CN107392213A - Human face portrait synthetic method based on the study of the depth map aspect of model - Google Patents
Human face portrait synthetic method based on the study of the depth map aspect of model Download PDFInfo
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Abstract
A kind of human face portrait synthetic method based on the study of the depth map aspect of model.Its step is：(1) sample set is generated；(2) image block set is generated；(3) depth characteristic is extracted；(4) human face portrait reconstructed blocks coefficient is solved；(5) human face portrait block is reconstructed；(6) human face portrait is synthesized.The present invention extracts the depth characteristic of human face photo block using depth convolutional network, depth characteristic figure coefficient and human face portrait block reconstruction coefficients are solved using Markov graph model, human face portrait block weighted sum is obtained using human face portrait block reconstruction coefficients to reconstruct human face portrait block, splicing reconstruct human face portrait block obtains synthesizing human face portrait.The present invention replaces the original pixels value information of image block using the depth characteristic extracted from depth convolutional network, has more preferable robustness to ambient noises such as illumination, can synthesize the high human face portrait of quality.
Description
Technical field
The invention belongs to technical field of image processing, further relates in pattern-recognition and technical field of computer vision
It is a kind of based on the depth map aspect of model study human face portrait synthetic method.The people that the present invention can be used in public safety field
Face is retrieved and identification.
Background technology
In criminal investigation is chased, public security department has citizen's picture data storehouse, with reference to face recognition technology determining crime
Suspect's identity, but general more difficult acquisition suspect's photo, but can be under the cooperation of artist and witness in practice
The sketch images picture of suspect is obtained to carry out follow-up face retrieval and identification.Due to having between portrait and common human face photo
Very big difference, it is difficult to acquire satisfied recognition effect directly with traditional face identification method.By citizen's picture data
Photo in storehouse is combined into the gap drawn a portrait and can effectively reduced on their textures, and then improves discrimination.
Paper " X.Gao, J.Zhou, D.Tao, and X.Li, Local the face sketch that X.Gao et al. delivers at it
Carried in synthesis learning " (Neurocomputing, vol.71, no.10-12, pp.1921-1930, Jun.2008)
Go out using generating pseudo- portrait using built-in type hidden Markov model.This method is entered to the photo in training storehouse and portrait first
Row piecemeal, then corresponding photo block and portrait block are modeled with built-in type hidden Markov model, arbitrarily give a photograph
Piece, piecemeal is equally carried out, for an arbitrary block, with the thought of selective ensemble, the model of selector piecemeal generation is carried out
The generation of puppet portrait is simultaneously merged so as to obtain final pseudo- portrait.Weak point is existing for this method, due to this method
Employ selective ensemble technology, the pseudo- portrait of generation will be weighted average, cause that background is not clean, details is unintelligible, enter
And reduce generation portrait quality.
Paper " H.Zhou, Z.Kuang, and the K.Wong.Markov Weight that H.Zhou et al. delivers at it
Fields for Face Sketch Synthesis”(In Proc.IEEE Int.Conference on Computer
Vision, pp.1091-1097,2012) in propose a kind of human face portrait synthetic method based on Markov weight field.Should
Method by training image and input test image uniform piecemeal, for any test image block, is searched for its some neighbour, obtained first
To the candidate blocks of image aspects to be synthesized.Then Markov artwork is used to test image block, neighbour's block and candidate image block
Type models, and asks for reconstructing weights.Finally using weights and candidate's portrait block reconstruct synthesis portrait block are reconstructed, splicing obtains synthesis picture
Picture.Weak point is existing for this method, and image block characteristics use original Pixel Information, scarce capacity is represented, by illumination etc.
Environmental Noise Influence is larger.
Patent " the human face portrait synthetic method based on direction graph model " (application number of Xian Electronics Science and Technology University's application：
The CN201610171867.2 applyings date：2016.03.24 application publication number：CN105869134A one kind is disclosed in) and is based on direction
The human face portrait synthetic method of graph model.This method is first by training image and input test image uniform piecemeal, for any
Photo block is tested, searches for its some neighbour's photo block and corresponding neighbour portrait block.Then to test photo block, neighbour's photo block
Extract direction character.Then test photo block, the direction character of neighbour's photo block and neighbour are drawn using Markov graph model
As block modeling, ask for reconstructing the reconstruct weights for synthesizing portrait block by neighbour's portrait block.Finally drawn a portrait using reconstructing weights and neighbour
Block reconstruct synthesis portrait block, splicing obtain synthesis portrait.Weak point is existing for this method, and image block characteristics are using manually setting
The high-frequency characteristic of meter, adaptive ability deficiency, is not learnt fully to feature.
The content of the invention
It is an object of the invention to overcome the shortcomings of above-mentioned prior art, propose a kind of based on the study of the depth map aspect of model
Human face portrait synthetic method, can synthesize by the Environmental Noise Influences such as illumination high quality draw a portrait.
Realize comprising the following steps that for the object of the invention：
(1) sample set is generated：
(1a) takes out M human face photo composition training human face photo sample sets, 2≤M≤U- from human face photo sample set
1, U represents human face photo sum in sample set；
Remaining human face photo composition in human face photo sample set is tested face photograph collection by (1b)；
(1c) takes out the one-to-one people of human face photo with training human face photo sample set from human face portrait sample set
Face is drawn a portrait, composition training human face portrait sample set；
(2) image block set is generated：
(2a) is any from test face photograph collection to choose a test human face photo, and test human face photo is divided into greatly
It is small identical, and degree of overlapping identical photo block, composition test photo set of blocks；
(2b) will train each photo in human face photo sample set, and it is identical to be divided into size, and degree of overlapping identical
Photo block, composition training photo sample set of blocks；
(2c) will train each portrait in human face portrait sample set, and it is identical to be divided into size, and degree of overlapping identical
Portrait block, composition training portrait sample set of blocks；
(3) depth characteristic is extracted：
(3a) will train photo set of blocks and test all photo blocks in photo set of blocks, input in object identification
In the depth convolutional network VGG for object identification trained on database ImageNet, forward-propagating is carried out；
The depth characteristic of (3b) using 128 layers of characteristic pattern that depth convolutional network VGG intermediate layer exports as photo block, it is special
The coefficient that sign schemes every layer is ui,l, andWherein, ∑ represents sum operation, and i represents the sequence number of test photo block, i=
1,2 ..., N, N represent test photo block sum, l represent characteristic pattern sequence number, l=1 ..., 128；
(4) human face portrait block reconstruction coefficients are solved：
(4a) uses k nearest neighbor searching algorithm, is found out in photo sample set of blocks is trained with each testing photo block most phase
As 10 neighbours train photo blocks, while selected in drawing a portrait sample set of blocks from training and neighbour trains photo block to correspond
10 neighbours train portrait block, the coefficient of each neighbour's training image blocks is wi,k, wherein,K represents training
Image block sequence number, k=1 ..., 10；
(4b) uses Markov graph model formula, and photo is trained to all test photo block depth characteristics, all neighbours
The depth characteristic of block, all neighbours train portrait block, the coefficient u of depth characteristic figurei,l, neighbour's training image blocks coefficient wi,kBuild
Mould；
(4c) solves to Markov graph model formula, obtains human face portrait block reconstruction coefficients wi,k；
(6) human face portrait block is reconstructed：
10 neighbours corresponding to photo block will each be tested and train portrait block and respective coefficient wi,kIt is multiplied, result after multiplication
Summation, human face portrait block is reconstructed as corresponding to each test photo block；
(7) human face portrait is synthesized：
Reconstruct human face portrait block corresponding to splicing all test photo blocks, obtains synthesizing human face portrait.
Compared with prior art, the invention has the advantages that：
1st, due to the original image of the invention that image block is replaced using the depth characteristic extracted from depth convolutional network
Plain value information, the character representation scarce capacity for overcoming prior art to use, by the Environmental Noise Influences such as illumination it is big the problem of so that
The present invention has the advantages of to the ambient noise robust such as illumination.
2nd, due to the present invention using Markov graph model to depth characteristic figure coefficient and human face portrait block reconstruction coefficients
Joint modeling is carried out, the human face portrait background for overcoming prior art to synthesize is not clean, the unsharp problem of details so that the present invention
With the advantages of synthesis human face portrait clean background, details is clear.
Brief description of the drawings
Fig. 1 is flow chart of the present invention；
Fig. 2 is the simulated effect figure of the present invention.
Embodiment
The present invention is further described below in conjunction with the accompanying drawings.
Reference picture 1, of the invention comprises the following steps that.
Step 1, sample set is generated.
M human face photo composition training human face photo sample sets, 2≤M≤U-1, U tables are taken out from human face photo sample set
This concentration of sample human face photo sum.
By remaining human face photo composition test face photograph collection in human face photo sample set.
Take out from human face portrait sample set and drawn with the one-to-one face of human face photo of training human face photo sample set
Picture, composition training human face portrait sample set.
Step 2, image block set is generated.
It is any from test face photograph collection to choose a test human face photo, test human face photo is divided into size phase
Together, and degree of overlapping identical photo block, composition test photo set of blocks.
Each photo in human face photo sample set will be trained, it is identical to be divided into size, and degree of overlapping identical photo
Block, composition training photo sample set of blocks.
Each portrait in human face portrait sample set will be trained, it is identical to be divided into size, and degree of overlapping identical is drawn a portrait
Block, composition training portrait sample set of blocks.
Degree of overlapping refers to that overlapping region accounts for mutual 1/2 between two adjacent images block.
Step 3, depth characteristic is extracted.
Photo set of blocks will be trained and test all photo blocks in photo set of blocks, inputted in object identification data
In the depth convolutional network VGG for object identification trained on the ImageNet of storehouse, forward-propagating is carried out.
128 layers of characteristic pattern that depth convolutional network VGG intermediate layer is exported are as the depth characteristic of photo block, characteristic pattern
Every layer of coefficient is ui,l, andWherein, ∑ expression sum operation, the sequence number of i expression test photo blocks, i=1,
2 ..., N, N represent test photo block sum, l represent characteristic pattern sequence number, l=1 ..., 128.
Intermediate layer refers to depth convolutional network VGG activation primitive Relu2_2 layers
Step 4, human face portrait block reconstruction coefficients are solved.
Using k nearest neighbor searching algorithm, found out in photo sample set of blocks is trained most like with each test photo block
10 neighbours train photo block, while are selected in drawing a portrait sample set of blocks from training and train photo block one-to-one 10 with neighbour
Individual neighbour trains portrait block, and the coefficient of each neighbour's training image blocks is wi,k, wherein,K represents training image
Block sequence number, k=1 ..., 10.
K nearest neighbor searching algorithm comprises the following steps that：
The first step, calculate each test photo block depth characteristic it is vectorial with it is all training photo blocks depth characteristic to
Euclidean distance between amount；
Second step, it is worth order from small to large according to Euclidean distance, all training photo blocks is ranked up；
3rd step, 10 training photo blocks before selection, photo block is trained as neighbour.
Using Markov graph model formula, photo block is trained to all test photo block depth characteristics, all neighbours
Depth characteristic, all neighbours train portrait block, the coefficient u of depth characteristic figurei,l, neighbour's training image blocks coefficient wi,kModeling.
Markov graph model formula is as follows：
Wherein, min represents operation of minimizing, and ∑ represents sum operation, | | | |2Represent modulus square operation, wi,kRepresent
K-th of neighbour of i-th of test photo block trains the coefficient of portrait block, oi,kRepresent k-th of neighbour of i-th of test photo block
The pixel value vector of the lap of training portrait block, wj,kRepresent that j-th of neighbour of j-th of test photo block trains portrait block
Coefficient, oj,kRepresent that k-th of neighbour of j-th of test photo block trains the pixel value vector of the lap of portrait block, ui,l
Represent the coefficient of the l layer depth characteristic patterns of the depth characteristic of i-th of test photo block, dl(xi) represent i-th of test photo block
Depth characteristic h layer characteristic patterns, dl(xi,k) represent that k-th of neighbour of i-th of test photo block trains the depth of photo block
The u layer characteristic patterns of feature, l, h, u value correspondent equal..
Markov graph model formula is solved, obtains human face portrait block reconstruction coefficients wi,k。
Step 5, human face portrait block is reconstructed.
10 neighbours corresponding to photo block will each be tested and train portrait block and respective coefficient wi,kIt is multiplied, result after multiplication
Summation, human face portrait block is reconstructed as corresponding to each test photo block.
Step 6, human face portrait is synthesized.
Reconstruct human face portrait block corresponding to splicing all test photo blocks, obtains synthesizing human face portrait.
The method for splicing reconstruct portrait block corresponding to all test photo blocks is as follows：
The first step, will be in place according to its institute positioned at reconstruct portrait block corresponding to all test photo blocks of portrait diverse location
Put and placed；
Second step, take it is adjacent two reconstruct human face portrait block between lap pixel value average value；
3rd step, adjacent two reconstruct is replaced with the average value of the pixel value of lap between adjacent two reconstruct human face portrait blocks
The pixel value of lap between human face portrait block, obtain synthesizing human face portrait.
The effect of the present invention is further illustrated by following emulation experiment.
1. emulation experiment condition：
The allocation of computer environment of emulation experiment of the present invention be Intel (R) Core i7-4790 3.6GHZ, internal memory 16G,
(SuSE) Linux OS, programming language use Python, and database uses Hong Kong Chinese University's CUHK student databases.
The control methods of prior art used in the emulation experiment of the present invention includes the following two kinds：
A kind of is based on the method being locally linear embedding into, and LLE is designated as in experiment；Bibliography for " Q.Liu, X.Tang,
H.Jin,H.Lu,and S.Ma”(A Nonlinear Approach for Face Sketch Synthesis and
Recognition.In Proc.IEEE Int.Conference on Computer Vision,pp.1005-1010,
2005)；
Another kind is the method based on markov weight field model, and MWF is designated as in experiment；Bibliography for " H.Zhou,
Z.Kuang,and K.Wong.Markov Weight Fields for Face Sketch Synthesis”(In
Proc.IEEE Int.Conference on Computer Vision,pp.1091-1097,2012)。
2. emulation experiment content：
The present invention shares one group of emulation experiment.
Portrait is synthesized on CUHK student databases, and with being locally linear embedding into LLE, markov weight field model
The portrait of MWF methods synthesis is contrasted.
3. the simulation experiment result and analysis：
As shown in Figure 2, wherein Fig. 2 (a) is arbitrarily taken from test photo sample set to the simulation experiment result of the present invention
The test photo gone out, Fig. 2 (b) is the portrait that the synthesis of LLE methods is locally linear embedding into using prior art, and Fig. 2 (c) is to make
The portrait synthesized with prior art markov weight field model MWF methods, Fig. 2 (d) are the pictures synthesized using the inventive method
Picture.
From Figure 2 it can be seen that due to the original pixels value information of the invention that image block is replaced using depth characteristic, it is to illumination
There is more preferable robustness, therefore the photo for being had a great influence by illumination etc. ambient noise, compared to be locally linear embedding into LLE,
Markov weight field model MWF methods, synthesis portrait quality is higher, and noise is smaller.
Claims (6)
1. a kind of human face portrait synthetic method based on the study of the depth map aspect of model, comprises the following steps：
(1) sample set is generated：
(1a) takes out M human face photo composition training human face photo sample sets, 2≤M≤U-1, U tables from human face photo sample set
This concentration of sample human face photo sum；
Remaining human face photo composition in human face photo sample set is tested face photograph collection by (1b)；
(1c) takes out from human face portrait sample set to be drawn with the one-to-one face of human face photo of training human face photo sample set
Picture, composition training human face portrait sample set；
(2) image block set is generated：
(2a) is any from test face photograph collection to choose a test human face photo, and test human face photo is divided into size phase
Together, and degree of overlapping identical photo block, composition test photo set of blocks；
(2b) will train each photo in human face photo sample set, and it is identical to be divided into size, and degree of overlapping identical photo
Block, composition training photo sample set of blocks；
(2c) will train each portrait in human face portrait sample set, and it is identical to be divided into size, and degree of overlapping identical is drawn a portrait
Block, composition training portrait sample set of blocks；
(3) depth characteristic is extracted：
(3a) will train photo set of blocks and test all photo blocks in photo set of blocks, input in object identification data
In the depth convolutional network VGG for object identification trained on the ImageNet of storehouse, forward-propagating is carried out；
128 layers of characteristic pattern that (3b) exports depth convolutional network VGG intermediate layer are as the depth characteristic of photo block, characteristic pattern
Every layer of coefficient is ui,l, andWherein, ∑ expression sum operation, the sequence number of i expression test photo blocks, i=1,
2 ..., N, N represent test photo block sum, l represent characteristic pattern sequence number, l=1 ..., 128；
(4) human face portrait block reconstruction coefficients are solved：
(4a) uses k nearest neighbor searching algorithm, is found out in photo sample set of blocks is trained most like with each test photo block
10 neighbours train photo block, while are selected in drawing a portrait sample set of blocks from training and train photo block one-to-one 10 with neighbour
Individual neighbour trains portrait block, and the coefficient of each neighbour's training image blocks is wi,k, wherein,K represents training image
Block sequence number, k=1 ..., 10；
(4b) uses Markov graph model formula, and photo block is trained to all test photo block depth characteristics, all neighbours
Depth characteristic, all neighbours train portrait block, the coefficient u of depth characteristic figurei,l, neighbour's training image blocks coefficient wi,kModeling；
(4c) solves to Markov graph model formula, obtains human face portrait block reconstruction coefficients wi,k；
(5) human face portrait block is reconstructed：
10 neighbours corresponding to photo block will each be tested and train portrait block and respective coefficient wi,kIt is multiplied, result is summed after multiplication,
Human face portrait block is reconstructed as corresponding to each test photo block；
(6) human face portrait is synthesized：
Reconstruct human face portrait block corresponding to splicing all test photo blocks, obtains synthesizing human face portrait.
2. the human face portrait synthetic method according to claim 1 based on the study of the depth map aspect of model, it is characterised in that：
Step (2a), step (2b), the degree of overlapping described in step (2c) refer to that overlapping region accounts for each other between two adjacent images block
1/2.
3. the human face portrait synthetic method according to claim 1 based on the study of the depth map aspect of model, it is characterised in that：
Intermediate layer described in step (3b) refers to depth convolutional network VGG activation primitive Relu2_2 layers.
4. the human face portrait synthetic method according to claim 1 based on the study of the depth map aspect of model, it is characterised in that：
K nearest neighbor searching algorithm comprises the following steps that described in step (4a)：
The first step, calculate the vectorial depth characteristic vector with all training photo blocks of depth characteristic of each test photo block
Between Euclidean distance；
Second step, it is worth order from small to large according to Euclidean distance, all training photo blocks is ranked up；
3rd step, 10 training photo blocks before selection, photo block is trained as neighbour.
5. the human face portrait synthetic method according to claim 1 based on the study of the depth map aspect of model, it is characterised in that：
Markov graph model formula described in step (4b) is as follows：
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Wherein, min represents operation of minimizing, and ∑ represents sum operation, | | | |2Represent modulus square operation, wi,kRepresent i-th
K-th of neighbour of individual test photo block trains the coefficient of portrait block, oi,kRepresent k-th of neighbour training of i-th of test photo block
The pixel value vector of the lap of portrait block, wj,kRepresent that j-th neighbour of j-th of test photo block trains the portrait block to be
Number, oj,kRepresent that k-th of neighbour of j-th of test photo block trains the pixel value vector of the lap of portrait block, ui,lRepresent
The coefficient of the l layer depth characteristic patterns of the depth characteristic of i-th of test photo block, dl(xi) represent i-th of depth for testing photo block
Spend the h layer characteristic patterns of feature, dl(xi,k) represent that k-th of neighbour of i-th of test photo block trains the depth characteristic of photo block
U layer characteristic patterns, l, h, u value correspondent equal.
6. the local two benches human face portrait generation method with global property of joint according to claim 1, its feature exist
In：The method of reconstruct portrait block is as follows corresponding to all test photo blocks of splicing described in step (6)：
The first step, it will enter positioned at reconstruct portrait block corresponding to all test photo blocks of portrait diverse location according to its position
Row is placed；
Second step, take it is adjacent two reconstruct human face portrait block between lap pixel value average value；
3rd step, adjacent two reconstruct face is replaced with the average value of the pixel value of lap between adjacent two reconstruct human face portrait blocks
The pixel value of lap between portrait block, obtains synthesizing human face portrait.
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| CN201710602696.9A CN107392213B (en) | 2017-07-21 | 2017-07-21 | Face portrait synthesis method based on depth map model feature learning |
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| CN201710602696.9A CN107392213B (en) | 2017-07-21 | 2017-07-21 | Face portrait synthesis method based on depth map model feature learning |
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