Source: https://patents.google.com/patent/CN105005755B/en
Timestamp: 2020-05-25 00:00:22
Document Index: 573571780

Matched Legal Cases: ['in fine', 'in fine', 'in fine', 'in fine', 'in fine', 'in fine']

CN105005755B - Three-dimensional face identification method and system - Google Patents
Three-dimensional face identification method and system Download PDF
CN105005755B
CN105005755B CN201410171665.9A CN201410171665A CN105005755B CN 105005755 B CN105005755 B CN 105005755B CN 201410171665 A CN201410171665 A CN 201410171665A CN 105005755 B CN105005755 B CN 105005755B
CN201410171665.9A
CN105005755A (en
2014-04-25 Application filed by 北京邮电大学, 腾讯科技（深圳）有限公司 filed Critical 北京邮电大学
2014-04-25 Priority to CN201410171665.9A priority Critical patent/CN105005755B/en
2015-10-28 Publication of CN105005755A publication Critical patent/CN105005755A/en
2019-03-29 Publication of CN105005755B publication Critical patent/CN105005755B/en
The present invention provides a kind of three-dimensional face identification method and systems.The described method includes: by three dimensional face reference model to the binocular vision image of input to Attitude estimation is carried out, to obtain the virtual image pair of the relatively described binocular vision image pair of attitude parameter and three dimensional face reference model；With the virtual image to the facial depth image for rebuilding the binocular vision image pair for prior information；According to the attitude parameter detect characteristic point in the facial depth image corresponding to Local grid scale invariant feature descriptor；The recognition result of the binocular vision image pair is generated according to the training data for detecting obtained Local grid scale invariant feature descriptor and being attached to classification mark.It can be reduced using the present invention and calculate cost and required memory space.
Three-dimensional face identification method and system
The present invention relates to face image processing technologies, more particularly to a kind of three-dimensional face identification method and system.
Three-dimensional data processing is always a problem constantly challenged in terms of computer vision and computer graphics, point Analysis and identification face have become the important branch of three-dimensional data processing.
With the development of face recognition technology, two-dimension human face identification has been obtained for higher accuracy, still, is made Gray level image still cannot achieve for extreme applications scene and accurately identify, and not due to two-dimension human face identification Foot also pushes the improvement of recognition of face performance by the three-dimensional acquisition device of low cost, that is to say, that three dimensional face data and The face recognition technology that two-dimentional face data combines is suggested, to improve the validity and accuracy of recognition of face.
However, the face recognition technology that three dimensional face data and two-dimentional face data combine needs expensive calculating cost And huge memory space is required, and then be not particularly suited for the extensive use of face recognition technology.
Based on this, it is necessary to for technical problem as described above, provide a kind of can reduce and calculate cost and required storage The three-dimensional face identification method in space.
In addition, being also provided with a kind of three-dimensional face recognition system that can reduce calculating cost and required memory space.
A kind of three-dimensional face identification method, includes the following steps:
By three dimensional face reference model to the binocular vision image of input to Attitude estimation is carried out, to obtain attitude parameter With the virtual image pair of the relatively described binocular vision image pair of three dimensional face reference model；
With the virtual image to the facial depth image for rebuilding the binocular vision image pair for prior information；
According to the attitude parameter detect characteristic point in the facial depth image corresponding to Local grid Scale invariant Feature descriptor；
According to the training data for detecting obtained Local grid scale invariant feature descriptor and subsidiary classification mark Generate the recognition result of the binocular vision image pair.
A kind of three-dimensional face recognition system, comprising:
Input picture processing module, for passing through three dimensional face reference model to the binocular vision image of input to progress appearance State estimation, to obtain the virtual image pair of attitude parameter and three dimensional face reference model with respect to binocular vision image pair；
Image reconstruction module, for the virtual image to the face for rebuilding the binocular vision image pair for prior information Portion's depth image；
Detection module, for according to the attitude parameter detect characteristic point in the facial depth image corresponding to part Mesh scale invariant features descriptor；
Result-generation module, for detecting obtained Local grid scale invariant feature descriptor and subsidiary class according to described The training data not marked generates the recognition result of the binocular vision image pair.
Above-mentioned three-dimensional face identification method and system, by three dimensional face reference model to the binocular vision image pair of input Attitude estimation is carried out, to obtain the virtual image pair of attitude parameter and three dimensional face reference model with respect to binocular vision image pair, With virtual image to the facial depth image for rebuilding binocular vision image pair for prior information, it is deep that face is detected according to attitude parameter Spend Local grid scale invariant feature descriptor corresponding to characteristic point in image, and then the Local grid ruler obtained according to detection The recognition result for spending the training data generation binocular vision image pair of invariant features descriptor and subsidiary classification mark, due to part Mesh scale invariant features descriptor is the feature descriptor of a kind of scale and invariable rotary, can effectively overcome the complicated figure viewed from behind and pole The interference of application scenarios is held, reduces and calculates cost and required memory space, to effectively improve the robustness of three-dimensional face identification And consistency.
Fig. 1 is the flow chart of three-dimensional face identification method in one embodiment；
Fig. 2 be in Fig. 1 by three dimensional face reference model to the binocular vision image of input to carrying out Attitude estimation, with To the method flow diagram of attitude parameter and the virtual image pair of the opposite binocular vision image pair of three dimensional face reference model；
Fig. 3 is the left side visual pattern of binocular vision image pair in one embodiment；
Fig. 4 is the right side visual pattern of binocular vision image pair in one embodiment；
Fig. 5 is left side virtual image corresponding with left side visual pattern in Fig. 3；
Fig. 6 is right side virtual image corresponding with right side visual pattern in Fig. 4；
Fig. 7 is the side in Fig. 1 with virtual image to the facial depth image for rebuilding binocular vision image pair for prior information Method flow chart；
Fig. 8 is the schematic diagram of one embodiment septum reset depth image；
Fig. 9 is to carry out face in one embodiment to divide several obtained facial area schematic diagrames；
Figure 10 be in Fig. 1 according to attitude parameter detect characteristic point in facial depth image corresponding to Local grid scale not Become the method flow diagram of feature descriptor；
Figure 11 is that answering for the corresponding Local grid scale invariant feature descriptor of characteristic point is calculated in one embodiment Use schematic diagram；
Figure 12 is the Local grid scale invariant feature descriptor obtained in one embodiment according to detection and subsidiary classification The training data of mark generates the method flow diagram of the recognition result of binocular vision image pair；
Figure 13 carries out for visual coding corresponding to the visual coding and each sample in Figure 12 to binocular vision image pair It calculates, and the method flow diagram of the recognition result according to obtained calculated result generation binocular vision image pair；
Figure 14 is to construct the binocular to Add User by Local grid scale invariant feature descriptor in one embodiment Method flow diagram of the visual pattern to classification mark and visual coding for sample；
Figure 15 is the structural schematic diagram of three-dimensional face recognition system in one embodiment；
Figure 16 is the structural schematic diagram of input picture processing module in Figure 15；
Figure 17 is the structural schematic diagram of image reconstruction module in Figure 15；
Figure 18 is the structural schematic diagram of detection module in Figure 15；
Figure 19 is the structural schematic diagram of result-generation module in Figure 15；
Figure 20 is the structural schematic diagram of result computing unit in Figure 19；
Figure 21 is the structural schematic diagram that sample constructs module in one embodiment.
As shown in Figure 1, in one embodiment, a kind of three-dimensional face identification method includes the following steps:
Step 110, by three dimensional face reference model to the binocular vision image of input to Attitude estimation is carried out, to obtain The virtual image pair of attitude parameter and three dimensional face reference model with respect to binocular vision image pair.
In the present embodiment, three dimensional face reference model is the binocular face view shot in advance by the video camera calibrated Figure, wherein shooting obtained binocular facial view in advance includes left side facial view and right side facial view, left side face view Characteristic point coordinate is also predetermined in figure and right side facial view.
After the binocular vision image of input is to capturing and calibrating when being the authentication of user's progress three-dimensional face identification User a pair of of visual pattern, to guarantee subsequent accurate processing.Specifically, binocular vision image is to including left side vision figure Picture and right side visual pattern will meet epipolar-line constraint condition, i.e., the point in the visual pattern of left side on a horizontal line is on right side In visual pattern also on same horizontal line.
Under the action of three dimensional face reference model, to binocular vision image to progress Attitude estimation to obtain binocular vision The human face posture and virtual image pair of image pair, wherein human face posture will be characterized with attitude parameter, will indicate that binocular vision Face is mapped to the position in two-dimensional surface in the rotation angle and face of three-dimensional space in image.Virtual image is to then including Face with three dimensional face reference model, but the face is then consistent with the human face posture of binocular vision image pair and size A pair of of image.
Step 130, with virtual image to the facial depth image for rebuilding binocular vision image pair for prior information.
In the present embodiment, facial depth image is corresponding with the face of binocular vision image pair, and contains depth The image of information.Since virtual image is to being, the seat of virtual image centering feature point corresponding with three dimensional face reference model The information such as mark can be obtained by three dimensional face reference model, therefore, will carry out binocular to as prior information using virtual image The reconstruction of the facial depth image of visual pattern pair.
Step 150, the Local grid Scale invariant according to corresponding to characteristic point in the facial depth image of attitude parameter detection Feature descriptor.
In the present embodiment, characteristic point refers to the key point for carrying out face recognition, for example, characteristic point can be nose On point, the point on point and right eye on left eye etc., will be by can effectively reflect three dimensional face shape mesoscale and rotation Turn the accurate description of Local grid scale invariant feature descriptor (SIFT feature) Lai Jinhang characteristic point of invariance, ensure that The consistency of characteristic point, and then improve the accuracy and robustness of three dimensional face shape description.
Specifically, will test the characteristic point in facial depth image, if with obtain can accurate representation three dimensional face shape Dry characteristic point, and then the calculating of Local grid scale invariant feature descriptor is carried out, to obtain corresponding to each characteristic point Local grid scale invariant feature descriptor.
Step 170, the training of the Local grid scale invariant feature descriptor and subsidiary classification mark that are obtained according to detection The recognition result of data generation binocular vision image pair.
In the present embodiment, training data includes the sample and the incidental classification mark of each sample of several users Classification belonging to user will be marked in note, classification mark.For example, for each user, corresponding to sample and class Authentication Biao Zhu not being completed again for it, authority is provided.
Detection obtains Local grid scale invariant feature corresponding to the binocular vision image pair characteristic point of user's input After descriptor, the binocular vision figure of user's input will be realized according to Local grid scale invariant feature descriptor and training data As to and training data in sample comparison, and then know whether the user passes through authentication, to obtain identifying knot accordingly Fruit.
Local grid scale invariant feature descriptor is introduced to the binocular vision image of input by mode as described above To being described, and due to the scale and rotational invariance of Local grid scale invariant feature descriptor, so that input Binocular vision image pair description is more accurate and robustness and consistency more preferably, during having ensured three-dimensional face identification Robustness and consistency.
As shown in Fig. 2, in one embodiment, above-mentioned steps 110 include:
Step 111, the binocular vision image pair of input is obtained.
In the present embodiment, binocular vision image pair is obtained by the shooting that the video camera of calibration is carried out, and to shooting institute Obtained binocular vision image is to calibrating, so that visual pattern and right side visual pattern on the left of binocular vision image pair The position of middle respective point is on same horizontal line, by the binocular of calibration after the calibration for completing binocular vision image pair Visual pattern is to as the binocular vision image pair inputted in three-dimensional face identification.
Step 113, three dimensional face reference model is projected to obtain consistent to posture virtual with binocular vision image Image pair.
In the present embodiment, just traded on the basis of the binocular vision image pair of input to three dimensional face reference model Shadow, to obtain virtual image pair, wherein virtual image centering face shape by with the three dimensional face in three dimensional face reference model Shape is consistent, and still, the posture of virtual image centering face shape is by the posture of the binocular vision image pair face with input Consistent, the binocular vision image of input is to such as Fig. 3 and as shown in Figure 4, wherein Fig. 3 is left side visual pattern, and Fig. 4 is right side Visual pattern；Correspondingly, the binocular vision image of this input to corresponding virtual image to as shown in Figure 5 and Figure 6, In, Fig. 5 is left side virtual image, and Fig. 6 is right side virtual image.
Step 115, binocular vision progress Attitude estimation inputted according to three dimensional face reference model and virtual image Feel the attitude parameter of image pair.
In the present embodiment, the attitude parameter of the binocular vision image pair of input is binocular vision image pair face in three-dimensional The rotation angle in space, can be indicated with γ, θ and φ, wherein γ, θ and φ are the rotation around X, Y and Z axis in three-dimensional space respectively Gyration.
Specifically, characteristic point is in three-dimensional in the attitude parameter and three dimensional face reference model of binocular vision image pair face Meet following relationship between the coordinate in space, the two-dimensional map point of characteristic point coordinate, i.e.,
Wherein, x=(x, y, z)TIndicate three dimensional face reference model in characteristic point three-dimensional space coordinate, p=(px, py)TIndicate the two-dimensional map point of characteristic point coordinate, R=RγRθRφIndicate that spin matrix, γ, θ and φ are around three-dimensional space respectively Between middle X, Y and Z axis rotation angle, the X in three-dimensional space, Y and Z axis are and the three dimensional face shape in three dimensional face reference model Posture corresponding to shape is relevant, t2dIt is the displacement in two-dimensional surface, f is focal length relevant to three dimensional face length.
Attitude parameter will be carried out by Levenberg Marquardt algorithm (column Wen Baige-Ma Kuaertefa algorithm) Estimation, with the attitude parameter of the binocular vision image pair inputted, wherein optimal solution function as shown by the following formula:
Wherein, (Fx,j,Fy,j) it is characterized the two-dimensional map point position of a coordinate, (px,j,py,j) it is matched three The position of dimensional feature point.
As shown in fig. 7, in one embodiment, above-mentioned steps 130 include:
Step 131, face parallax is obtained to the corresponding relationship operation between virtual image pair according to binocular vision image Value.
In the present embodiment, since virtual image is to resulting from three dimensional face reference model, and on the left of virtual image centering Corresponding relationship between virtual image and right side virtual image be it is known, therefore, will be using virtual image to as being used to mark Determine the priori knowledge of the position for the point that binocular vision image pair is mutually matched.
Specifically, the calibration for the point that binocular vision image pair is mutually matched can be realized by following process:
(1) according to the corresponding relationship of left side visual pattern and left side virtual image obtain left side visual pattern it is virtual in left side Corresponding points on image；
(2) left side void is obtained according to the corresponding relationship on the left of virtual image centering between virtual image and right side virtual image Quasi- corresponding points of the image on the virtual image of right side；
(3) according to the corresponding relationship of right side virtual image and right side visual pattern obtain right side visual pattern it is virtual on right side Corresponding points on image；
On the basis of the point on right side visual pattern and left side visual pattern meets epipolar-line constraint relationship, then right side vision Image is three-dimensional between the corresponding points on the virtual image of left side in the corresponding points on the virtual image of right side and left side visual pattern Matched two corresponding points.
Level difference between matched corresponding points is face parallax value.That is, will acquire two between each other The abscissa of the corresponding points of Stereo matching, the difference between two abscissas is face parallax value.
It should be noted that the calibration for the point that binocular vision image pair is mutually matched can also be by right side visual pattern and the right side The corresponding relationship of side virtual image is starting, then via the corresponding relationship and left side of right side virtual image and left side virtual image The corresponding relationship of virtual image and left side visual pattern obtains Stereo matching between left side visual pattern and right side visual pattern Two corresponding points.
Therefore, the point being mutually matched in binocular vision image pair calibration sequence can flexible transformation, be not limited in institute The sequence enumerated.
Step 133, the camera calibration parameter of shooting binocular vision image pair is combined to obtain rough depth face difference As a result.
In the present embodiment, rough depth results are a rough parallax results.Camera calibration parameter will be by imaging The parameter calibration that machine is carried out is obtained.Specifically, by one diaphragm plate of printing and being attached in a plane, shoot from different perspectives To several diaphragm plate images, and the characteristic point in diaphragm plate image is detected, to find out internal reference, outer ginseng and the distortion ginseng of video camera Number, the internal reference of the video camera and outer ginseng, distortion parameter are camera calibration parameter.
Step 135, it is blocked using virtual image to obtaining binocular vision image pair face pixel as prior information Situation optimizes processing according to the situation that is blocked of facial pixel, to obtain fine depth results.
In the present embodiment, in conjunction with virtual image to this facial prior information, i.e., left side vision figure in binocular vision image The corresponding relationship of picture and left side virtual image, the corresponding relationship of left side virtual image and right side virtual image, right side virtual image The situation that is blocked of binocular vision image pair face pixel is obtained with the corresponding relationship of right side visual pattern.
Specifically, by the facial pixel x of binocular vision image pair is obtained according to facial prior information in left side vision figure Whether it is blocked as in, if being blocked, the situation O that is blocked of corresponding face pixel xL(x)=1, otherwise OL(x)=0, phase It answers, ORIt also is similar definition.
Facial key area is carried out according to the situation that is blocked of binocular vision image pair face pixel further excellent Change processing, to obtain fine depth results.
Wherein, facial key area is the key area for carrying out face recognition, for example, the face key area can be Nasal area in the face of binocular vision image pair.
Further, face depth value D can be calculated by following formula:
minE,s.t.E(D,O；I)=Ed(D,O；I)+Es(D,O；I)+Ev(D,O；I) (3)
Wherein, Ed(D,O；It I) is data in binocular vision image, Es(D,O；It I) is flatness parameter, Ev(D,O；I it) retouches The visibility characteristics of three-dimensional data are stated.
It will definition
Wherein,(D,O；It I is) energy function of foreground area,(D,O；I) for providing foreground area, WithThe probability of foreground area and background area septum reset pixel x is respectively represented.
Wherein, PFIt (x) is prospect probability, PBIt (x) is background probability, S is foreground area mapping, and Dis (x) is by facial picture For plain x to the Euclidean distance of partitioning boundary, Tb is preset threshold value.
Therefore, Ed(D,O；I it) can further express are as follows:
Wherein, ILFor left side visual pattern, IRFor right side visual pattern, ρ (x, D (x)；I facial pixel x and difference) are described The robustness of matching cost, Z between differentnIt (x) is the normalized parameter of the matching cost of each facial pixel x, matching cost η is then It is used to quantify to block the ratio shared by whole image.
The local smoothing method in obtained facial depth image is rebuild with effective reflection.
Wherein, N (x) is one group of neighborhood pixels of facial pixel x, λ (x, y) be it is consistent with sudden change it is discontinuous because Son, ρ are defined as ρsThe robust formula of (D (x), D (y))=min (D (x)-D (y), T), T will be used to control the cost upper limit.
After face depth value D is calculated, fine depth results that will be rebuild by Camera calibration parameter Z, i.e.,
Wherein, F is the focal length of video camera, and b is the parallax range of video camera.
The excellent performance of three dimensional face recovery has been obtained in conjunction with prior information, or even in poor illumination condition and there is screening It still is able to accurately realize that three dimensional face is restored in the case where gear, improves the stability and speed of three-dimensional face identification.
Step 137, the facial depth image of binocular vision image pair is formed by rough depth results and fine depth results.
In the present embodiment, obtained facial key area by rough depth results and fine depth results from blocking and complicated The facial depth image for refining out in background, for example, as shown in Figure 8.
It is rebuild by facial depth image as described above, stereoscopic rendering will have been carried out to three-dimensional face, be truly anti- The geometric consequence for reflecting input face is laid a good foundation.
In another embodiment, before above-mentioned steps 150, this method further include:
Facial segmentation is carried out to facial depth image according to shape indexing value and shape band, to obtain several facial areas Domain.
In the present embodiment, shape indexing value and shape band are the form parameters for characterizing face shape, wherein shape Index value is obtained according to three-dimensional surface curvature estimation, and shape indexing value and shape band will be used to carry out facial segmentation.
Specifically, by the shape feature intrinsic as the different facial areas of segmentation using shape indexing value and shape band.Example Such as, by using the shape band of ocular as search window, Gaussian curvature is determined as eye socket position close to zero position, it will The point of the rightmost side and the leftmost side is found by shape band, obtains the nose boundary as saddle groove shapes to extract, more than eye Region will be regarded as forehead and white space, the region of left eye region to mouth will be left cheek region, right eye region to mouth Region will be right cheek region, as shown in Figure 9.
As shown in Figure 10, in one embodiment, above-mentioned steps 150 include:
Step 151, several facial areas that confronting portions are cut are calculated includes in facial area to obtain Three dimensional face grid.
In the present embodiment, cut by Delaunay algorithm (Delaunay triangulation algorithm) confronting portions several Facial area is calculated to obtain the three dimensional face grid that each facial area is included.
Step 153, the characteristic point in three dimensional face grid is detected.
In the present embodiment, the constant Gaussian filter of Application density is calculated to the morphology of three dimensional face grid, And then obtain the characteristic point in three dimensional face grid.
Specifically, by the detection with 8*8 three dimensional face grid for one group of carry out characteristic point.
With 8*8 three dimensional face grid for one group of building Gaussian difference scale space, i.e. DOG function, vertex is The vertex of next group of 8*8 three dimensional face grid isI.e.
Wherein,It is expressed as one groupSingle order it is adjacent nontoxic,For between each other apart from constant vertex.
DoG functionIt will be calculated by following formula, i.e.,
Wherein, σSFor scale factor (filter width), by being calculated,
At this point, obtained local scale maximum of points is characteristic point, i.e.,Numerical value is maximum point.
Step 155, the corresponding local area network of characteristic point that detection obtains is calculated according to the attitude parameter of binocular vision image pair Lattice scale invariant feature descriptor.
In the present embodiment, retouched Local grid scale invariant feature is calculated by the histogram of gradients of each characteristic point State symbol.To each characteristic point for detecting and obtaining, it will be set in the subpoint of xy, yz and xz plane, and by three-dimensional gradient space And corresponding Local grid scale invariant feature descriptor is calculated in the attitude parameter of binocular vision image pair, such as Figure 11 institute Show, wherein IxIt is characterized a little x-axis coordinate corresponding to x/y plane or the subpoint of xz plane, IyBe characterized a little in x/y plane or Y-axis coordinate corresponding to the subpoint of yz plane,It is characterized a little y-axis coordinate corresponding to the subpoint of yz plane,For Characteristic point y-axis coordinate corresponding to the subpoint of xz plane, parameter γ, α and β are then obtained by attitude parameter.
It follows that the image gradient on horizontally and vertically will be calculated by following formula:
Wherein,WithIt is the gradient on the direction x and the direction y respectively, and then the image gradient being calculated is Local grid scale invariant feature descriptor.
As shown in figure 11, in each three dimensional face grid, the gradient side of arrow direction facial pixel where representing To arrow length represents gradient modulus value.It will be with 8 gradients of 4*4 three dimensional face grid computing in 8*8 three dimensional face grid The accumulated value in direction just forms a seed point to draw the histogram in each direction herein, wherein a characteristic point will By 2*2, totally 4 seed points are formed, and each seed point is one group of 8 dimensional vector, and 8 dimensional vectors corresponding to this 4 seed points are just constituted The Local grid scale invariant feature descriptor of characteristic point, since its gradient direction distribution characteristic makes the local area network of characteristic point Lattice scale invariant feature descriptor has rotational invariance.
Local grid scale invariant feature descriptor obtained by calculation will be effectively treated extreme posture and block, illumination The degeneration of caused missing data, and then ensure that accuracy.
As shown in figure 12, in one embodiment, above-mentioned steps 170 include:
Step 171, the training data for obtaining subsidiary classification mark is extracted to obtain every by the training data that subsidiary classification marks Visual coding corresponding to one sample.
In the present embodiment, training data by include several users sample and the incidental classification mark of each sample Note, wherein the description of sample will be carried out using Local grid scale invariant feature descriptor, and to the constant spy of local mesh scale Sign descriptor obtains the corresponding visual coding of the sample after being quantified, and therefore, each sample standard deviation has corresponding vision to compile Code, will carry out the ratio between several samples and binocular vision image pair currently entered by the corresponding visual coding of sample It is right.
Step 173, it is obtained indicating binocular vision image according to the Local grid scale invariant feature descriptor that detection obtains Pair visual coding.
In the present embodiment, Local grid Scale invariant corresponding to the characteristic point that is detected to binocular vision image pair Feature descriptor is quantified, to obtain corresponding visual coding.
Step 175, visual coding corresponding to the visual coding to binocular vision image pair and each sample calculates, And the recognition result of binocular vision image pair is generated according to obtained calculated result.
In the present embodiment, calculate between visual coding corresponding to the visual coding and each sample of binocular vision image pair Similarity degree, and then obtain with binocular vision image to the most similar sample, by the classification mark according to corresponding to the sample Note generates recognition result.
As shown in figure 13, in one embodiment, above-mentioned steps 175 include:
Step 1751, it calculates between visual coding corresponding to the visual coding and each sample of binocular vision image pair Euclidean distance.
In the present embodiment, the visual coding as corresponding to the visual coding and each sample of binocular vision image pair is To the numerical value after the quantization of local mesh scale invariant features descriptor, therefore, by with the visual coding of binocular vision image pair and Visual coding corresponding to each sample is according to corresponding to the visual coding and each sample for calculating binocular vision image pair Euclidean distance between visual coding.
Step 1753, it is extracted from the Euclidean distance being calculated and obtains minimum Eustachian distance.
In the present embodiment, several Euclidean distances will be calculated, binocular vision image will be characterized between sample Similarity degree, that is, Euclidean distance is bigger, then binocular vision image is lower to the similarity degree between sample；Euclidean distance Smaller, then binocular vision image will be higher to the similarity degree between sample.
Based on this, the smallest Euclidean distance of numerical value will be obtained by extracting in several Euclidean distances for being calculated.
Step 1755, visual coding corresponding to sample where obtaining minimum Eustachian distance, and it is subsidiary to extract visual coding Classification mark, extract classification mark be binocular vision image pair recognition result.
In another embodiment, before above-mentioned steps 170, this method further include:
The binocular vision image to Add User is constructed by Local grid scale invariant feature descriptor to for sample Classification mark and visual coding.
In the present embodiment, when Adding User, it will need to add the corresponding sample that Adds User in training data And corresponding classification mark is enclosed for the sample.
Specifically, obtaining the characteristic point and characteristic point of binocular vision image pair by step 110 to step 150 detection It, will be to the Local grid scale invariant feature descriptor amount of progress after corresponding Local grid scale invariant feature descriptor Change, with the incidental classification mark of the sample, the sample that are Added User and visual coding.
As shown in figure 14, in one embodiment, it constructs above by Local grid scale invariant feature descriptor with new Add the binocular vision image at family includes: to the step of classification mark and visual coding for sample
Step 701, with the binocular vision image that Adds User to for one group of Local grid scale invariant feature of sample extraction Descriptor, and set corresponding classification mark.
In the present embodiment, the local feature of N number of D dimension of the binocular vision image pair to Add User, the i.e. office of D dimension are extracted Portion's mesh scale invariant features descriptor, is denoted as L=[l1,l2,…,lN]∈RD×N。
Step 703, the one group of Local grid scale invariant feature descriptor extracted according to the coefficient histogram of sample Rarefaction representation.
In the present embodiment, this group of Local grid scale invariant feature extracted is calculated by the coefficient histogram of sample and is retouched The rarefaction representation of symbol is stated, specifically, C=[c will be defined1,c2,…,cN]∈RMIt, will be by optimizing as follows for the rarefaction representation of L Problem obtains corresponding rarefaction representation, i.e.,
Wherein, B is the visual vocabulary book of building, is defined as B=[b1,b2,…,bM]∈RD×M, it is one group in D dimension space Complete base is crossed, // // is not this black norm (the Frobenius norm) of Luo Beini, // //0It is to be carried out to nonzero element Count obtained l0Norm, vector ciThe sparse coefficient of ∈ C will be related to specified classification.
Therefore, the sample of each user will be calculated using coefficient histogram, i.e.,Local grid is calculated The rarefaction representation of scale invariant feature descriptor.
Wherein, ci∈RMIt is C ∈ RM×NI-th of Local grid scale invariant feature descriptor, N is office in sample The sum of portion's mesh scale invariant features descriptor, hj∈RM。
Step 705, pass through the rarefaction representation of one group of Local grid scale invariant feature descriptor and the one group of part extracted Mesh scale invariant features descriptor obtains the visual coding of description sample.
In the present embodiment, one group of Local grid scale invariant feature descriptor that extraction is calculated rarefaction representation it Afterwards, optimization problem as described above will be solved, i.e. formula (13) obtains newly-increased use to obtain corresponding visual vocabulary book Visual coding corresponding to the sample at family.
The rarefaction representation of Local grid scale invariant feature descriptor will describe its Local grid scale invariant feature The method for carrying out sparse constraint that is mapping through accorded with to the feature space of visual vocabulary book indicates, to realize effective table of feature Show.
Further, in visual vocabulary book, every sample will be all encoded by coefficient histogram to obtain corresponding vision Coding, in order to subsequent face recognition process.
By three-dimensional face identification method as described above, learn to construct by Local grid scale invariant feature descriptor The visual vocabulary book of virtual progress rarefaction representation is realized, can effectively overcome the influence that self is blocked, have high knowledge Other speed, and complexity is lower, it is easier to implement.
The shape feature of each face face can be captured by Local grid scale invariant feature descriptor and effective Facial area, i.e., drawing three-dimensional geological information in facial key area has the consistent of good robustness identification Property.
As shown in figure 15, in one embodiment, a kind of three-dimensional face recognition system, including input picture processing module 810, image reconstruction module 830, detection module 850 and result-generation module 870.
Input picture processing module 810, for by three dimensional face reference model to the binocular vision image of input into Row Attitude estimation, to obtain the virtual image pair of attitude parameter and three dimensional face reference model with respect to binocular vision image pair.
Under the action of three dimensional face reference model, input picture processing module 810 is to binocular vision image to progress appearance State is estimated to obtain the human face posture and virtual image pair of binocular vision image pair, wherein human face posture will be with attitude parameter Characterization, will indicate that face is mapped in two-dimensional surface in the rotation angle and face of three-dimensional space in binocular vision image Position.Virtual image to the face then contained with three dimensional face reference model, but the face then with binocular vision image pair Human face posture and consistent a pair of of the image of size.
Image reconstruction module 830, for deep to the face for rebuilding binocular vision image pair for prior information with virtual image Spend image.
In the present embodiment, facial depth image is corresponding with the face of binocular vision image pair, and contains depth The image of information.Since virtual image is to being, the seat of virtual image centering feature point corresponding with three dimensional face reference model The information such as mark can be obtained by three dimensional face reference model, and therefore, image reconstruction module 830 will be using virtual image to as priori Information carries out the reconstruction of the facial depth image of binocular vision image pair.
Detection module 850, for the Local grid according to corresponding to characteristic point in the facial depth image of attitude parameter detection Scale invariant feature descriptor.
In the present embodiment, characteristic point refers to the key point for carrying out face recognition, for example, characteristic point can be nose On point, the point on point and right eye on left eye etc., will be by can effectively reflect three dimensional face shape mesoscale and rotation Turn the Local grid scale invariant feature descriptor of invariance to carry out the accurate description of characteristic point, ensure that the one of characteristic point Cause property, and then improve the accuracy and robustness of three dimensional face shape description.
Specifically, detection module 850 will test the characteristic point in facial depth image, with obtain can accurate representation three-dimensional surface Several characteristic points of portion's shape, and then the calculating of Local grid scale invariant feature descriptor is carried out, to obtain each feature The corresponding Local grid scale invariant feature descriptor of point.
Result-generation module 870, Local grid scale invariant feature descriptor and subsidiary class for being obtained according to detection The training data not marked generates the recognition result of binocular vision image pair.
Detection obtains Local grid scale invariant feature corresponding to the binocular vision image pair characteristic point of user's input After descriptor, result-generation module 870 will realize user according to Local grid scale invariant feature descriptor and training data The binocular vision image of input to and training data in sample comparison, and then know whether the user passes through authentication, with Obtain corresponding recognition result.
As shown in figure 16, in one embodiment, above-mentioned input picture processing module 810 include image acquisition unit 811, Projecting cell 813 and Attitude estimation unit 815.
Image acquisition unit 811, for obtaining the binocular vision image pair of input.
In the present embodiment, binocular vision image pair is obtained by the shooting that the video camera of calibration is carried out, and to shooting institute Obtained binocular vision image is to calibrating, so that visual pattern and right side visual pattern on the left of binocular vision image pair The position of middle respective point is on same horizontal line, the image acquisition unit after the calibration for completing binocular vision image pair 811 by the binocular vision image of calibration to as the binocular vision image pair inputted in three-dimensional face identification.
Projecting cell 813, for being projected to obtain with binocular vision image three dimensional face reference model to posture one The virtual image pair of cause.
In the present embodiment, projecting cell 813 is on the basis of the binocular vision image pair of input to three dimensional face reference model Carry out rectangular projection, to obtain virtual image pair, wherein virtual image centering face shape will in three dimensional face reference model Three dimensional face shape it is consistent, still, the posture of virtual image centering face shape is by the binocular vision image pair with input The posture of face is consistent.
Attitude estimation unit 815, it is defeated for being obtained according to three dimensional face reference model and virtual image progress Attitude estimation The attitude parameter of the binocular vision image pair entered.
Attitude estimation unit 815 will carry out the estimation of attitude parameter by Levenberg Marquardt algorithm, to obtain The attitude parameter of the binocular vision image pair of input, wherein optimal solution function as shown by the following formula:
As shown in figure 17, in one embodiment, above-mentioned image reconstruction module 830 include parallax arithmetic element 831, it is rough Depth calculation unit 833, optimization processing unit 835 and facial depth image form unit 837.
Parallax arithmetic element 831, for according to binocular vision image to the corresponding relationship operation between virtual image pair Obtain face parallax value.
In the present embodiment, since virtual image is to resulting from three dimensional face reference model, and on the left of virtual image centering Corresponding relationship between virtual image and right side virtual image be it is known, therefore, parallax arithmetic element 831 will use virtual graph As to the priori knowledge as the position for demarcating the point that binocular vision image pair is mutually matched.
Specifically, the calibration for the point that the binocular vision image pair that parallax arithmetic element 831 is carried out is mutually matched can lead to Following process is crossed to realize:
Level difference between matched corresponding points is face parallax value.That is, parallax arithmetic element 831 will obtain The abscissa of the corresponding points of two mutual Stereo matchings is taken, the difference between two abscissas is face parallax value.
Rough depth calculation unit 833, for face parallax value to be combined to the video camera mark of shooting binocular vision image pair Determine parameter and obtains rough depth results.
Optimization processing unit 835, for obtaining binocular vision image pair face to as prior information using virtual image The situation that is blocked of pixel optimizes processing according to the situation that is blocked of facial pixel, to obtain fine depth results.
In the present embodiment, optimization processing unit 835 combines virtual image to this facial prior information, i.e. binocular vision figure The corresponding relationship of left side visual pattern and left side virtual image, left side virtual image and the corresponding of right side virtual image are closed as in The corresponding relationship of system, right side virtual image and right side visual pattern obtains the feelings that are blocked of binocular vision image pair face pixel Condition.
Specifically, optimization processing unit 835 will obtain the facial picture of binocular vision image pair according to facial prior information Whether plain x is blocked in the visual pattern of left side, if being blocked, the situation O that is blocked of corresponding face pixel xL(x)=1, Otherwise OL(x)=0, correspondingly, ORIt also is similar definition.
Optimization processing unit 835 is according to the situation that is blocked of binocular vision image pair face pixel to facial key area Further optimization processing is carried out, to obtain fine depth results.
Wherein, N (x) is one group of neighborhood pixels of facial pixel x, λ (x, y) be it is consistent with sudden change it is discontinuous because Son, ρ are defined as ρsThe robust formula of (D (x), D (y))=min (| D (x)-D (y) |, T), T will be used to control the cost upper limit.
After face depth value D is calculated, optimization processing unit 835 will be rebuild by Camera calibration parameter Fine depth results Z, i.e.,
Facial depth image forms unit 837, for forming binocular vision by rough depth results and fine depth results The facial depth image of image pair.
In the present embodiment, facial depth image formed unit 837 by rough depth results and fine depth results obtain by Facial key area is from the facial depth image for blocking with refining out in complex background.
In one embodiment, system as described above further comprises face segmentation module.Face segmentation module is used for Facial segmentation is carried out to facial depth image according to shape indexing value and shape band, to obtain several facial areas.
In the present embodiment, shape indexing value and shape band are the form parameters for characterizing face shape, wherein shape Index value is obtained according to three-dimensional surface curvature estimation, and face segmentation module will use shape indexing value and shape band to carry out face Portion's segmentation.
Specifically, face segmentation module by using shape indexing value and shape band as divide different facial areas inherently Shape feature.For example, face segmentation module is by using the shape band of ocular as search window, by Gaussian curvature close to zero Position be determined as eye socket position, the point of the rightmost side and the leftmost side will be found by shape band, be obtained with extracting as saddle groove The nose boundary of shape, region more than eye will be regarded as forehead and white space, and the region of left eye region to mouth will be a left side Cheek region, the region of right eye region to mouth will be right cheek region.
As shown in figure 18, in one embodiment, above-mentioned detection module 850 includes grid computing unit 851, characteristic point inspection Survey unit 853 and descriptor computation unit 855.
Grid computing unit 851, several facial areas cut for confronting portions are calculated to obtain face The three dimensional face grid for including in region.
In the present embodiment, several faces that grid computing unit 851 is cut by Delaunay algorithm confronting portions Region is calculated to obtain the three dimensional face grid that each facial area is included.
Characteristic point detection unit 853, for detecting the characteristic point in three dimensional face grid.
In the present embodiment, the constant Gaussian filter of Application density is calculated three dimensional face net by characteristic point detection unit 853 The morphology of lattice, and then obtain the characteristic point in three dimensional face grid.
Specifically, characteristic point detection unit 853 by with 8*8 three dimensional face grid be one group of carry out characteristic point detection.
Descriptor computation unit 855, for calculating the feature that detection obtains according to the attitude parameter of binocular vision image pair The corresponding Local grid scale invariant feature descriptor of point.
In the present embodiment, part will be calculated by the histogram of gradients of each characteristic point in descriptor computation unit 855 Mesh scale invariant features descriptor.Detect obtained characteristic point to each, descriptor computation unit 855 will be arranged its xy, The subpoint of yz and xz plane, and be calculated accordingly by the attitude parameter in three-dimensional gradient space and binocular vision image pair Local grid scale invariant feature descriptor, as shown in figure 11, wherein IxIt is characterized a little in x/y plane or the subpoint of xz plane Corresponding x-axis coordinate, IyIt is characterized a little y-axis coordinate corresponding to x/y plane or the subpoint of yz plane,It is characterized a little The y-axis coordinate corresponding to the subpoint of yz plane,It is characterized a little y-axis coordinate corresponding to the subpoint of xz plane, is joined Number γ, α and β are then obtained by attitude parameter.
As shown in figure 19, in one embodiment, the above results generation module 870 includes coding extraction unit 871, input Image encodes acquiring unit 873 and result computing unit 875.
Extraction unit 871 is encoded, for obtaining the training data of subsidiary classification mark, the training number marked by subsidiary classification The corresponding visual coding of each sample is obtained according to extraction.
In the present embodiment, training data by include several users sample and the incidental classification mark of each sample Note, wherein the description of sample will be carried out using Local grid scale invariant feature descriptor, encode 871 pairs of parts of extraction unit Mesh scale invariant features descriptor obtains the corresponding visual coding of the sample after being quantified, therefore, each sample standard deviation have with Its corresponding visual coding will carry out several samples and binocular vision figure currently entered by the corresponding visual coding of sample As the comparison between.
Input picture encodes acquiring unit 873, for describing to the Local grid scale invariant feature obtained according to detection Symbol obtains indicating the visual coding of binocular vision image pair.
In the present embodiment, input picture encodes the characteristic point that acquiring unit 873 detects binocular vision image pair Corresponding Local grid scale invariant feature descriptor is quantified, to obtain corresponding visual coding.
As a result computing unit 875, for vision corresponding to the visual coding and each sample to binocular vision image pair Coding is calculated, and the recognition result of binocular vision image pair is generated according to obtained calculated result.
In the present embodiment, the visual coding of the as a result calculating of computing unit 875 binocular vision image pair and each sample institute are right The similarity degree between visual coding answered, and then obtain with binocular vision image to the most similar sample, it will be according to the sample Classification mark corresponding to this generates recognition result.
As shown in figure 20, in one embodiment, as a result computing unit 875 includes apart from computation subunit 8751, distance It extracts subelement 8753 and classification mark extracts subelement 8755.
Apart from computation subunit 8751, for calculating corresponding to the visual coding and each sample of binocular vision image pair Euclidean distance between visual coding.
In the present embodiment, the visual coding as corresponding to the visual coding and each sample of binocular vision image pair is It therefore will be with binocular vision apart from computation subunit 8751 to the numerical value after the quantization of local mesh scale invariant features descriptor Visual coding corresponding to the visual coding of image pair and each sample is according to the visual coding for calculating binocular vision image pair Euclidean distance between visual coding corresponding to each sample.
Distance extracts subelement 8753, obtains minimum Eustachian distance for extracting from the Euclidean distance being calculated.
Based on this, distance, which extracts subelement 8753, to obtain numerical value most by extracting in several Euclidean distances for being calculated Small Euclidean distance.
Classification mark extracts subelement 8755, for the corresponding visual coding of sample where obtaining minimum Eustachian distance, and The subsidiary classification mark of visual coding is extracted, the classification mark extracted is the recognition result of binocular vision image pair.
In one embodiment, system as described above further comprises sample building module.Sample building module is used for The binocular vision image to Add User is constructed by Local grid scale invariant feature descriptor to the classification mark for sample And visual coding.
In the present embodiment, when Adding User, sample building module, which will need to add this in training data, to Add User Corresponding sample and corresponding classification mark is enclosed for the sample.
Specifically, being obtained by input picture processing module 810, image reconstruction module 830, the detection of detection module 850 After Local grid scale invariant feature descriptor corresponding to the characteristic point and characteristic point of binocular vision image pair, sample structure Modeling block will quantify the Local grid scale invariant feature descriptor, with Added User sample, appended by sample The classification of band marks and visual coding.
As shown in figure 21, in one embodiment, above-mentioned sample building module 910 include descriptor abstraction unit 911, it is dilute Dredge extraction unit 913 and coding acquiring unit 915.
Descriptor abstraction unit 911, for the binocular vision image that Adds User to for one group of local area network of sample extraction Lattice scale invariant feature descriptor, and set corresponding classification mark.
In the present embodiment, descriptor abstraction unit 911 extracts N number of D dimension of the binocular vision image pair to Add User The Local grid scale invariant feature descriptor of local feature, i.e. D dimension, is denoted as L=[l1,l2,…,lN]∈RD×N。
Sparse extraction unit 913, the one group of Local grid scale extracted for the coefficient histogram according to sample is not Become the rarefaction representation of feature descriptor.
In the present embodiment, sparse extraction unit 913 calculates this group of local area network extracted by the coefficient histogram of sample The rarefaction representation of lattice scale invariant feature descriptor, specifically, C=[c will be defined1,c2,…,cN]∈RMFor the rarefaction representation of L, Corresponding rarefaction representation will be obtained by following optimization problem, i.e.,
Wherein, B is the visual vocabulary book of building, is defined as B=[b1,b2,…,bM]∈RD×M, it is one group in D dimension space Complete base is crossed, // // is not this black norm (the Frobenius norm) of Luo Beini, //0It is to be counted to nonzero element The obtained l of number0Norm, vector ciThe sparse coefficient of ∈ C will be related to specified classification.
Therefore, sparse extraction unit 913 will calculate the sample of each user using coefficient histogram, i.e.,Meter Calculation obtains the rarefaction representation of Local grid scale invariant feature descriptor.
Acquiring unit 915 is encoded, for the rarefaction representation by one group of Local grid scale invariant feature descriptor and is mentioned The one group of Local grid scale invariant feature descriptor taken obtains the visual coding of description sample.
In the present embodiment, one group of Local grid scale invariant feature descriptor that extraction is calculated rarefaction representation it Afterwards, coding acquiring unit 915 will solve optimization problem as described above, i.e., formula (13) is to obtain corresponding visual vocabulary This, and then visual coding corresponding to the sample to be Added User.
By three-dimensional face recognition system as described above, learn to construct by Local grid scale invariant feature descriptor The visual vocabulary book of virtual progress rarefaction representation is realized, can effectively overcome the influence that self is blocked, have high knowledge Other speed, and complexity is lower, it is easier to implement.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with Relevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage medium In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access Memory, RAM) etc.
1. a kind of three-dimensional face identification method, includes the following steps:
By three dimensional face reference model to the binocular vision image of input to Attitude estimation is carried out, to obtain attitude parameter and three Tie up the virtual image pair of the relatively described binocular vision image pair of facial reference model；
It is generated according to the training data for detecting obtained Local grid scale invariant feature descriptor and subsidiary classification mark The recognition result of the binocular vision image pair.
2. the method according to claim 1, wherein it is described by three dimensional face reference model to the binocular of input Visual pattern is to Attitude estimation is carried out, to obtain the relatively described binocular vision image pair of attitude parameter and three dimensional face reference model The step of virtual image pair include:
Obtain the binocular vision image pair of input；
Three dimensional face reference model is projected to obtain with the binocular vision image to the consistent virtual image pair of posture；
The binocular vision image pair of the input is obtained to Attitude estimation is carried out according to three dimensional face reference model and virtual image Attitude parameter.
3. the method according to claim 1, wherein described rebuild institute to for prior information with the virtual image The step of stating the facial depth image of binocular vision image pair include:
Face parallax value is obtained to the corresponding relationship operation between virtual image pair according to binocular vision image；
The face parallax value is obtained into rough depth knot in conjunction with the camera calibration parameter for shooting the binocular vision image pair Fruit；
The situation that is blocked of binocular vision image pair face pixel is obtained to as prior information using virtual image, according to described The situation that is blocked of facial pixel optimizes processing, to obtain fine depth results；
The facial depth image of the binocular vision image pair is formed by the rough depth results and the fine depth results.
4. the method according to claim 1, wherein described according to the attitude parameter detection facial depth In image corresponding to characteristic point the step of Local grid scale invariant feature descriptor before, the method also includes:
Facial segmentation is carried out to the facial depth image according to shape indexing value and shape band, to obtain several facial areas Domain.
5. according to the method described in claim 4, it is characterized in that, described according to the attitude parameter detection facial depth Include: the step of Local grid scale invariant feature descriptor corresponding to characteristic point in image
Several facial areas obtained to the face segmentation are calculated to obtain the three-dimensional for including in the facial area Facial grid；
Detect the characteristic point in the three dimensional face grid；
It is calculated according to the attitude parameter of binocular vision image pair and described detects the obtained corresponding Local grid scale of characteristic point not Become feature descriptor.
6. the method according to claim 1, wherein the Local grid scale obtained according to the detection is not Become feature descriptor and is attached to the step of training data that classification marks generates the recognition result of the binocular vision image pair packet It includes:
The training data for obtaining subsidiary classification mark is extracted to obtain each sample institute by the training data of the subsidiary classification mark Corresponding visual coding；
It obtains indicating the binocular vision image pair according to the obtained Local grid scale invariant feature descriptor that detects Visual coding；
Visual coding corresponding to visual coding and each sample to the binocular vision image pair calculates, and according to To calculated result generate the recognition result of the binocular vision image pair.
7. according to the method described in claim 6, it is characterized in that, the visual coding to the binocular vision image pair and Visual coding corresponding to each sample is calculated, and generates the binocular vision image pair according to obtained calculated result The step of recognition result includes:
Calculate the Euclidean distance between visual coding corresponding to the visual coding and each sample of the binocular vision image pair；
It is extracted from the Euclidean distance being calculated and obtains minimum Eustachian distance；
Visual coding corresponding to sample where obtaining the minimum Eustachian distance, and extract the subsidiary classification of the visual coding Mark, it is described to extract the recognition result that obtained classification mark is the binocular vision image pair.
8. according to the method described in claim 6, it is characterized in that, described detect obtained Local grid scale not according to described Become the step of the training data of feature descriptor and subsidiary classification mark generates the recognition result of the binocular vision image pair it Before, the method also includes:
The binocular vision image to Add User is constructed by Local grid scale invariant feature descriptor to the classification for sample Mark and visual coding.
9. according to the method described in claim 8, it is characterized in that, described pass through Local grid scale invariant feature descriptor structure Build binocular vision image to Add User includes: to the step of classification mark and visual coding for sample
With the binocular vision image that Adds User to for one group of Local grid scale invariant feature descriptor of sample extraction, and set Corresponding classification mark；
According to the sparse table for one group of Local grid scale invariant feature descriptor that the coefficient histogram of the sample is extracted Show；
Pass through the rarefaction representation of one group of Local grid scale invariant feature descriptor and one group of Local grid of the extraction Scale invariant feature descriptor obtains describing the visual coding of the sample.
10. a kind of three-dimensional face recognition system characterized by comprising
Input picture processing module, for being estimated to the binocular vision image of input to posture is carried out by three dimensional face reference model Meter, to obtain the virtual image pair of attitude parameter and three dimensional face reference model with respect to binocular vision image pair；
Image reconstruction module, for deep to the face for rebuilding the binocular vision image pair for prior information with the virtual image Spend image；
Detection module, for according to the attitude parameter detect characteristic point in the facial depth image corresponding to Local grid Scale invariant feature descriptor；
Result-generation module, for detecting obtained Local grid scale invariant feature descriptor and subsidiary classification mark according to described The training data of note generates the recognition result of the binocular vision image pair.
11. system according to claim 10, which is characterized in that the input picture processing module includes:
Image acquisition unit, for obtaining the binocular vision image pair of input；
Projecting cell, it is consistent to posture with the binocular vision image for being projected to obtain to three dimensional face reference model Virtual image pair；
Attitude estimation unit, for obtaining the input to Attitude estimation is carried out according to three dimensional face reference model and virtual image Binocular vision image pair attitude parameter.
12. system according to claim 10, which is characterized in that described image rebuilds module and includes:
Parallax arithmetic element, for obtaining face to the corresponding relationship operation between virtual image pair according to binocular vision image Parallax value；
Rough depth calculation unit, for the video camera mark by the face parallax value in conjunction with the shooting binocular vision image pair Determine parameter and obtains rough depth results；
Optimization processing unit, for obtaining the quilt of binocular vision image pair face pixel to as prior information using virtual image Circumstance of occlusion optimizes processing according to the situation that is blocked of the facial pixel, to obtain fine depth results；
Facial depth image forms unit, for forming the binocular vision by the rough depth results and fine depth results The facial depth image of image pair.
13. system according to claim 10, which is characterized in that the system also includes:
Face segmentation module, for carrying out facial segmentation to facial depth image according to shape indexing value and shape band, to obtain Several facial areas.
14. system according to claim 13, which is characterized in that the detection module includes:
Grid computing unit, several facial areas for obtaining to the face segmentation are calculated to obtain the face The three dimensional face grid for including in region；
Characteristic point detection unit, for detecting the characteristic point in the three dimensional face grid；
Descriptor computation unit, for calculating the characteristic point pair that the detection obtains according to the attitude parameter of binocular vision image pair The Local grid scale invariant feature descriptor answered.
15. system according to claim 10, which is characterized in that the result-generation module includes:
Extraction unit is encoded, for obtaining the training data of subsidiary classification mark, by the training data of the subsidiary classification mark Extraction obtains visual coding corresponding to each sample；
Input picture encodes acquiring unit, for obtaining to according to the obtained Local grid scale invariant feature descriptor that detects To the visual coding for indicating the binocular vision image pair；
As a result computing unit, for visual coding corresponding to the visual coding and each sample to the binocular vision image pair It is calculated, and generates the recognition result of the binocular vision image pair according to obtained calculated result.
16. system according to claim 15, which is characterized in that the result computing unit includes:
Apart from computation subunit, for calculating vision corresponding to the visual coding and each sample of the binocular vision image pair Euclidean distance between coding；
Distance extracts subelement, obtains minimum Eustachian distance for extracting from the Euclidean distance being calculated；
Classification mark extracts subelement, for the corresponding visual coding of sample where obtaining the minimum Eustachian distance, and extracts The subsidiary classification mark of the visual coding, it is described to extract the identification that obtained classification mark is the binocular vision image pair As a result.
17. system according to claim 15, which is characterized in that the system also includes:
Sample constructs module, for constructing the binocular vision figure to Add User by Local grid scale invariant feature descriptor As to the classification mark and visual coding for sample.
18. system according to claim 17, which is characterized in that the sample constructs module and includes:
Descriptor abstraction unit, for the binocular vision image that Adds User to for one group of Local grid scale of sample extraction not Become feature descriptor, and sets corresponding classification mark；
Sparse extraction unit, one group of Local grid Scale invariant for being extracted according to the coefficient histogram of the sample are special Levy the rarefaction representation of descriptor；
Acquiring unit is encoded, for the rarefaction representation by one group of Local grid scale invariant feature descriptor and described is mentioned The one group of Local grid scale invariant feature descriptor taken obtains describing the visual coding of the sample.
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