CN107122705A - Face critical point detection method based on three-dimensional face model - Google Patents
Face critical point detection method based on three-dimensional face model Download PDFInfo
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Abstract
The present invention relates to a kind of face critical point detection method based on three-dimensional face model, comprise the following steps：Step 01, the initial parameter of facial image and three-dimensional face model is obtained in face training sample；Step 02, attitude-adaptive feature and normalization codes co-ordinates are generated according to the facial image and initial parameter；Step 03, enter line translation using convolutional neural networks to the attitude-adaptive feature and normalization codes co-ordinates respectively to merge, obtain the parameter residual error of true residue and initial parameter；Step 04, the initial parameter is updated according to the parameter residual error, goes to step 02 until the parameter residual error reaches predetermined threshold value；Step 05, the three-dimensional face model is updated using the parameter residual error for reaching predetermined threshold value, gathers the face key point on the three-dimensional face model.In the present invention, the face critical point detection under full posture is realized.
Description
Technical field
The invention belongs to image processing and pattern recognition field, and in particular to a kind of people based on three-dimensional face model
Face critical point detection method.
Background technology
Face key point is a series of with fixed semantic point, such as canthus, nose and the corners of the mouth, based on people on face
In the computer vision that face understands, detection key point is an important pre-treatment step.Most human face analysis systems are all
Need to carry out critical point detection first, an accurately understanding is distributed with the face to face, so that in the specific bit of face
Put extraction feature.But most of critical point detection methods can only be all handled below medium posture at present, i.e., deflection angle (yaw) is small
In the face critical point detection of (deflection angle can reach 90 °) under 45 ° of face, big posture be always difficult point.
Challenged present in it in terms of mainly having three below：First, in traditional critical point detection algorithm, it is assumed that all
Key point all has stable external performance to be detected.However, under big posture, some key points can not can be kept away
Exempt from due to becoming invisible from blocking, these invisible points are made because its presentation information is blocked and can not be detected
Into conventional method failure；Secondly, it is more complicated in the presentation change of big posture human face, side can be changed to from front, this
It is required that location algorithm must more robust to understand the face presentation under different postures；Finally in terms of training data, demarcation is big
The key point of posture human face is relatively difficult, needs to guess its position, most of existing number for sightless key point
Under being all medium posture according to the face in storehouse, databases of the minority comprising big posture face are also only labelled with visible key point,
It is difficult to design the key point algorithm of any posture of processing.
One possible solution it is directly to fit three-dimensional face model by image in the prior art.Usually using level
The convolutional neural networks of connection enter line translation to an input picture, return out the parameter of three-dimensional face model.But the technology is deposited
In following defect：First, the technology states the rotation of face using Eulerian angles, and Eulerian angles can be due to universal joint under big posture
Deadlock and produce ambiguity；Secondly, the input feature vector of image aspects is used only in the technology, i.e., original image is sent directly into convolution
Neutral net, and intermediate result image can be used progressively to be corrected in cascade, so as to further lift fitting precision；Most
Afterwards, priority of the technology in training convolutional neural networks not to model parameter is effectively modeled, and makes convolutional Neural net
The fitting performance of network is dispersed on some minor parameters.
The content of the invention
In order to solve above mentioned problem of the prior art, the present invention proposes a kind of face based on three-dimensional face model and closed
Key point detecting method, to realize the face critical point detection under full posture.
This method comprises the following steps：
Step 01, the initial parameter of facial image and three-dimensional face model is extracted in face training sample；
Step 02, attitude-adaptive feature and normalization codes co-ordinates are generated according to the facial image and initial parameter；
Step 03, the attitude-adaptive feature and normalization codes co-ordinates are become using convolutional neural networks respectively
Fusion is changed, the parameter residual error of true residue and initial parameter is obtained；
Step 04, the initial parameter is updated according to the parameter residual error, goes to step 02 until the parameter residual error reaches
To predetermined threshold value；
Step 05, the three-dimensional face model is updated using the parameter residual error for reaching predetermined threshold value, gathers the three-dimensional people
Face key point on face model.
Preferably, the three-dimensional face model is carried out when generating the attitude-adaptive feature in the step 02
Projection, formula during projection includes：
Wherein, V (p) is the function for constructing three-dimensional face model and projecting, and can obtain each key point on threedimensional model and is scheming
As upper two-dimensional coordinate,Represent the average shape of face, AidRepresent the PCA master extracted on the three-dimensional face of neutral expression
Into split axle, αidRepresent form parameter, AexpRepresent the PCA principal component axles extracted in the difference of expression face and neutral face, αexpTable
Show expression parameter, f is zoom factor, and Pr is direct projective matrix, and R is spin matrix, by four-tuple [q0,q1,q2,q3] build,
t2dFor translation vector, fit object parameter is [f, R, t2d,αid,αexp], fit object parameter sets are [f, q0,q1,q2,q3,
t2d,αid,αexp]。
Preferably, it is described by four-tuple [q0,q1,q2,q3] build spin matrix formula be：
Preferably, attitude-adaptive feature is generated in the step 02 includes：
The TWO-DIMENSIONAL CIRCULAR CYLINDER coordinate on each summit of the three-dimensional face model is calculated, and between azimuth axis and altitude axis are first-class
Every n*n anchor point of ground sampling；In fit procedure, these anchor points are carried out with deformation, scaling, rotation using the parameter of "current" model
Position of the anchor point on image is obtained with translation, attitude-adaptive feature is generated.
Preferably, generation normalization codes co-ordinates include equation below in the step 02：
PNCC (I, p)=I＆ZBuffer (V3d(p),NCC)
Wherein, PNCC is normalization codes co-ordinates, and I is the facial image of input, and p is parameter current, and ＆ is in channel dimension
Stacking computing, function ZBuffer be after three-dimensional dough sheet is rendered using texture generate two dimensional image a function, V3d(p)
It is the three-dimensional face after scaling rotation translation deformation, the figure for being stacked together generation is normalization codes co-ordinates.
Preferably, the step 03 is specifically included：
According to two parallel-convolution neutral nets, the attitude-adaptive feature and normalization codes co-ordinates are carried out respectively
Conversion, and the feature after conversion is merged using an extra full articulamentum, fusion results return to be joined
Number residual error.
Preferably, the calculation formula of parameter residual error is in the step 03：
Δpk=Netk(PAF(pk,I),PNCC(pk,I))
Wherein, pkFor parameter current, I is input picture, Δ pkFor parameter current and the residual error of true residue, PAF is posture
Self-adaptive features, PNCC is normalization codes co-ordinates, NetkFor two-way parallel-convolution neutral net.
Preferably, the step 03 also includes being trained the convolutional neural networks, in training to described true
Residual error is weighted processing, and formula is：
Wherein, pc=p0+ Δ p, 0≤w≤1, w are parameter weights, and Δ p is the output of convolutional neural networks, pgTo be true residual
Difference, p0For the input parameter of current iteration, pcFor parameter current, V (p) is deformation and weak perspective projection function, and diag is to angular moment
Battle array construction.
Preferably, it is specially that the parameter is residual to update the initial parameter according to the parameter residual error in the step 04
It is poor to be added with the initial parameter.
Compared with prior art, the present invention at least has advantages below：
By the face critical point detection method based on three-dimensional face model in the present invention, the face under full posture is realized
Critical point detection.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the face critical point detection method provided by the present invention based on three-dimensional face model；
Fig. 2 is two-way parallel-convolution Processing with Neural Network schematic flow sheet provided by the present invention.
Embodiment
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this
A little embodiments are used only for explaining the technical principle of the present invention, it is not intended that limit the scope of the invention.
The invention discloses a kind of face critical point detection method based on three-dimensional face model, as shown in figure 1, including such as
Lower step：
Step 00, three-dimensional variable faceform is built.
Three-dimensional face point cloud sample is obtained by three-dimensional scanner, and three-dimensional variable is built using principal component analysis (PCA)
Model：
Wherein S represents three-dimensional face,Represent the average shape of face, AidRepresent that the three-dimensional face in neutral expression is above carried
The PCA principal component axles of taking-up, αidRepresent form parameter, AexpRepresent the PCA that is extracted in the difference of expression face and neutral face it is main into
Split axle, αexpRepresent expression parameter.
Construct after three-dimensional face model, be projected into using weak perspective projection on the plane of delineation：
Wherein, V (p) is the function for constructing faceform and projecting, and can obtain two of each point on threedimensional model on image
Dimension coordinate, f is zoom factor, and Pr is direct projective matrix, and R is spin matrix, t2dFor translation vector；The target component being so fitted
For [f, R, t2d,αid,αexp]。
Traditionally human face posture is generally represented with Eulerian angles, including pitching, deflection and rolling.However, when deflection angle is close
90 ° when i.e. posture is close to side, Eulerian angles can be made the problem of universal joint deadlock to produce the different Eulerian angles of ambiguity, i.e., two can
Identical spin matrix can be corresponded to.Therefore, we employ four-tuple [q0,q1,q2,q3] represent spin matrix, and will
Zoom factor f is incorporated into this matrix, and the model parameter collection so obtained is combined into：
[f,q0,q1,q2,q3,t2d,αid,αexp]。
Using three-dimensional variable, faceform is used as fit object.The training sample demarcated by hand based on face key point
(or using training sample based on disclosed face key point data set), and face sideization technology is used on this basis
It will be rotated outside face carry out face, generation becomes the bigger and more rich face training sample set of corner.
Step 01, facial image and initial parameter are extracted.
Step 02, generation attitude-adaptive feature and normalization codes co-ordinates.
The three-dimensional face model fitting algorithm based on convolutional neural networks is described below, i.e., how to use convolutional neural networks
Estimate the posture, shape and expression parameter of face.For the input of convolutional neural networks, we devise two kinds of input feature vectors,
It is the normalization coding of attitude-adaptive feature and projection respectively.
First, attitude-adaptive feature (Pose Adaptive Feature-PAF) is illustrated.
In convolutional neural networks, traditional convolutional layer is to carry out convolution pixel-by-pixel along two dimensional image axle, and in PAF
Convolution is some fixation semantic locations progress in face.The position that PAF carries out convolution algorithm is obtained by following approach：Consider
Can be roughly approximate with cylinder to face, we calculate the TWO-DIMENSIONAL CIRCULAR CYLINDER coordinate on each summit of three-dimensional face model, and in side
Equally spaced sampled on parallactic angle axle and altitude axis n*n anchor point.In fit procedure, current model parameter p is given, we throw
Shadow three-dimensional face model simultaneously obtains the position of anchor point on 2d, and the position of convolution algorithm is carried out as PAF.Notice
Convolution algorithm on anchor point forms n*n figure, can subsequently carry out traditional convolution algorithm.In order to reduce at occlusion area
The influence of feature, and we are by the response at occlusion area divided by 2, generate attitude-adaptive feature.
Illustrate normalization codes co-ordinates (the Projected Normalized Cooridnate Code- of projection below
PNCC).This input feature vector depends on a kind of new codes co-ordinates, first normalizes to three-dimensional average face in three dimensions
On 0-1：
Put and be all uniquely distributed on [0,0,0] to [1,1,1] on threedimensional model after normalization, therefore one kind can be regarded as
Three-dimensional codes co-ordinates, we term it normalization codes co-ordinates.With usually used numbering (such as 0,1 ..., n) different, normalizing
It is continuous in three dimensions to change codes co-ordinates.In fit procedure, current model parameter p is given, we use ZBuffer
Algorithm renders the three-dimensional face of projection with normalization codes co-ordinates：
PNCC (I, p)=I＆ZBuffer (V3d(p),NCC)
Wherein, PNCC is normalization codes co-ordinates, and I is the facial image of input, and p is parameter current, and ＆ is in channel dimension
Stacking computing, function ZBuffer be after three-dimensional dough sheet is rendered using texture generate two dimensional image a function, V3d(p)
It is the three-dimensional face after scaling rotation translation deformation, the figure for being stacked together generation is normalization codes co-ordinates, will enter into convolution
Neutral net.
The feature of both generations has complementarity, wherein the normalization coding projected belongs to the feature of image aspects, its
Feature is original image to be sent directly into convolutional neural networks.And attitude-adaptive feature belongs to the feature at model visual angle, its
Feature is that the intermediate result that can use fitting is corrected to artwork.The normalization coding of projection will be due to that will include whole face figure
Picture, therefore image context information is more abundant, is adapted to Face detection and thick fitting, relatively attaches most importance in initial iteration several times
Will；Attitude-adaptive feature is equivalent to using "current" model parameter to the people in image due to carrying out convolution algorithm at anchor point
Face is positioned and corrected, and progressively simplifies fitting task, is adapted to the fitting in details, important in final iteration several times.
Step 03, fusion treatment is converted, parameter residual error is obtained.
It can be seen that, there is complementary relationship in both above-mentioned features, in order to make full use of the advantage of both features, we
K iteration is carried out using two-way parallel-convolution neural network structure.In kth time iteration, an initial parameter p is givenk, we
Use pkThe normalization codes co-ordinates feature of attitude-adaptive feature and projection is generated, and trains a two-way as shown in Figure 2 simultaneously
Row convolutional neural networks, wherein attitude-adaptive feature branch include 5 convolutional layers, 4 pond layers and a full articulamentum.Throw
The normalization codes co-ordinates branch of shadow includes the convolutional layer of an attitude-adaptive, three common convolutional layers, three pond layers with
One full articulamentum.The parallel two-way neutral net of the Web vector graphic enters line translation to two features respectively, and is connected entirely with one
Layer is connect to be merged.The final feature merged out is used for returning out the residual error of parameter current and target component：
Δpk=Netk(PAF(pk,I),PNCC(pk,I))
Wherein, pkFor parameter current, I is input picture, Δ pkFor parameter current and the residual error of true residue, PAF is posture
Self-adaptive features, PNCC is normalization codes co-ordinates, NetkFor two-way parallel-convolution neutral net.
Be described below how training convolutional neural networks, its basic thought is the parameter residual error that recurrence close to true
Parameter residual error.Importance yet with faceform's parameter is different, and the importance of a small number of parameters (such as posture) is significantly larger than
Exhausted most regions parameter, it is therefore desirable to which the loss in training to each parameter is weighted.Weights are mutual in traditional algorithm
Independent, generally by being manually specified or being determined according to " loss produced by mistakenly estimating certain parameter ".But between parameter
Weights be correlative, such as before attitude parameter is accurate enough, estimation expression parameter is nonsensical.The present invention passes through
Optimize an energy function uniformly to obtain the weights of all parameters, devise the parameter distance loss of following optimal weighting
(Optimized Weighted Parameter Distance Cost-OWPDC)：
Eowpdc=(Δ p- (pg-p0))Tdiag(w*)(Δp-(pg-p0))
Wherein, pc=p0+ Δ p, 0≤w≤1, w are parameter weights, and Δ p is the output of convolutional neural networks, pgTo be true residual
Difference, p0For the input parameter of current iteration, pcFor parameter current, V (p) is deformation and weak perspective projection function, and diag is to angular moment
Battle array construction.
As shown by the equation, by by true residue diag (w) * (p of weightingg-pc) it is added to parameter current pcIn, it is desirable to
The three-dimensional face constructed by parameter after renewal is more nearly real human face V (pg).Simultaneously as the capability of fitting of neutral net
It is limited, therefore use λ | | diag (w) * (pg-pc)||2To model the pressure that fitting parameter current is produced to neutral net, add
Into loss item, it is expected that neutral net can assign a weighting to the higher parameter of cost performance.
In the training process, optimal w is asked for each sample excessively complicated, therefore by V (pc+diag(w)*(pg-pc))
In pgPlace is obtained using Taylor expansion：
||V′(pg)*diag(w-1)*Δpc||2+λ||diag(w)*Δpc||2
Wherein, V ' (pg) it is V (pg) Jacobian matrixes, above formula is deployed and removes constant term, is obtained：
wT(diag(Δpc)V′(pg)TV′(pg)diag(Δpc))w-2*1T(diag(Δpc)V′(pg)TV′(pg)diag
(Δpc))w
-λ*wTdiag(Δpc.*Δpc)w
Make H=V ' (pg)diag(Δpc), then original optimization problem can be write：
0≤w≤1
Above formula is a standard quadratic programming problem, can rapidly be solved with interior point method.But in the loss function H meter
Calculate and take very much, recalculating H when training each sample makes the training time unacceptable.This experiment find H it is unique very
Several is V ' (pg), and to each training sample V ' (pg) it is fixed.Therefore before training, can be by the V ' of each sample
(pg) calculate and store, directly read in training.The weighting that parameters lose in the weights asked for i.e. OWPDC,
The priority of parameters can be described.
Step 04, the initial parameter is updated according to the parameter residual error.
Input parameter is added with parameter residual error afterwards, a more preferable parameter p is obtainedk+1=pk+Δpk, and carry out next time
Iteration, includes the parameter Estimation of input feature vector construction and convolutional neural networks.Carry out after K iteration so that parameter residual error reaches
To after predetermined threshold value, V (p are usedk) obtain position of the every bit on image on three-dimensional face.
Step 05, the face key point on collection three-dimensional face model.
Because existing face key point training sample is usually within medium posture, and the present invention is by existing training sample
Rotated outside this progress face, generate the training sample under big posture, it is specific as follows：
A training sample, including facial image and the key point of manual demarcation are given, three based on key point are used
Dimension faceform's fitting can obtain the threedimensional model of face in image.Then equably sampled in background area some anchor points.
To each anchor point, its depth is estimated according to the point on the three-dimensional face model away from its nearest neighbours.Obtain all anchor points
After depth, anchor point is constituted into a series of tri patch using trigonometric ratio.These dough sheets are constituted together with the three-dimensional face fitted
The depth information of image.Being somebody's turn to do " virtual depth image " can carry out rotating outside face in three dimensions, and carry out at any angle
Render, presentation of the face under different postures in generation image.The present invention expands deflection angle with 5 ° for step-length, gradually generates one
Serial virtual sample, until 90 °.
It can not position from the defect for blocking key point, directly be entered by image instant invention overcomes traditional critical point detection algorithm
Row three-dimensional face model is fitted, and up-samples out key point from the three-dimensional face being fitted.During human face fitting, except
Using the normalization codes co-ordinates of the Projection Character of image aspects, certain moduli type visual angle characteristic " attitude-adaptive have also been devised
Feature ", this feature can use fitting intermediate result to carry out implicit frontization to image, so as to progressively simplify fitting task, enter
One step lifts fitting precision.Because image aspects feature and model visual angle characteristic have complementary relationship, in order to combine two kinds of features
Advantage, line translation entered to two kinds of input feature vectors and merged simultaneously using two-way parallel-convolution neutral net, it is final to use fusion
The feature gone out carries out model parameter recurrence.When training convolutional neural networks, the present invention is by considering faceform's parameter
Priority and convolutional neural networks emphasis is fitted some important parameters and further increase fitting precision.Finally so that this hair
It is bright to realize the face critical point detection of full posture.
So far, combined preferred embodiment shown in the drawings describes technical scheme, still, this area
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these embodiments.Without departing from this
On the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to correlation technique feature, these
Technical scheme after changing or replacing it is fallen within protection scope of the present invention.
Claims (9)
1. a kind of face critical point detection method based on three-dimensional face model, it is characterised in that comprise the following steps：
Step 01, the initial parameter of facial image and three-dimensional face model is obtained in face training sample；
Step 02, attitude-adaptive feature and normalization codes co-ordinates are generated according to the facial image and initial parameter；
Step 03, are entered by line translation using convolutional neural networks and is melted for the attitude-adaptive feature and normalization codes co-ordinates respectively
Close, obtain the parameter residual error of true residue and initial parameter；
Step 04, the initial parameter is updated according to the parameter residual error, go to step 02 until the parameter residual error reach it is pre-
If threshold value；
Step 05, the three-dimensional face model is updated using the parameter residual error for reaching predetermined threshold value, gathers the three-dimensional face mould
Face key point in type.
2. the face critical point detection method based on three-dimensional face model according to claim 1, it is characterised in that the step
In rapid 02 when generating the attitude-adaptive feature, the three-dimensional face model is projected, formula during projection includes：
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Wherein, V (p) is the function for constructing three-dimensional face model and projecting, and can obtain on threedimensional model each key point on image
Two-dimensional coordinate, S represents the average shape of face, AidRepresent the PCA principal components extracted on the three-dimensional face of neutral expression
Axle, αidRepresent form parameter, AexpRepresent the PCA principal component axles extracted in the difference of expression face and neutral face, αexpRepresent table
Feelings parameter, f is zoom factor, and Pr is direct projective matrix, and R is spin matrix, by four-tuple [q0,q1,q2,q3] build, t2dIt is flat
The amount of shifting to, fit object parameter is [f, R, t2d,αid,αexp], fit object parameter sets are [f, q0,q1,q2,q3,t2d,αid,
αexp]。
3. the face critical point detection method based on three-dimensional face model according to claim 1, it is characterised in that it is described by
Four-tuple [q0,q1,q2,q3] build spin matrix formula be：
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</msub>
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</msup>
</mrow>
</mtd>
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4. the face critical point detection method based on three-dimensional face model according to claim 3, it is characterised in that the step
Attitude-adaptive feature is generated in rapid 02 to be included：
The TWO-DIMENSIONAL CIRCULAR CYLINDER coordinate on each summit of the three-dimensional face model is calculated, and in azimuth axis and altitude axis equally spaced
N*n anchor point of sampling；In fit procedure, these anchor points are carried out with deformation, scaling, rotation peace using the parameter of "current" model
Shifting obtains position of the anchor point on image, generates attitude-adaptive feature.
5. the face critical point detection method based on three-dimensional face model according to claim 3, it is characterised in that the step
Generation normalization codes co-ordinates include equation below in rapid 02：
PNCC (I, p)=I＆ZBuffer (V3d(p),NCC)
<mrow>
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<msub>
<mi>t</mi>
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</mrow>
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</mrow>
Wherein, PNCC is normalization codes co-ordinates, and I is the facial image of input, and p is parameter current, and ＆ is the heap in channel dimension
Folded computing, function ZBuffer is the function that two dimensional image is generated after three-dimensional dough sheet is rendered using texture, V3d(p) it is contracting
The three-dimensional face after rotation translation deformation is put, the figure for being stacked together generation is normalization codes co-ordinates.
6. the face critical point detection method based on three-dimensional face model according to claim 1, it is characterised in that the step
Rapid 03 specifically includes：
According to two parallel-convolution neutral nets, the attitude-adaptive feature and normalization codes co-ordinates are become respectively
Change, and the feature after conversion is merged using an extra full articulamentum, fusion results return to obtain parameter
Residual error.
7. the face critical point detection method based on three-dimensional face model according to claim 6, it is characterised in that the step
The calculation formula of parameter residual error is in rapid 03：
Δpk=Netk(PAF(pk,I),PNCC(pk,I))
Wherein, pkFor parameter current, I is input picture, Δ pkFor parameter current and the residual error of true residue, PAF is that posture is adaptive
Feature is answered, PNCC is normalization codes co-ordinates, NetkFor two-way parallel-convolution neutral net.
8. the face critical point detection method based on three-dimensional face model, its feature according to any one of claim 1~7
It is, the step 03 also includes being trained the convolutional neural networks, and the true residue is added in training
Power is handled, and formula is：
<mrow>
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</msup>
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</munder>
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<mi>&lambda;</mi>
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</msup>
</mrow>
Wherein, pc=p0+ Δ p, 0≤w≤1, w are parameter weights, and Δ p is the output of convolutional neural networks, pgFor true residue, p0
For the input parameter of current iteration, pcFor parameter current, V (p) is deformation and weak perspective projection function, and diag is diagonal matrix structure
Make.
9. the face critical point detection method based on three-dimensional face model, its feature according to any one of claim 1~7
Be, updated in the step 04 according to the parameter residual error initial parameter be specially by the parameter residual error with it is described just
Beginning parameter is added.
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