CN103824050A - Cascade regression-based face key point positioning method - Google Patents
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Abstract
The invention relates to a cascade regression-based face key point positioning method. The cascade regression-based face key point positioning method includes the following steps that: 1) a large number of face image data are acquired, and an initial key point position is marked; 2) the face image data are trained, and a coarse regression machine can be obtained through learning, and then with the output of the coarse regression machine adopted as input, a fine regression machine can be obtained through learning; and 3) face image data to be recognized are given, and the initial shape of a face is regressed to the vicinity of a real shape through the coarse regression machine, and then, with the output of the coarse regression machine adopted as input, the precise coordinates of a face key point can be obtained through the fine regression machine. According to the cascade regression-based face key points positioning method of the invention, a coarse-to-fine cascade regression algorithm is adopted, and a large number of samples are learned, and multi-feature fusion and multiple-regression machine fusion are realized, and therefore, the speed and the robustness of the algorithm are improved greatly, and the face key point can be excellently positioned under the situations of occlusion, low lightness and poses such as side faces, and the accuracy and the speed of face key point positioning can be effectively improved.
Description
Technical field
The invention belongs to Digital Image Processing and face recognition technology field, be specifically related to a kind of face key point localization method returning based on cascade.
Background technology
Face key point is the strong key points of some sign abilities of face, such as eyes, nose, face and face mask etc.Key point is positioned at recognition of face field very important effect, such as recognition of face, tracking, expression analysis and 3D modeling all depend on the result that key point is located.
Traditional face key point localization method, is the method based on parameter shape, according near appearance features key point, learns out a parameter model, optimizes iteratively in use the position of key point, finally obtains key point coordinate.
Above-mentioned face key point localization method, all depends critically upon the shooting quality of picture, human face posture.Blocking, illumination and attitude be while changing greatly, all can not obtain result accurately.Meanwhile, due to developing rapidly of current mobile terminal demand, said method can not be realized in real time and being processed by mobile terminals such as mobile phones.
Summary of the invention
Existing face key point localization method to attitude, block, light is very responsive, precision and velocity ratio are poor, and mobile phone that computational resource limited poor at image-forming condition etc. is difficult to accomplish real-time processing on mobile terminal.The present invention proposes one fast, accurately by slightly to smart cascade homing method, by great amount of samples is learnt and many Fusion Features, return more device merge, solved well the key point orientation problem in recognition of face.
The technical solution used in the present invention is as follows:
The face key point localization method returning based on cascade, its step comprises:
1) gather a large amount of face image datas, and mark initial key point position (can pass through handmarking);
2) by described a large amount of face image datas are trained, study obtains a robust regression device, and then, using the output of described robust regression device as input, study obtains an essence and returns device, thereby obtains by slightly to smart cascade recurrence device;
3) given face picture to be detected and corresponding face position, by described robust regression device, the original shape of face is revert near true shape, then using the output of described robust regression device as input, return device and obtain the accurate coordinates of face key point by described essence.
Further, described robust regression device is designed to linear regression device, extracts SURF feature at all key points place.This recurrence device can represent the relation between 3D attitude and SURF feature.
Further, the linear regression device that described robust regression device comprises multi-stage cascade, preferably adopts two-stage linear regression device, and the output of the first order is as the input of the second level.The robust regression device consisting of this two-stage linear regression device, can obtain a rough key point position and 3D attitude.
Further, described essence returns device using the output of robust regression device above as input, uses random fern cascade to return device, using pixel value difference as feature.Return device by essence, the rough result that robust regression device can be provided returns into an accurate result.
Further, it is a double-layer structure that described essence returns device, and ground floor is a series of weak device { f that return
1, f
2..., f
tcascade; The second layer, is the cascade that a series of random ferns return devices, returns device f a little less than forming one.
Further, described face key point comprises the positions such as eyes, nose, face, face mask, more specifically, as pupil, canthus, eyebrow angle, the corners of the mouth, lip along etc. position.
In the present invention, proposed a kind of by slightly to smart cascade regression algorithm, designed and one, returned the cascade that device merges more and return device.This cascade returns device and is divided into two parts: 1. robust regression device, and feature is that speed is fast, can revert to fast near of normal solution; 2. essence returns device, and feature is that each amount returning is less, but can obtain result more accurately.According to the feature of the recurrence device of design, allow different recurrence devices complete different task (the random fern of linear regression device and cascade returns device), merge various features (SURF and margin of image element feature).
The present invention propose by slightly to smart cascade regression algorithm, by great amount of samples is learnt, and many Fusion Features, return more device merge mode, speed and the robustness of algorithm are improved greatly, carry out face key point location blocking, under the attitude such as light difference and side face and all obtained extraordinary effect, precision and the speed that can effectively improve face key point location, be obviously better than existing other algorithms.
Accompanying drawing explanation
Fig. 1 is the flow chart of steps of the face key point localization method returning based on cascade of the present invention.
Fig. 2 is that cascade of the present invention returns device schematic diagram.
Fig. 3 adopts cascade recurrence device initial value to be revert to the schematic diagram of true solution.
Fig. 4 adopts method of the present invention to carry out the schematic diagram of face key point location.
Embodiment
Below by specific embodiments and the drawings, the present invention will be further described.
The face key point localization method returning based on cascade of the present invention, its steps flow chart as shown in Figure 1, mainly comprise two parts content, the one, set up by robust regression device part and essence and return the cascade recurrence device that device part forms, the 2nd, utilize the cascade of setting up to return device facial image data are processed to identify key point.
1. set up by slightly returning device to smart cascade
General frame of the present invention is that a cascade returns device.Our target is a regression function f of study, makes it be mapped to solution space from initial sample space, can make mean square deviation minimum.While running into higher dimensional space and complicated linear relationship, express this mapping relations unrealistic if just learn a recurrence device.So we have proposed to use the method for cascade, by the multiple weak devices that return of cascade, they are formed to a strong recurrence device that recurrence ability is stronger.The cascade homing method that the present invention adopts, is divided into t the simply cascade { f of regression function by regression function f
1, f
2..., f
t, every one-level f
kinput be all its previous stage f
k ?1output, as shown in Figure 2, by f
1, f
2..., f
tcombine, the regression function obtaining can be similar to out the complicated Nonlinear Mapping relation of original shape to true shape.
Recurrence device of the present invention is followed by slightly to smart process, and cascade returns device and is divided into two parts, and robust regression device and essence return device.
If just according to method above, adopt and simply carries out cascade with several weak recurrence devices, first effect is undesirable, because the shooting condition of picture varies, attitude is different, and the shape that return is also all not quite similar, obtain perfect effect, too high to the requirement of recurrence device.Secondly,, if cascade progression is too much, speed also can be very slow, can not meet the requirement to speed.In the present invention, propose to use dissimilar recurrence device phase cascade to innovation, made it that Each performs its own functions, mutually promoted, maximized favourable factors and minimized unfavourable ones.
Therefore, the recurrence device of cascade is divided into two parts by we, and Part I is robust regression device, initial value revert to near of true solution, completes large regressive object, but be indifferent to details.This part, the coarse regressive object completing, speed is very fast, for Part II generates input.Part II is that essence returns device, only need to regulate in detail, progressively slowly approaches to true solution, and whole process as shown in Figure 3.Two parts, have formed one by slightly returning device to smart cascade, in speed and effect, have very large lifting.
For two-part different qualities, the present invention has designed different sorters and feature, can complete in maximum efficiency regressive object.
The target of Part I is to obtain fast coarse solution, and we adopt SURF feature, an out linear regression device of study, and this part returns device, can rapidly initial value be mapped near normal solution.Concrete implementation step is as follows:
1. on original shape, each key point place extracts initial SURF feature, is denoted as Φ
0, true regressive object is designated as Δ X*;
2. in training process, because true shape X is known, initial value X
0be known, so true regressive object Δ X* is known, Δ X*=X ?X
0.Linear regression device can be expressed as Δ X
0=R
0* Φ
0+ b
0, target is exactly to allow return the estimator Δ X obtaining
0with true regressive object Δ X* infinite approach.Here the parameter requiring is exactly R
0and b
0.Can try to achieve by minimizing following formula:
Wherein, d
ibe i face picture, X
0 ibe the original shape of i face, Δ X
* ibe the true review target of i face, Φ
0 ibe that i face is at original shape X
0 ithe SURF proper vector at place, this is the solution least square problem that we are familiar with, and can be easy to obtain R
0and b
0.
3. according to the R obtaining
0and b
0, just can obtain the increment Delta X estimating
0=R
0* Φ
1+ b
0, X+ Δ X
0as new training set, be designated as X
1.According to new training set, extract new SURF feature Φ
1, have Δ X
1=R
1* Φ
1+ b
1, in like manner, according to above-mentioned method, can be easy to try to achieve R
1and b
1.By that analogy, can learn a lot of similarly linear regression devices, at Part I, it is just much of that we learn two-layer linear regression device, the solution X of estimation
2approach very much true solution X.After Part I obtains coarse solution, as the input of Part II, remaining meticulous regressive object is given below and is done.
Part II, the present invention has adopted the random fern of cascade to return device, and pixel value difference is as feature.Our input using the output of Part I as this part, the true solution of distance is very approaching for this value, the just adjustment in detail that do, make its progressively approaching to reality solution.
It is the combination of 5 features and threshold value that random fern returns device, and training sample is divided into 2
5individual space.The corresponding output Δ X in each space
bin, Δ X
binfor being divided into the mean value of all regressive objects in this space.
The random fern of the cascade of Part II returns device, is a two-layer recurrence device.Because if just this is returned to device and is designed to original random fern and returns the cascade of device, recurrence ability too a little less than, so be designed to a two-layer structure in the present invention.Specific as follows, multiple original random ferns return device cascade, return device f a little less than forming one.Device { f will be returned again a little less than these
1, f
2..., f
tstrong device that returns of cascade formation, namely Part II essence mentioned above returns device.
Concrete implementation step is as follows:
1. extract the pixel value difference feature of each sample: get at random two key points, generate at random an interpolation coefficient, obtain a position in 2 lines, two so locational pixel value differences are as feature.In the present invention, extract altogether the feature of 400 points.
2. selected characteristic: generated the feature of 400 points above, has 160000 combination of two.The random fern of using in the present invention returns in device and uses 5 stack features, in so large feature space, select 5 groups out.Method is as follows, first generates a random column vector, and true regressive object matrix is mapped in a direction, then calculates respectively the related coefficient of each proper vector and this projection vector, 5 groups of choosing related coefficient maximum.
3. the weak generation that returns device: the feature of extracting according to previous step, can be divided into original random fern sample and return in certain space of device.Calculate the average true shape increment Delta X of all samples in this space
bin, be added to when the each estimation in front space in shape, obtain new estimation shape.Return the input of device using the estimation shape obtaining as the original random fern of the next one, pass to next original random fern and return device, keep feature invariant, obtain new random fern and return device.Such 10 original random ferns are returned a little less than device cascade forms one and return device.
4. return by force the generation of device: through above-mentioned steps, learnt weak recurrence device f
k, for initial set X at the beginning of
k, can pass through f
kobtain returning increment and estimate Δ X
k, new first initial set can be by calculating X
k+ Δ X
kobtain, on new estimation shape basis, extract new feature, obtain according to the method described above next weak recurrence device, as shown in Figure 2, by that analogy, in the present embodiment, a little less than 100 of cascades, return device { f
1, f
2..., f
100, form the strong recurrence device of two layers.
So far, obtained complete by slightly returning device to smart cascade.
2. utilize cascade to return device facial image data are processed, to identify key point
This in the present invention can be obtained to extraordinary effect by slightly returning device to smart cascade in the time solving face key point orientation problem.
Specifically, in the time that face key point is located, a given original shape (can with average shape being snapped to face frame center) extracts SURF operator as proper vector in each key point, passes through Part I, obtain a rough result, set it as the original shape of Part II, in the position of key point, extract pixel value difference feature, use step by step, finally obtain accurate shape.Whole cascade returns device and follows by slightly to smart process, and Part I, adjusts large attitude, such as side face, pitching, rotation equal angles, face size, translational movement etc.The essence of Part II returns device, adjusts the details of shape, such as face shape, eye shape, eyebrow shape etc.Two parts, have reached into one by slightly returning device to smart cascade, in speed and effect, have very large lifting.
Fig. 4 adopts cascade recurrence device to carry out the schematic diagram of face key point location, wherein (a) is init state, (b) the face key point position obtaining through Part I robust regression device, (c) for returning through Part II essence the face key point position that device obtains.Can find out, adopt method of the present invention, through Part I robust regression device, face key point is revert near of true solution by initial value, and then approach true solution through Part II essence recurrence device, prove that method of the present invention can obtain good effect aspect face key point location.
Method of the present invention, processes a face, and on the computing machine of Intel (R) Core (TM) [email protected], the used time is about 8ms, speed be before the several times of method.The present invention is on LFPW data set, and average error is approximately the average error of the each point of 0.036(divided by interocular distance), reach industry advanced level.
Above embodiment is only in order to technical scheme of the present invention to be described but not be limited; those of ordinary skill in the art can modify or be equal to replacement technical scheme of the present invention; and not departing from the spirit and scope of the present invention, protection scope of the present invention should be as the criterion with described in claim.
Claims (10)
1. the face key point localization method returning based on cascade, its step comprises:
1) gather a large amount of face image datas, and the initial key point position of mark;
2) by described a large amount of face image datas are trained, study obtains a robust regression device, and then, using the output of described robust regression device as input, study obtains an essence and returns device, thereby obtains by slightly to smart cascade recurrence device;
3) given face picture to be identified and corresponding face position, by described robust regression device, the original shape of face is revert near true shape, then using the output of described robust regression device as input, return device and obtain the accurate coordinates of face key point by described essence.
2. the method for claim 1, is characterized in that: described robust regression device adopts linear regression device, extracts SURF feature at all key points place.
3. method as claimed in claim 2, is characterized in that: the linear regression device that described robust regression device is a cascade, the output of previous stage is as the input of rear one-level.
4. method as claimed in claim 3, is characterized in that: use SURF feature learning to obtain described linear regression device, concrete steps comprise:
1. on original shape, each key point place extracts initial SURF feature, is denoted as Φ
0, true regressive object is designated as Δ X*;
2. in training process, due to key point coordinate X, initial value key point coordinate X
0known, so the true regressive object Δ of key point X* be known, Δ X*=X-X
0; Linear regression device is expressed as 0X
0=R
0* Φ
0+ b
0, parameters R wherein
0and b
0try to achieve by minimizing following formula:
Wherein, d
ibe i face picture, X
0 ibe the original shape of i face, Δ X
* ibe the true review target of i face, Φ
0 ibe that i face is at original shape X
0 ithe SURF proper vector at place;
3. according to the R obtaining
0and b
0, obtain the increment Delta X estimating
0=R
0* Φ
1+ b
0, X+ Δ X
0as new training set, be designated as X
1; According to new training set, extract new SURF feature Φ
1, have Δ X
1=R
1* Φ
1+ b
1, in like manner, try to achieve R according to said method
1and b
1; By that analogy, obtain multistage linear regression device.
5. method as claimed in claim 4, is characterized in that: described robust regression device comprises two-stage linear regression device.
6. method as claimed in claim 1 or 2, is characterized in that: described essence returns device and adopts random fern cascade to return device, using pixel value difference as feature.
7. method as claimed in claim 6, is characterized in that: it is a double-layer structure that described essence returns device, and ground floor is a series of weak cascades that return device; The second layer is the cascade that a series of random ferns return device, forms a described weak device that returns.
8. method as claimed in claim 7, is characterized in that: the step that generates the essence recurrence device of described double-layer structure comprises:
1. extract the pixel value difference feature of each sample: get at random two key points, generate at random an interpolation coefficient, obtain a position in 2 lines, two so locational pixel value differences are as feature;
2. selected characteristic: generate a random column vector, true regressive object matrix is mapped in a direction, then calculate respectively the related coefficient of each proper vector and this projection vector, use random fern recurrence device to choose many stack features of related coefficient maximum;
3. the weak generation that returns device: the feature of extracting according to previous step, sample is divided into original random fern and returns in certain space of device, calculate the average true shape increment Delta X of all samples in this space
binbe added to when the each estimation in front space in shape, obtain new estimation shape, return the input of device using the estimation shape obtaining as the original random fern of the next one, pass to next original random fern and return device, keep feature invariant, obtain new random fern and return device, multiple original random ferns are returned a little less than device cascade forms one and return device;
4. return by force the generation of device: through above-mentioned steps, learnt weak recurrence device f
k, for initial set X at the beginning of
k, pass through f
kobtain returning increment and estimate Δ X
k, new first initial set is by calculating X
k+ Δ X
kobtain, on new estimation shape basis, extract new feature, obtain according to the method described above the next weak device that returns, by that analogy, the multiple weak devices that return of cascade, form the strong recurrence device of two layers.
9. method as claimed in claim 8, is characterized in that: the original random fern that the weak recurrence device that described essence returns device comprises 10 cascades returns device, the described weak device that returns that the strong recurrence device that described essence returns device comprises 100 cascades.
10. the method for claim 1, is characterized in that: described face key point comprises the position of eyes, nose, face, face mask.
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