of each sub regions of statistics eyebrow
Figure 170071DEST_PATH_IMAGE038
Be same person eyebrow and the probability distribution while not being the same person eyebrow; G). calculate the similar probability in two contrast images eyebrow zones
Figure 201310003415X100002DEST_PATH_IMAGE040
, according to step b), c), d) and e) calculate two and treat contrast images
Figure 754767DEST_PATH_IMAGE014
The similarity of sub regions ,
Figure 705406DEST_PATH_IMAGE042
...,
Figure 24129DEST_PATH_IMAGE038
, by the probability distribution in step f, obtain
Figure 201310003415X100002DEST_PATH_IMAGE044
With , Mean corresponding the
Figure 201310003415X100002DEST_PATH_IMAGE048
When sub regions is the eyebrow of same person, similarity is Probable value;
Figure 448441DEST_PATH_IMAGE046
Mean corresponding the When sub regions is not the eyebrow of same person, similarity is
Figure 341628DEST_PATH_IMAGE038
Probable value, 0<
Figure 766662DEST_PATH_IMAGE018
≤
Figure 629576DEST_PATH_IMAGE014
Obtain under each similarity the probability that two contrast images are the same person eyebrow according to Bayesian formula:
Figure 201310003415X100002DEST_PATH_IMAGE050
Figure 201310003415X100002DEST_PATH_IMAGE052
；
The probable value that is the same person eyebrow according to two contrast images of obtaining, can judge whether two contrast images come from same person.
Step e) in, corresponding subregion should be the eyebrow zone of two images according to identical subregion division methods, the corresponding part of obtaining, only have corresponding part just to have meaning and necessity of calculating similarity.In this step, due to
Figure 117250DEST_PATH_IMAGE036
, calculate
Figure 116430DEST_PATH_IMAGE030
With The matrix that similarity between any two row is obtained
Figure 127822DEST_PATH_IMAGE034
In, no matter how element is arranged, and can not affect similarity
Figure 529722DEST_PATH_IMAGE038
The size of numerical value.Step f), in, the similarity of take is obtained as sample Be same person eyebrow and the probability distribution while not being the same person eyebrow; Like this, in step g) in, by step f) in the probability distribution function that obtains can calculate
Figure 838661DEST_PATH_IMAGE044
With
Figure 676167DEST_PATH_IMAGE046
Numerical value.Step g) can set threshold values in, when two contrast images of the obtaining similar probable value that is the same person eyebrow is greater than while setting threshold values, think that two images come from same people; If be less than while setting threshold values, think that two images are not same people's images.
Eyebrow recognition method based on the SIFT feature of the present invention, step b) the eyebrow zone of choosing in
Figure 179961DEST_PATH_IMAGE010
For left eyebrow zone, right eyebrow zone or whole eyebrows zone, establish the eyebrow zone of choosing Width, highly be respectively
Figure 201310003415X100002DEST_PATH_IMAGE054
, , it comprises the following steps: b-1). and the location pupil position adopts pupil positioning method location human face photo
Figure 219034DEST_PATH_IMAGE012
Pupil position, the line of take between two pupils as
Figure 201310003415X100002DEST_PATH_IMAGE058
Axle is set up plane right-angle coordinate, and the coordinate of establishing left and right pupil is respectively , B-2). ask for interocular distance, according to formula , ask for the distance between two pupils; B-3) if. the zone
Figure 164076DEST_PATH_IMAGE010
During for left eyebrow zone, the eyebrow zone of choosing Width
Figure 201310003415X100002DEST_PATH_IMAGE066
, highly ,
Figure 611423DEST_PATH_IMAGE010
The regional center point coordinate is If regional
Figure 346161DEST_PATH_IMAGE010
During for right eyebrow zone, the eyebrow zone of choosing
Figure 408794DEST_PATH_IMAGE010
Width
Figure 21434DEST_PATH_IMAGE066
, highly
Figure 32115DEST_PATH_IMAGE068
,
Figure 937754DEST_PATH_IMAGE010
The regional center point coordinate is
Figure 201310003415X100002DEST_PATH_IMAGE072
If regional
Figure 220837DEST_PATH_IMAGE010
During for whole eyebrows zone, the eyebrow zone of choosing
Figure 932441DEST_PATH_IMAGE010
Width
Figure 201310003415X100002DEST_PATH_IMAGE074
, highly
Figure 118002DEST_PATH_IMAGE068
, The regional center point coordinate is
Figure 201310003415X100002DEST_PATH_IMAGE076
Wherein,
Figure 201310003415X100002DEST_PATH_IMAGE078
,
Figure 201310003415X100002DEST_PATH_IMAGE080
,
Figure 201310003415X100002DEST_PATH_IMAGE082
,
Figure 201310003415X100002DEST_PATH_IMAGE084
Be constant.
Wherein,
Figure 843217DEST_PATH_IMAGE078
=0.625, =0.391,
Figure 953573DEST_PATH_IMAGE082
=1.563,
Figure 263332DEST_PATH_IMAGE084
=0.281.
Eyebrow recognition method based on the SIFT feature of the present invention, described step e) matrix of corresponding subregion in
Figure 201310003415X100002DEST_PATH_IMAGE086
With
Figure 201310003415X100002DEST_PATH_IMAGE088
Between any two row, the computing method of similarity are Euclidean distance, mahalanobis distance or inner product of vectors algorithm.Euclidean distance, mahalanobis distance or inner product of vectors are the existing method of calculating similarity.
Eyebrow recognition method based on the SIFT feature of the present invention, step f) in, the probability distribution of statistics similarity is discrete model or continuous model; Be under the situation of discrete model, obtaining by adding up the probable value that each similarity value drops on each numerical value interval; Be under the situation of continuous model, using the mixed Gaussian probabilistic Modeling, matching formation probability density function.
Eyebrow recognition method based on the SIFT feature of the present invention, described step is middle capacity a)
Figure 201310003415X100002DEST_PATH_IMAGE090
=2000,
Figure 583323DEST_PATH_IMAGE004
=200,
Figure 636730DEST_PATH_IMAGE006
=10; Described different shooting condition refers to different attitudes, different light.
The invention has the beneficial effects as follows: the eyebrow recognition method based on the SIFT feature of the present invention, human face photo storehouse under the different shooting conditions of the some people of model, the eyebrow zone is divided into to subregion and adopts the SIFT eigenmatrix to calculate the regional similarity of taking an X-ray in twos, then take similarity as Sample Establishing be same person eyebrow and the probability distribution while not being the same person eyebrow; Finally, according to Bayesian formula, can obtain the probability that two contrast images are the same person eyebrow, by the probability size, can judge whether two images come from same people.
Yardstick invariant features conversion SIFT algorithm, be applicable to, in the feature description and characteristic matching of rigid objects, to have the rotation to image, the characteristic that dimensional variation remains unchanged.Use the SIFT feature to carry out eyebrow recognition, can effectively reduce the impact of illumination, attitude variation, and not need artificial participation.
The accompanying drawing explanation
The similarity that Fig. 1 is the eyebrow all subregion
Figure 915658DEST_PATH_IMAGE038
Probability distribution when being the same person eyebrow;
The similarity that Fig. 2 is the eyebrow all subregion
Figure 334001DEST_PATH_IMAGE038
Probability distribution when not being the same person eyebrow.
Embodiment
Below in conjunction with accompanying drawing and embodiment, the invention will be further described.
Eyebrow recognition method based on the SIFT feature of the present invention comprises the following steps:
A). gather human face photo, the establishment capacity is
Figure DEST_PATH_IMAGE091
The human face photo storehouse, wherein
Figure 875710DEST_PATH_IMAGE004
The number of people in face database,
Figure 670490DEST_PATH_IMAGE006
Mean the
Figure 364777DEST_PATH_IMAGE008
The human face photo of individual under different shooting conditions, wherein 0<
Figure 954021DEST_PATH_IMAGE008
≤
Figure 364450DEST_PATH_IMAGE004
This step is middle capacity a)
Figure 388687DEST_PATH_IMAGE090
Can be chosen for 2000,
Figure 388347DEST_PATH_IMAGE004
=200,
Figure 476389DEST_PATH_IMAGE006
=10; Described different shooting condition refers to different attitudes, different light;
B). choose the eyebrow zone
Figure 681105DEST_PATH_IMAGE010
, choose human face photo
Figure 129273DEST_PATH_IMAGE012
The eyebrow zone , the zone that utilizes it to calculate as the human face photo similarity;
This step b) the eyebrow zone of choosing in
Figure 791515DEST_PATH_IMAGE010
Can, for left eyebrow zone, right eyebrow zone or whole eyebrows zone, establish the eyebrow zone of choosing
Figure 483528DEST_PATH_IMAGE010
Width, highly be respectively
Figure 535054DEST_PATH_IMAGE054
,
Figure 996122DEST_PATH_IMAGE056
, it comprises the following steps:
B-1). the location pupil position adopts pupil positioning method location human face photo
Figure 425967DEST_PATH_IMAGE012
Pupil position, the line of take between two pupils as
Figure 588964DEST_PATH_IMAGE058
Axle is set up plane right-angle coordinate, and the coordinate of establishing left and right pupil is respectively
Figure DEST_PATH_IMAGE092
,
Figure DEST_PATH_IMAGE062A
B-2). ask for interocular distance, according to formula
Figure DEST_PATH_IMAGE064A
, ask for the distance between two pupils;
B-3) if. the zone
Figure 194913DEST_PATH_IMAGE010
During for left eyebrow zone, the eyebrow zone of choosing
Figure 776067DEST_PATH_IMAGE010
Width
Figure 111233DEST_PATH_IMAGE066
, highly ,
Figure 346399DEST_PATH_IMAGE010
The regional center point coordinate is
Figure 657425DEST_PATH_IMAGE070
If regional During for right eyebrow zone, the eyebrow zone of choosing Width
Figure 698434DEST_PATH_IMAGE066
, highly
Figure 178482DEST_PATH_IMAGE068
,
Figure 121030DEST_PATH_IMAGE010
The regional center point coordinate is If regional
Figure 917265DEST_PATH_IMAGE010
During for whole eyebrows zone, the eyebrow zone of choosing Width , highly
Figure 851351DEST_PATH_IMAGE068
,
Figure 75659DEST_PATH_IMAGE010
The regional center point coordinate is
Figure 278101DEST_PATH_IMAGE076
Wherein,
Figure 562452DEST_PATH_IMAGE078
,
Figure 974979DEST_PATH_IMAGE080
,
Figure 675081DEST_PATH_IMAGE082
,
Figure 292882DEST_PATH_IMAGE084
Be constant; Especially =0.625,
Figure 585640DEST_PATH_IMAGE080
=0.391,
Figure 89434DEST_PATH_IMAGE082
=1.563,
Figure 63206DEST_PATH_IMAGE084
=0.281.
C). divide subregion, by the eyebrow zone
Figure 128508DEST_PATH_IMAGE010
Be divided into
Figure 515627DEST_PATH_IMAGE014
Sub regions, subregion is used
Figure 557532DEST_PATH_IMAGE016
Mean; Common factor between different subregions can be sky, can not be also empty;
Figure 651390DEST_PATH_IMAGE016
Indicate The of zone
Figure 994964DEST_PATH_IMAGE018
Sub regions, 0<
Figure 604674DEST_PATH_IMAGE018
≤
Figure 490722DEST_PATH_IMAGE014
D). obtain the SIFT eigenmatrix, utilize the SIFT algorithm to obtain the eyebrow zone
Figure 554810DEST_PATH_IMAGE014
The SIFT eigenmatrix of sub regions
Figure DEST_PATH_IMAGE093
, Mean of regional A
Figure 457835DEST_PATH_IMAGE018
The unique point number that sub regions is extracted, The dimension that means the SIFT eigenmatrix; Mean that line number is
Figure 305202DEST_PATH_IMAGE022
, columns is
Figure 726694DEST_PATH_IMAGE024
Matrix;
E). calculate the similarity of two human face photos, for two human face photos
Figure 36452DEST_PATH_IMAGE026
With
Figure 44860DEST_PATH_IMAGE028
, all according to step b), c) and d) obtain respectively the SIFT eigenmatrix
Figure DEST_PATH_IMAGE094
With
Figure DEST_PATH_IMAGE095
Calculate the matrix of corresponding subregion
Figure 973633DEST_PATH_IMAGE094
With
Figure 813413DEST_PATH_IMAGE095
Similarity between any two row, form matrix by all similarity values
Figure DEST_PATH_IMAGE096
, and definition
Figure DEST_PATH_IMAGE097
,
Figure 608587DEST_PATH_IMAGE038
Mean photo
Figure 274929DEST_PATH_IMAGE026
With
Figure 7393DEST_PATH_IMAGE028
The similarity of corresponding j sub regions, its size is got matrix The maximal value of middle all elements;
This step e) in, the matrix of corresponding subregion
Figure 290924DEST_PATH_IMAGE086
With
Figure 336240DEST_PATH_IMAGE088
Between any two row, the computing method of similarity are Euclidean distance, mahalanobis distance or inner product of vectors algorithm;
F). the probability distribution of statistics similarity, with photo eyebrow zone in twos The similarity of subregion be sample, the similarity of each sub regions of statistics eyebrow is being same person eyebrow and the probability distribution while not being the same person eyebrow;
This step f), in, the probability distribution of statistics similarity is discrete model or continuous model; Be under the situation of discrete model, obtaining by adding up the probable value that each similarity value drops on each numerical value interval; Be under the situation of continuous model, using the mixed Gaussian probabilistic Modeling, matching formation probability density function; As depicted in figs. 1 and 2, provided respectively similarity
Figure 594757DEST_PATH_IMAGE038
Be same person eyebrow and the Probability Distribution Fitting formation probability density function image while not being the same person eyebrow;
G). calculate the similar probability in two contrast images eyebrow zones
Figure DEST_PATH_IMAGE098
, according to step b), c), d) and e) calculate two and treat contrast images
Figure 620481DEST_PATH_IMAGE014
The similarity of sub regions
Figure 323733DEST_PATH_IMAGE042
,
Figure 257054DEST_PATH_IMAGE042
..., , by the probability distribution in step f, obtain
Figure DEST_PATH_IMAGE100
With
Figure DEST_PATH_IMAGE102
,
Figure 437073DEST_PATH_IMAGE100
Mean corresponding the
Figure 801189DEST_PATH_IMAGE048
When sub regions is the eyebrow of same person, similarity is
Figure 741463DEST_PATH_IMAGE038
Probable value;
Figure 701067DEST_PATH_IMAGE102
Mean corresponding the When sub regions is not the eyebrow of same person, similarity is
Figure 920007DEST_PATH_IMAGE038
Probable value, 0<
Figure 460710DEST_PATH_IMAGE018
≤
Figure 141397DEST_PATH_IMAGE014
Obtain under each similarity the probability that two contrast images are the same person eyebrow according to Bayesian formula:
Figure DEST_PATH_IMAGE103
Figure DEST_PATH_IMAGE105
；
The probable value that is the same person eyebrow according to two contrast images of obtaining, can judge whether two contrast images come from same person.

Claims (6)

1. the eyebrow recognition method based on the SIFT feature, is characterized in that, comprises the following steps:
A). gather human face photo, the establishment capacity is
Figure 201310003415X100001DEST_PATH_IMAGE002
The human face photo storehouse, wherein
Figure 201310003415X100001DEST_PATH_IMAGE004
The number of people in face database,
Figure 201310003415X100001DEST_PATH_IMAGE006
Mean the The human face photo of individual under different shooting conditions, wherein 0<
Figure 407197DEST_PATH_IMAGE008
≤
Figure 808222DEST_PATH_IMAGE004
B). choose the eyebrow zone
Figure 201310003415X100001DEST_PATH_IMAGE010
, choose human face photo
Figure 201310003415X100001DEST_PATH_IMAGE012
The eyebrow zone
Figure 467130DEST_PATH_IMAGE010
, the zone that utilizes it to calculate as the human face photo similarity;
C). divide subregion, by the eyebrow zone
Figure 965107DEST_PATH_IMAGE010
Be divided into
Figure 201310003415X100001DEST_PATH_IMAGE014
Sub regions, subregion is used
Figure 201310003415X100001DEST_PATH_IMAGE016
Mean; Common factor between different subregions can be sky, can not be also empty;
Figure 782760DEST_PATH_IMAGE016
Indicate
Figure 936660DEST_PATH_IMAGE010
The of zone
Figure 201310003415X100001DEST_PATH_IMAGE018
Sub regions, 0< ≤
D). obtain the SIFT eigenmatrix, utilize the SIFT algorithm to obtain the eyebrow zone
Figure 723985DEST_PATH_IMAGE010
Figure 161919DEST_PATH_IMAGE014
The SIFT eigenmatrix of sub regions
Figure 201310003415X100001DEST_PATH_IMAGE020
,
Figure 201310003415X100001DEST_PATH_IMAGE022
Mean of regional A The unique point number that sub regions is extracted,
Figure 201310003415X100001DEST_PATH_IMAGE024
The dimension that means the SIFT eigenmatrix;
Figure 307303DEST_PATH_IMAGE020
Mean that line number is
Figure 732337DEST_PATH_IMAGE022
, columns is
Figure 657568DEST_PATH_IMAGE024
Matrix;
E). calculate the similarity of two human face photos, for two human face photos
Figure 201310003415X100001DEST_PATH_IMAGE026
With
Figure 201310003415X100001DEST_PATH_IMAGE028
, all according to step b), c) and d) obtain respectively the SIFT eigenmatrix
Figure 201310003415X100001DEST_PATH_IMAGE030
With
Figure 201310003415X100001DEST_PATH_IMAGE032
Calculate the matrix of corresponding subregion
Figure 196390DEST_PATH_IMAGE030
With
Figure 452360DEST_PATH_IMAGE032
Similarity between any two row, form matrix by all similarity values , and definition ,
Figure 201310003415X100001DEST_PATH_IMAGE038
Mean photo With
Figure 901237DEST_PATH_IMAGE028
The similarity of corresponding j sub regions, its size is got matrix
Figure 929236DEST_PATH_IMAGE034
The maximal value of middle all elements;
F). the probability distribution of statistics similarity, with photo eyebrow zone in twos
Figure 48501DEST_PATH_IMAGE010
The similarity of subregion be sample, the similarity of each sub regions of statistics eyebrow Be same person eyebrow and the probability distribution while not being the same person eyebrow;
G). calculate the similar probability in two contrast images eyebrow zones
Figure 201310003415X100001DEST_PATH_IMAGE040
, according to step b), c), d) and e) calculate two and treat contrast images
Figure 777478DEST_PATH_IMAGE014
The similarity of sub regions
Figure 201310003415X100001DEST_PATH_IMAGE042
, ...,
Figure 631875DEST_PATH_IMAGE038
, by the probability distribution in step f, obtain With
Figure 201310003415X100001DEST_PATH_IMAGE046
,
Figure 195711DEST_PATH_IMAGE044
Mean corresponding the
Figure 201310003415X100001DEST_PATH_IMAGE048
When sub regions is the eyebrow of same person, similarity is Probable value;
Figure 60954DEST_PATH_IMAGE046
Mean corresponding the
Figure 217129DEST_PATH_IMAGE048
When sub regions is not the eyebrow of same person, similarity is
Figure 951867DEST_PATH_IMAGE038
Probable value, 0<
Figure 560702DEST_PATH_IMAGE018
≤
Figure 406299DEST_PATH_IMAGE014
Obtain under each similarity the probability that two contrast images are the same person eyebrow according to Bayesian formula:
Figure 201310003415X100001DEST_PATH_IMAGE050
Figure 201310003415X100001DEST_PATH_IMAGE052
；
The probable value that is the same person eyebrow according to two contrast images of obtaining, can judge whether two contrast images come from same person.
2. the eyebrow recognition method based on the SIFT feature according to claim 1, is characterized in that step b) in the eyebrow zone chosen
Figure 731494DEST_PATH_IMAGE010
For left eyebrow zone, right eyebrow zone or whole eyebrows zone, establish the eyebrow zone of choosing
Figure 699450DEST_PATH_IMAGE010
Width, highly be respectively
Figure 201310003415X100001DEST_PATH_IMAGE054
,
Figure 201310003415X100001DEST_PATH_IMAGE056
, it comprises the following steps:
B-1). the location pupil position adopts pupil positioning method location human face photo
Figure 670948DEST_PATH_IMAGE012
Pupil position, the line of take between two pupils as
Figure 201310003415X100001DEST_PATH_IMAGE058
Axle is set up plane right-angle coordinate, and the coordinate of establishing left and right pupil is respectively
Figure DEST_PATH_IMAGE060
,
Figure DEST_PATH_IMAGE062
B-2). ask for interocular distance, according to formula
Figure DEST_PATH_IMAGE064
, ask for the distance between two pupils;
B-3) if. the zone
Figure 631820DEST_PATH_IMAGE010
During for left eyebrow zone, the eyebrow zone of choosing Width
Figure DEST_PATH_IMAGE066
, highly
Figure DEST_PATH_IMAGE068
,
Figure 17289DEST_PATH_IMAGE010
The regional center point coordinate is
Figure DEST_PATH_IMAGE070
If regional
Figure 525018DEST_PATH_IMAGE010
During for right eyebrow zone, the eyebrow zone of choosing
Figure 977996DEST_PATH_IMAGE010
Width
Figure 963270DEST_PATH_IMAGE066
, highly
Figure 709247DEST_PATH_IMAGE068
,
Figure 766589DEST_PATH_IMAGE010
The regional center point coordinate is
Figure DEST_PATH_IMAGE072
If regional
Figure 819996DEST_PATH_IMAGE010
During for whole eyebrows zone, the eyebrow zone of choosing
Figure 597459DEST_PATH_IMAGE010
Width
Figure DEST_PATH_IMAGE074
, highly
Figure 466668DEST_PATH_IMAGE068
,
Figure 460907DEST_PATH_IMAGE010
The regional center point coordinate is
Figure DEST_PATH_IMAGE076
Wherein,
Figure DEST_PATH_IMAGE078
,
Figure DEST_PATH_IMAGE080
,
Figure DEST_PATH_IMAGE082
,
Figure DEST_PATH_IMAGE084
Be constant.
3. the eyebrow recognition method based on the SIFT feature according to claim 2, is characterized in that: described
Figure 865475DEST_PATH_IMAGE078
=0.625,
Figure 264488DEST_PATH_IMAGE080
=0.391, =1.563,
Figure 571153DEST_PATH_IMAGE084
=0.281.
4. the eyebrow recognition method based on the SIFT feature according to claim 1 and 2, is characterized in that: the matrix of corresponding subregion described step e)
Figure DEST_PATH_IMAGE086
With
Figure DEST_PATH_IMAGE088
Between any two row, the computing method of similarity are Euclidean distance, mahalanobis distance or inner product of vectors algorithm.
5. the eyebrow recognition method based on the SIFT feature according to claim 1 and 2 is characterized in that: step f), the probability distribution of statistics similarity is discrete model or continuous model; Be under the situation of discrete model, obtaining by adding up the probable value that each similarity value drops on each numerical value interval; Be under the situation of continuous model, using the mixed Gaussian probabilistic Modeling, matching formation probability density function.
6. the eyebrow recognition method based on the SIFT feature according to claim 1 and 2 is characterized in that: described step a) in capacity
Figure DEST_PATH_IMAGE090
=2000,
Figure 153313DEST_PATH_IMAGE004
=200,
Figure 282199DEST_PATH_IMAGE006
=10; Described different shooting condition refers to different attitudes, different light.
CN201310003415XA 2013-01-06 2013-01-06 Scale-invariant feature transform (SIFT) feature based method for identifying eyebrows Pending CN102999751A (en)

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