CN108764106A - Multiple dimensioned colour image human face comparison method based on cascade structure - Google Patents

Multiple dimensioned colour image human face comparison method based on cascade structure Download PDF

Info

Publication number
CN108764106A
CN108764106A CN201810497963.5A CN201810497963A CN108764106A CN 108764106 A CN108764106 A CN 108764106A CN 201810497963 A CN201810497963 A CN 201810497963A CN 108764106 A CN108764106 A CN 108764106A
Authority
CN
China
Prior art keywords
face alignment
decision tree
face
rgb
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201810497963.5A
Other languages
Chinese (zh)
Other versions
CN108764106B (en
Inventor
章东平
井长兴
倪佩青
陈奇
杨力
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Jiliang University
Original Assignee
China Jiliang University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Jiliang University filed Critical China Jiliang University
Priority to CN201810497963.5A priority Critical patent/CN108764106B/en
Publication of CN108764106A publication Critical patent/CN108764106A/en
Application granted granted Critical
Publication of CN108764106B publication Critical patent/CN108764106B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate

Abstract

The present invention proposes that a kind of multiple dimensioned colour image human face comparison method based on cascade structure, this method include：（1）For coloured image RGB triple channels, the facial image in each channel is to carrying out multiple dimensioned face characteristic extraction；（2）For RGB triple channels, face alignment decision tree, the corresponding triple channel face alignment decision tree classifier of training are built respectively；（3）Based on Softcascade cascade structures, joint triple channel face alignment decision tree classifier exports face alignment mathematical expectation of probability, obtains face alignment combined chance value.The present invention compares problem for multiple dimensioned colour image human face, and by extracting across dimension normalization pixel difference feature, training is based on Softcascade cascade structure face alignment decision-tree models, can effectively improve multiple dimensioned colour image human face matching identification rate.

Description

Multiple dimensioned colour image human face comparison method based on cascade structure
Technical field
The present invention is to belong to computer vision, face alignment and identification field, and specific design is for the multiple dimensioned coloured silk of input The face comparison method of color image.
Background technology
Biological characteristic is attribute of people itself, has very strong individual differences and self stability.In recent years, as life Face alignment algorithm in object identification technology, it has many characteristics, such as real-time, substantivity, convenience, is more and more calculated Machine visual field expert and scholar's research.With the development of machine learning algorithm and the extensive use of face alignment, face alignment Problem is just gradually developing into the hot spot of research.It is played in the fields such as automatic monitoring, alarm, authentication and finance activities simultaneously Increasingly important role.
But current face alignment is confined to the processing of gray-scale map on algorithm research, has ignored coloured image in face alignment Research.Multiple dimensioned different size image is cannot achieve during face alignment simultaneously to compare, needed before face alignment by Image loses image information during scaling and amplification and causes distortion etc. and ask to redefining the uniform sizes of image pair Topic, wastes computing resource, and information use is not enough and reduces face alignment discrimination.
Invention content
Present in existing face alignment technology (1) is studied just for gray-scale map and ignores coloured image in face ratio To important function；(2) it needs to redefine uniform sizes in face of different input size pictures, cannot achieve multiple dimensioned face The problems such as comparison, the present invention propose a kind of in face of multiple dimensioned colour image human face comparison method, compared to the prior art, this hair It is bright targetedly to be applied in face in RGB triple channels using decision tree and cascade structure, while being directed to input picture pair Size it is different, it is proposed that a kind of feature extracting method across size realizes multiple dimensioned colour image human face and compares, into The discrimination of face alignment image algorithm is improved and improved to one step.
Multiple dimensioned colour image human face comparison method based on cascade structure, feature include the following steps：
Step 1：For coloured image RGB triple channels, the multiple dimensioned facial image in each channel returns extraction respectively One changes pixel difference feature；
Step 2：Using the normalization pixel difference feature of extraction, face alignment decision tree, the corresponding threeway of training are built respectively Road face alignment decision tree classifier；
Step 3：Based on Softcascade cascade structures, joint triple channel face alignment decision tree output face alignment is general Rate mean value obtains face alignment combined chance value.Complete multiple dimensioned, colour image human face pair face alignment probability output.
Further, step 1 includes the following steps：
Step 1.1：Training data sample set D includes the P positive sample pair and N with label information of different scale size A negative sample pair, for training dataset RGB Three Channel Color image patterns pair, respectively to the multiple dimensioned face in each channel Any two points extraction normalization pixel difference feature of image pattern pair, realizes that across scale feature extraction, formula are as follows：
Wherein, AM×M、BN× N is the RGB color facial image for being respectively M × M sizes and N × N sizes；For R, G, B value that A image RGB respective channel coordinates are x points；For B images RGB Corresponding channel coordinate is R, G, B value of y points；Normalization pixel difference is characterized as m2×n2The feature vector of dimension；F(B)、F(B)、F(B) For the normalization pixel difference feature extracted for the RGB of image A and image B.
Further, step 2 includes the following steps：
Step 2.1：The training dataset RGB triple channels normalization pixel difference feature f (x, y) obtained by step 1, difference needle Face alignment decision tree classifier is trained to RGB triple channels.Assuming that given input normalizes pixel difference feature f, decision leaf Child node exports face alignment similarity probability S.According to split vertexes function, that is, maximum value purity function gamma, pixel will be normalized The split vertexes of poor feature construction decision tree, input feature vector f train dual threshold α by the method for exhaustion1, α2, according to dual threshold α1, α2 Determine the left and right of each split vertexes of decision tree structure
Maximum value purity function gamma enables training sample data collection distinguish positive negative sample to the greatest extent, and formula is as follows：
Wherein, uP、vNTo be split off the number of positive sample P and negative sample N that node is distinguished in decision tree.
It is assumed that in the decision tree for N number of node that depth is κ, wherein there is L leaf node.In each leafy node lL It can indicate the probability distribution s of image similarityl∈S.Each node of decision tree will under the constraint of maximum value purity function gamma Each leaf node exports image similarity probability value S, it is assumed that label is distributed as σ on corresponding leaf nodel, decision tree The face alignment similarity probability value of one training sample pair can be expressed as：
Wherein,
Step 2.2：Mechanism is cascaded based on Softcascade, trained decision tree is cascaded into face alignment strong classifier, And decision tree leaf node face alignment mathematical expectation of probability is exported, formula is as follows：
Wherein, C1、C2、C3For the decision tree number of RGB triple channels training；For RGB channel cascade point The face alignment probability value of i-th of tree of class device output；It is equal for RGB triple channel decision tree face alignment probability Value.
Further, above-mentioned steps 3 include the following steps：
Step 3.1：It is general according to the Softcascade cascade classifier decision tree leaf node face alignments of RGB triple channels Rate mean value, can respectively obtain face alignment combined chance value V isMean value, maximum value or minimum value, Formula difference is as follows：
Wherein,For the face alignment mathematical expectation of probability of RGB triple channel decision trees；L1、L2、L3For RGB The decision tree number of triple channel training.
Step 3.2：By targetedly training the respective face alignment decision tree classifier of triple channel and being cascaded into one Complete strong classifier, it is comprehensive that the face alignment mathematical expectation of probability that comprehensive triple channel face alignment strong classifier exports obtains face alignment Close probability value V, setting face alignment similarity threshold τ.Comprehensive face alignment combined chance value V and face alignment similarity threshold τ, the judgement of face alignment similarity are as follows：
Description of the drawings
Fig. 1 is total techniqueflow chart；
Fig. 2 is Softcascade cascade structure face alignment decision tree structure figures.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.
As shown in Figure 1, the multiple dimensioned colour image human face comparison method based on cascade structure of the present invention, including walk as follows Suddenly：
Step 1：For coloured image RGB triple channels, the multiple dimensioned facial image in each channel returns extraction respectively One changes pixel difference feature.Sub-step is as follows：
Step 1.1：Training data sample set D includes the P positive sample pair and N with label information of different scale size A negative sample pair, for training dataset RGB Three Channel Color image patterns pair, respectively to the multiple dimensioned face in each channel Any two points extraction normalization pixel difference feature of image pattern pair, realizes that across scale feature extraction, formula are as follows：
Wherein, AM×M、BN× N is the RGB color facial image for being respectively M × M sizes and N × N sizes；For R, G, B value that A image RGB respective channel coordinates are x points；For B images RGB Corresponding channel coordinate is R, G, B value of y points；Normalization pixel difference is characterized as m2×n2The feature vector of dimension；F(B)、F(B)、F(B) For the normalization pixel difference feature extracted for the RGB of image A and image B.
Step 2：Using the normalization pixel difference feature of extraction, face alignment decision tree, the corresponding threeway of training are built respectively Road face alignment decision tree classifier.Sub-step is as follows：
Step 2.1：The training dataset RGB triple channels normalization pixel difference feature f (x, y) obtained by step 1, difference needle Face alignment decision tree classifier is trained to RGB triple channels.Assuming that given input normalizes pixel difference feature f, decision leaf Child node exports face alignment similarity probability S.According to split vertexes function, that is, maximum value purity function gamma, pixel will be normalized The split vertexes of poor feature construction decision tree, input feature vector f train dual threshold α by the method for exhaustion1, α2, according to dual threshold α1, α2 It determines that the left and right route of each split vertexes of decision tree structure is distinguished, it is as follows to distinguish mode：
Maximum value purity function gamma enables training sample data collection distinguish positive negative sample to the greatest extent, and formula is as follows：
Wherein, uP、vNTo be split off the number of positive sample P and negative sample N that node is distinguished in decision tree.
It is assumed that in the decision tree for N number of node that depth is κ, wherein there is L leaf node.In each leafy node l ∈ L It can indicate the probability distribution s of image similarityl∈S.Each node of decision tree is under the constraint of maximum value purity function gamma By each leaf node output image similarity probability value S, it is assumed that label is distributed as σ on corresponding leaf nodel, decision Tree can be expressed as the face alignment similarity probability value of a training sample pair：
Wherein,
Step 2.2：Mechanism is cascaded based on Softcascade, trained decision tree is cascaded into face alignment strong classifier, And decision tree leaf node face alignment mathematical expectation of probability is exported, formula is as follows：
Wherein, C1、C2、C3For the decision tree number of RGB triple channels training；For RGB channel cascade point The face alignment probability value of i-th of tree of class device output；It is equal for RGB triple channel decision tree face alignment probability Value.
Step 3：Based on Softcascade cascade structures, joint triple channel face alignment decision tree output face alignment is general Rate mean value obtains face alignment combined chance value.Complete multiple dimensioned, colour image human face pair face alignment probability output.Son Steps are as follows：
Step 3.1：It is general according to the Softcascade cascade classifier decision tree leaf node face alignments of RGB triple channels Rate mean value, can respectively obtain face alignment combined chance value V isMean value, maximum value or minimum value, Formula difference is as follows：
Wherein,For the face alignment mathematical expectation of probability of RGB triple channel decision trees；L1、L2、L3For RGB tri- The decision tree number of channel training.
Step 3.2：By targetedly training the respective face alignment decision tree classifier of triple channel and being cascaded into one Complete strong classifier, it is comprehensive that the face alignment mathematical expectation of probability that comprehensive triple channel face alignment strong classifier exports obtains face alignment Close probability value V, setting face alignment similarity threshold τ.Comprehensive face alignment combined chance value V and face alignment similarity threshold τ, the judgement of face alignment similarity are as follows：

Claims (4)

1. the multiple dimensioned colour image human face comparison method based on cascade structure, including steps are as follows：
Step 1：For coloured image RGB triple channels, the multiple dimensioned facial image in each channel normalizes extraction respectively Pixel difference feature；
Step 2：Using the normalization pixel difference feature of extraction, face alignment decision tree, the corresponding threeway Taoist of training are built respectively Face compares decision tree classifier；
Step 3：Based on Softcascade cascade structures, joint triple channel face alignment decision tree output face alignment probability is equal Value, obtains face alignment combined chance value.Complete multiple dimensioned, colour image human face pair face alignment probability output.
2. the multiple dimensioned colour image human face comparison method according to claim 1 based on cascade structure, it is characterised in that： It is as follows in above-mentioned steps 1：
Step 1.1：Training data sample set D includes that the P positive sample pair with label information of different scale size is born with N number of Sample pair, for training dataset RGB Three Channel Color image patterns pair, respectively to the multiple dimensioned facial image in each channel Any two points extraction normalization pixel difference feature of sample pair, realizes that across scale feature extraction, formula are as follows：
Wherein, AM×M、BN× N is the RGB color facial image for being respectively M × M sizes and N × N sizes；
For R, G, B value that A image RGB respective channel coordinates are x points；
For R, G, B value that B image RGB corresponding channel coordinates are y points；Normalization pixel difference is characterized as m2× n2The feature vector of dimension；F(B)、F(B)、F(B)For the normalization pixel difference feature extracted for the RGB of image A and image B.
3. the multiple dimensioned colour image human face comparison method according to claim 1 based on cascade structure, it is characterised in that： It is as follows in above-mentioned steps 2：
Step 2.1：The training dataset RGB triple channels normalization pixel difference feature f (x, y) obtained by step 1, is directed to respectively RGB triple channels train face alignment decision tree classifier.Assuming that given input normalizes pixel difference feature f, decision leaf Node exports face alignment similarity probability S.According to split vertexes function, that is, maximum value purity function gamma, pixel difference will be normalized The split vertexes of feature construction decision tree, input feature vector f train dual threshold α by the method for exhaustion1, α2, according to dual threshold α1, α2Really The left and right route for determining each split vertexes of decision tree structure is distinguished, and it is as follows to distinguish mode：
Maximum value purity function gamma enables training sample data collection distinguish positive negative sample to the greatest extent, and formula is as follows：
Wherein, uP、vNTo be split off the number of positive sample P and negative sample N that node is distinguished in decision tree；
It is assumed that in the decision tree for N number of node that depth is κ, wherein there is L leaf node.Each leafy node lL can Indicate the probability distribution s of image similarityl∈S.Each node of decision tree will be each under the constraint of maximum value purity function gamma A leaf node output image similarity probability value S, it is assumed that label is distributed as σ on corresponding leaf nodel, decision tree pair one The face alignment similarity probability value of a training sample pair can be expressed as：
Wherein,
∑l∈Lσl(f | γ, h)=1；
Step 2.2：Mechanism is cascaded based on Softcascade, trained decision tree is cascaded into face alignment strong classifier, and defeated Go out decision tree leaf node face alignment mathematical expectation of probability, formula is as follows：
Wherein, C1、C2、C3For the decision tree number of RGB triple channels training；For RGB channel cascade classifier The face alignment probability value of i-th of tree output；For RGB triple channel decision tree face alignment mathematical expectation of probability.
4. the multiple dimensioned colour image human face comparison method according to claim 1 based on cascade structure, it is characterised in that： It is as follows in above-mentioned steps 3：
Step 3.1：It is equal according to the Softcascade cascade classifier decision tree leaf node face alignment probability of RGB triple channels Value, can respectively obtain face alignment combined chance value V isMean value, maximum value or minimum value, formula It is as follows respectively：
Wherein,For the face alignment mathematical expectation of probability of RGB triple channel decision trees；L1、L2、L3For RGB triple channels Trained decision tree number.
Step 3.2：By targetedly training the respective face alignment decision tree classifier of triple channel and being cascaded into one completely Strong classifier, it is general that the face alignment mathematical expectation of probability of comprehensive triple channel face alignment strong classifier output obtains face alignment synthesis Rate value V, setting face alignment similarity threshold τ.Comprehensive face alignment combined chance value V and face alignment similarity threshold τ, The judgement of face alignment similarity is as follows：
CN201810497963.5A 2018-05-22 2018-05-22 Multi-scale color image face comparison method based on cascade structure Active CN108764106B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810497963.5A CN108764106B (en) 2018-05-22 2018-05-22 Multi-scale color image face comparison method based on cascade structure

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810497963.5A CN108764106B (en) 2018-05-22 2018-05-22 Multi-scale color image face comparison method based on cascade structure

Publications (2)

Publication Number Publication Date
CN108764106A true CN108764106A (en) 2018-11-06
CN108764106B CN108764106B (en) 2021-12-21

Family

ID=64004809

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810497963.5A Active CN108764106B (en) 2018-05-22 2018-05-22 Multi-scale color image face comparison method based on cascade structure

Country Status (1)

Country Link
CN (1) CN108764106B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210374386A1 (en) * 2017-03-24 2021-12-02 Stripe, Inc. Entity recognition from an image

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080240504A1 (en) * 2007-03-29 2008-10-02 Hewlett-Packard Development Company, L.P. Integrating Object Detectors
CN104966085A (en) * 2015-06-16 2015-10-07 北京师范大学 Remote sensing image region-of-interest detection method based on multi-significant-feature fusion
CN106599918A (en) * 2016-12-13 2017-04-26 开易（深圳）科技有限公司 Vehicle tracking method and system
CN107506702A (en) * 2017-08-08 2017-12-22 江西高创保安服务技术有限公司 Human face recognition model training and test system and method based on multi-angle

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080240504A1 (en) * 2007-03-29 2008-10-02 Hewlett-Packard Development Company, L.P. Integrating Object Detectors
CN104966085A (en) * 2015-06-16 2015-10-07 北京师范大学 Remote sensing image region-of-interest detection method based on multi-significant-feature fusion
CN106599918A (en) * 2016-12-13 2017-04-26 开易（深圳）科技有限公司 Vehicle tracking method and system
CN107506702A (en) * 2017-08-08 2017-12-22 江西高创保安服务技术有限公司 Human face recognition model training and test system and method based on multi-angle

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
MENGLIN JIANG ET AL.: "Video Copy Detection Using a Soft Cascade of Multimodal Features", 《2012 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210374386A1 (en) * 2017-03-24 2021-12-02 Stripe, Inc. Entity recognition from an image
US11727053B2 (en) * 2017-03-24 2023-08-15 Stripe, Inc. Entity recognition from an image

Also Published As

Publication number Publication date
CN108764106B (en) 2021-12-21

Similar Documents

Publication Publication Date Title
WO2020114118A1 (en) Facial attribute identification method and device, storage medium and processor
US8682029B2 (en) Rule-based segmentation for objects with frontal view in color images
Cao et al. Gender recognition from body
US8385609B2 (en) Image segmentation
CN108268859A (en) A kind of facial expression recognizing method based on deep learning
Islam et al. Performance of SVM, CNN, and ANN with BoW, HOG, and image pixels in face recognition
CN105069400A (en) Face image gender recognition system based on stack type sparse self-coding
CN109657612B (en) Quality sorting system based on facial image features and application method thereof
CN108229458A (en) A kind of intelligent flame recognition methods based on motion detection and multi-feature extraction
CN106485222A (en) A kind of method for detecting human face being layered based on the colour of skin
CN110348322A (en) Human face in-vivo detection method and equipment based on multi-feature fusion
Chen et al. Skin color modeling for face detection and segmentation: a review and a new approach
Indi et al. Detection of malpractice in e-exams by head pose and gaze estimation
CN103440510A (en) Method for positioning characteristic points in facial image
Yao et al. Face detection and location based on skin chrominance and lip chrominance transformation from color images
CN108509920A (en) The face identification method of the multichannel combined feature selecting study of more patch based on CNN
CN106845328A (en) A kind of Intelligent human-face recognition methods and system based on dual camera
CN109190561A (en) Face identification method and system in a kind of video playing
CN108985200A (en) A kind of In vivo detection algorithm of the non-formula based on terminal device
CN115527269B (en) Intelligent human body posture image recognition method and system
Shah et al. Multi-camera handoff for person re-identification
CN108764106A (en) Multiple dimensioned colour image human face comparison method based on cascade structure
CN108446639A (en) Low-power consumption augmented reality equipment
KR102617756B1 (en) Apparatus and Method for Tracking Missing Person based on Attribute
CN106548130A (en) A kind of video image is extracted and recognition methods and system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant