CN106778489A - The method for building up and equipment of face 3D characteristic identity information banks - Google Patents

The method for building up and equipment of face 3D characteristic identity information banks Download PDF

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Publication number
CN106778489A
CN106778489A CN201611032737.7A CN201611032737A CN106778489A CN 106778489 A CN106778489 A CN 106778489A CN 201611032737 A CN201611032737 A CN 201611032737A CN 106778489 A CN106778489 A CN 106778489A
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face
characteristic
information
personal
identity information
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黄源浩
肖振中
许宏淮
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Shenzhen Orbbec Co Ltd
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Shenzhen Orbbec Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G06V20/653—Three-dimensional objects by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification

Abstract

The invention provides the method for building up and equipment of a kind of face 3D characteristic identity information banks.The method is comprised the following steps：Personal face RGBD atlas is gathered, wherein, the personal identity information is known；The personal face 3D characteristic informations are obtained by the face RGBD atlas；The identity information of the individual is identified to the corresponding face 3D characteristic informations of the individual to obtain personal information, and the personal information is preserved to form face 3D characteristic identity information banks.The equipment includes the first acquisition module, first information acquisition module and Knowledge Base Module.The influence of the change of situations such as improve the accuracy of recognition of face, and the non-geometric such as attitude, expression, illumination and facial makeup of face cosmetic variation and fat or thin face are not readily susceptible in identification.

Description

The method for building up and equipment of face 3D characteristic identity information banks
Technical field
The present invention relates to the method for building up field of face 3D characteristic identity information banks, more particularly to a kind of face 3D features The method for building up and equipment in identity information storehouse.
Background technology
Information security issue has caused the extensive attention of various circles of society.The main path for ensuring information safety is exactly Identity to information user is accurately differentiated whether the authority for determining whether user's acquisition information by identification result closes Method, so as to reach the purpose that guarantee information was not leaked and ensured user's legitimate rights and interests.Therefore, reliable identification is very It is important and necessary.
Recognition of face is a kind of biological identification technology that the facial feature information based on people carries out identification, recognition of face Technology increasingly attracts attention as a kind of personal identification authentication technique more conveniently, safely.Traditional face recognition technology It is 2D recognitions of face, 2D recognitions of face do not have depth information, easily non-several by attitude, expression, illumination and facial makeup etc. The influence of what cosmetic variation, therefore, it is difficult to carry out accurate recognition of face.
The content of the invention
The present invention provides the method for building up and equipment of a kind of face 3D characteristic identity information banks, can solve the problem that prior art is deposited The problem for being difficult to accurate recognition of face.
In order to solve the above technical problems, one aspect of the present invention is：A kind of face 3D characteristic identities are provided The method for building up of information bank, the method is comprised the following steps：Personal face RGBD atlas is gathered, wherein, the personal identity Information is known；The personal face 3D characteristic informations are obtained by the face RGBD atlas；By the identity of the individual Message identification to the corresponding face 3D characteristic informations of the individual to obtain personal information, and by the personal information preserve with Form face 3D characteristic identity information banks.
Wherein, the face 3D characteristic identities information bank carries out hierarchical classification management to the identity information.
Wherein, the level includes personal attribute's level and group property level.
Wherein, it is described by the identity information of the individual be identified to the corresponding face 3D characteristic informations of the individual with Obtain personal information, and by the personal information preserve to form face 3D characteristic identity information banks the step of after, also include： Recognition of face training is carried out to the face 3D characteristic identities information bank.
Wherein, it is described the step of recognition of face is trained is carried out to the face 3D characteristic identities information bank to include：Collection is Know the face RGBD atlas of the tester of identity information；The people of the tester is obtained from the face RGBD atlas of the tester Face 3D characteristic informations；In face 3D characteristic informations and the face 3D characteristic identity information banks of the tester that will be obtained Face 3D characteristic informations are compared；If comparison result is correct, by the face RGBD atlas of the tester, corresponding described Face 3D characteristic informations and the identity information are saved in the face 3D characteristic identity information banks.
Wherein, the tester includes being preserved in the face 3D characteristic identity information banks the individual of personal information The individual for not preserving personal information in the face 3D characteristic identity information banks.
Wherein, the step of face RGBD atlas of the collection individual also includes：Gather the personal face RGB atlas； It is described also to include the step of obtain the personal face 3D characteristic informations by the face RGBD atlas：By the face RGB Atlas obtains the personal face 2D characteristic informations；It is described that the identity information of the individual is identified to the personal corresponding institute Face 3D characteristic informations are stated to obtain personal information, and the personal information is preserved to form face 3D characteristic identity information banks The step of also include：The identity information of the individual is identified to corresponding face 3D characteristic informations of the individual and described The personal information is preserved to form face 3D characteristic identity information banks by face 2D characteristic informations to obtain personal information.
Wherein, the step of obtaining the personal face 3D characteristic informations by the face RGBD atlas includes：By described The characteristic point of RGBD man face image acquiring faces；Face colour 3D grids are set up according to the characteristic point；It is color according to the face Color 3D grids measure the characteristic value of the characteristic point and calculate the annexation between the characteristic point；To the characteristic value and institute Annexation is stated to be analyzed to obtain the face 3D characteristic informations.
In order to solve the above technical problems, another technical solution used in the present invention is：One kind is provided and sets up face 3D spies The equipment for levying identity information storehouse, the equipment includes the first acquisition module, first information acquisition module and Knowledge Base Module；First adopts Collection module is used to gather the face RGBD atlas of individual, wherein, the personal identity information is known；First information acquisition module It is connected with first acquisition module, for obtaining the personal face 3D characteristic informations by the face RGBD atlas；Letter Breath library module includes memory module, and the memory module connects with first acquisition module and the first information acquisition module Connect, for the identity information of the individual to be identified into the corresponding face 3D characteristic informations of the individual to obtain personal letter Breath, and the personal information is preserved, to form face 3D characteristic identity information banks.
Wherein, described information library module also includes management module, and the management module is connected with the memory module, described Management module is used to carry out the identity information hierarchical classification management.
Wherein, the level includes personal attribute's level and group property level.
Wherein, the equipment for setting up face 3D characteristic identity information banks also include training module, the training module with First acquisition module, first information acquisition module and described information library module connection, for the face 3D characteristic identities Information bank carries out recognition of face training.
Wherein, the training module includes control module and comparing module；The control module is used to control described first The face RGBD atlas of the tester of acquisition module collection known identities information, controls the first information acquisition module from described The face 3D characteristic informations of the tester are obtained in the face RGBD atlas of tester；The comparing module and the control module Connection, for by the face 3D characteristic informations of the tester of the acquisition and the face 3D characteristic identity information banks Face 3D characteristic informations are compared；The control module is also connected with the memory module, and the control module is additionally operable to When comparison result is correct, the memory module is controlled to believe the face RGBD atlas of the tester, corresponding face 3D features Breath and the identity information are saved in the face 3D characteristic identity information banks.
Wherein, the tester includes being preserved in the face 3D characteristic identity information banks the individual of personal information The individual of personal information is preserved not in the face 3D characteristic identity information banks.
Wherein, the equipment also includes the second acquisition module and the second data obtaining module；Second acquisition module is used In the collection personal face RGB atlas；Second data obtaining module is connected with second acquisition module, for by described Face RGB atlas obtains the personal face 2D characteristic informations；The memory module also with second acquisition module and Two data obtaining modules are connected, for the personal face 2D characteristic informations to be believed with the face 3D features for identifying identity information Breath is saved in the face 3D characteristic identity information banks.
Wherein, state first information acquisition module further include the 3rd acquisition module, grid set up module, computing module and Analysis module；3rd acquisition module is connected with first acquisition module, for by the RGB man face image acquirings face Characteristic point；Grid is set up module and is connected with the 3rd acquisition module, for setting up face colour 3D nets according to the characteristic point Lattice；Computing module is set up module and is connected with the grid, for measuring the characteristic point according to face colour 3D grids Characteristic value simultaneously calculates the annexation between the characteristic point；Analysis module is connected with the computing module, described for analyzing Characteristic value and the annexation are obtaining the face 3D characteristic informations.
The beneficial effects of the invention are as follows：The situation of prior art is different from, the present invention obtains people by face RGBD atlas Face 3D characteristic informations, then personally identifiable information is identified to the personal corresponding face 3D characteristic informations and together preservation and is formed Face 3D characteristic identities information bank for recognition of face, because face 3D characteristic informations include colouring information and depth letter Breath, can be created as face skeleton, therefore, the face information in the 3D information picture libraries is more comprehensive, is carrying out recognition of face When, can recognize more accurate, also, because the face information in the D information picture libraries is 3D information, therefore face appearance The change of situations such as non-geometric such as state, expression, illumination and facial makeup cosmetic variation and fat or thin face will not be to face Identification is influenceed.
Brief description of the drawings
Technical scheme in order to illustrate more clearly the embodiments of the present invention, below will be to that will make needed for embodiment description Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings Accompanying drawing.
Fig. 1 is that a kind of flow of the method for building up of face 3D characteristic identity information banks provided in an embodiment of the present invention is illustrated Figure；
Fig. 2 is a kind of schematic diagram of face 3D characteristic identity information banks provided in an embodiment of the present invention；
Fig. 3 is a kind of hierarchical classification pipe of identity information of face 3D characteristic identity information banks provided in an embodiment of the present invention The schematic diagram of reason；
Fig. 4 is a kind of face 3D features letter of the single people of face 3D characteristic identity information banks provided in an embodiment of the present invention The hierarchical schematic diagram for sorting out management of breath；
Fig. 5 is that a kind of flow of the method for building up of face 3D characteristic identity information banks that another embodiment of the present invention is provided is shown It is intended to；
Fig. 6 is the schematic flow sheet of step S24 in Fig. 5；
Fig. 7 is signal when a kind of face 3D characteristic identity information banks provided in an embodiment of the present invention are identified training Figure；
Fig. 8 is signal when another face 3D characteristic identity information banks provided in an embodiment of the present invention are identified training Figure；
Fig. 9 is that a kind of flow of the method for building up of face 3D characteristic identity information banks that further embodiment of this invention is provided is shown It is intended to；
Figure 10 is a kind of structural representation of equipment for setting up face 3D characteristic identity information banks provided in an embodiment of the present invention Figure；
Figure 11 is that the structure of the equipment that another kind provided in an embodiment of the present invention sets up face 3D characteristic identity information banks is shown It is intended to；
Figure 12 is that the structure of another equipment for setting up face 3D characteristic identity information banks provided in an embodiment of the present invention is shown It is intended to；
Figure 13 is a kind of structure of entity apparatus for setting up face 3D characteristic identity information banks provided in an embodiment of the present invention Schematic diagram.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on Embodiment in the present invention, it is all other that those of ordinary skill in the art are obtained under the premise of creative work is not made Embodiment, belongs to the scope of protection of the invention.
Fig. 1 is referred to, Fig. 1 is a kind of method for building up of face 3D characteristic identity information banks provided in an embodiment of the present invention Schematic flow sheet.
The method for building up of the face 3D characteristic identity information banks of the present embodiment is comprised the following steps：
S11：Personal face RGBD atlas is gathered, wherein, the personal identity information is known.
In step S11, the collection of face RGBD atlas can be carried out by Kinect sensor, and RGBD facial images include The colouring information (RGB) and depth information (Depth) of face, increased depth information, the face compared to traditional 2D images RGBD atlas includes many personal face RGBD atlas, and face multiple angle can be included in the face RGBD atlas of same person Multiple RGBD images of degree.
Personally identifiable information can include personal name, sex, age, nationality, native place, contact method, work unit, The personal basic information of department, unit address etc..
S12：The personal face 3D characteristic informations are obtained by face RGBD atlas.
Specifically, step S12 includes：
S121：By the characteristic point of RGBD man face image acquiring faces.In the step, usually carried out by gathering face unit The collection of characteristic point, wherein, face element includes：One of eyebrow, eyes, nose, face, cheek and lower Palestine and China more It is individual.Characteristic point can be obtained by the face such as the eyes of handmarking's face, nose, cheek, lower jaw and its edge etc..
For example, the localization method of face key feature points：Choose face 9 characteristic points, these characteristic points point Cloth has angle invariability, respectively 2 eyeball central points, 4 canthus points, the midpoint in two nostrils and 2 corners of the mouth points.In this base Other characteristic point positions of each organ characteristic of the face relevant with identification and extension can be readily available on plinth, be used for into one The recognizer of step.
When face characteristic extraction is carried out, due to local marginal information effectively cannot be organized, traditional side Edge detective operators can not reliably extract the feature (profile of eyes or mouth) of face, but from human visual system, fully The positioning of face key feature points is carried out using the feature of edge and angle point, then can greatly improve its reliability.
Wherein selection Susan (Smallest Univalue Segment Assimilating Nucleus) operator is used for Extract the edge and Corner Feature of regional area.According to the characteristic of Susan operators, it both can be used to detect edge, can be used for again Extract angle point.Therefore compared with the edge detection operator such as Sobel, Canny, Susan operators are more suitable for carrying out face eye The extraction of the feature such as portion and face, is especially automatically positioned to canthus point and corners of the mouth point.
The following is the introduction of Susan operators：
With a circular shuttering traversing graph picture, if the gray value of other any pixels and template center's pixel (core) in template The difference of gray value be less than certain threshold value, being considered as the point and core has the gray value of identical (or close), meets such condition Pixel composition region be referred to as the similar area of core value (Univalue Segment Assimilating Nucleus, USAN). It is the base of SUSAN criterions that each pixel in image is associated with the regional area with close gray value.
It is to scan whole image with circular shuttering during specific detection, compares the ash of each pixel and center pixel in template Angle value, and given threshold value differentiates whether the pixel belongs to USAN regions, such as following formula：
In formula, c (r, r0) to belong to the discriminant function of the pixel in USAN regions, I (r in template0) it is template center's pixel The gray value of (core), I (r) is the gray value of other any pixels in template, and t is gray scale difference thresholding.It influences to detect angle point Number.T reduces, and more fine changes in image is obtained, so as to provide relatively large number of amount detection.Thresholding t must root The factors such as contrast and noise according to image determine.Then the USAN area sizes of certain point can be expressed from the next in image：
Wherein, g is geometric threshold, and the angle point shape that influence is detected, g is smaller, and the angle point for detecting is more sharp.(1)t,g Determination thresholding g determine output angle point USAN regions maximum, as long as pixel that is, in image has the USAN smaller than g Region, the point is just judged as angle point.The size of g is not only determined can extract the number of angle point from image, and such as preceding institute State, it also determines the acuity of detected angle point.So once it is determined that the quality (acuity) of required angle point, G can just take a changeless value.Thresholding t represents the minimum contrast that can be detected angle point, is also the noise that can ignore Maximum tolerance.It essentially dictates the feature quantity that can be extracted, and t is smaller, and spy can be extracted from the lower image of contrast Levy, and the feature extracted is also more.Therefore for different contrast and the image of noise situations, different t values should be taken. SUSAN operators have the advantages that one it is prominent, be exactly that, to local insensitive for noise, anti-noise ability is strong.This is because it is not relied on The result of early stage image segmentation, and avoid gradient calculation, in addition, USAN regions are that had with template center pixel by template The pixel of similar gray-value adds up and obtains, and this is actually an integral process, has good inhibiting effect for Gaussian noise.
The last stage of SUSAN two dimensional characters detection, exactly finds the local maximum of initial angle point response, also It is non-maximum suppression treatment, to obtain final corner location.Non- maximum suppression is exactly in subrange, such as its name suggests The initial communication of fruit center pixel is the maximum in this region, then retain its value, is otherwise deleted, and so can be obtained by part The maximum in region.
(1) eyeball and canthus are automatically positioned.During being automatically positioned of eyeball and canthus, first using normalization mould The method Primary Location face of plate matching.The general area of face is determined in whole facial image.Common human eye positioning Algorithm is positioned according to the valley point property of eyes, and herein then using by the symmetrical of the search of valley point and direction projection and eyeball Property the method that is combined, the degree of accuracy of eyes positioning can be improved using the correlation between two.To the upper left of face area Gradient map integral projection is carried out with upper right portion, and histogram to integral projection is normalized, first according to floor projection Valley point determine approximate location of the eyes in y directions, then allow x to change in the larger context, find the paddy in this region Point, will detect o'clock as the eyeball central point of two.
On the basis of two eyeball positions are obtained, ocular is processed, first using self-adaption binaryzation method Determine threshold value, obtain the automatic binary image of ocular, then in conjunction with Susan operators, examined using edge and angle point The algorithm of survey is accurately positioned interior tail of the eye point in ocular.
By the ocular edge image that above-mentioned algorithm is obtained, angle is carried out to the boundary curve in image on this basis Point is extracted can obtain accurate two intraoculars external eyes corner location.
(2) nose characteristic of field point is automatically positioned.The key feature points of face nasal area are defined as two nostrils center The midpoint of line, i.e. muffle central point.The position of face muffle central point is relatively stablized, and for facial image normalizing The effect of datum mark is may also function as when changing pretreatment.
Based on two eyeball positions for finding, two positions in nostril are determined using the method for area grayscale integral projection Put.
The strip region of two eye pupil hole widths is intercepted first, Y-direction integral projection is carried out, and then drop shadow curve is divided Analysis.It can be seen that, along drop shadow curve from the Y-coordinate height search downwards of eyeball position, find first position of valley point (logical The appropriate peak valley Δ value of adjustment selection is crossed, ignoring centre may be because the burr that the factors such as face's scar or glasses are produced influences), Using this valley point as naris position Y-coordinate datum mark；It is width that second step is chosen with two eyeball X-coordinate, in the Y-coordinate of nostril Lower δ pixels (for example, choosing δ=[nostril Y-coordinate-eyeball Y-coordinate] × 0.06) are that the region of height carries out X-direction integration throwing Shadow, is then analyzed to drop shadow curve, and used as central point, both sides are carried out the X-coordinate using two eye pupil hole midpoints to the left and right respectively Search, the X-coordinate of the first valley point as central point in left and right nostril for finding.Two midpoints in nostril are calculated as in muffle Point, obtains the accurate location of muffle central point, and delimits nasal area.
(3) corners of the mouth is automatically positioned.Because the difference of human face expression may cause the large variation of face shape, and Face region is easier to be disturbed by factors such as beards, thus mouth feature point extract accuracy for identification influence compared with Greatly.Influence relative variability smaller because the position of corners of the mouth point is expressed one's feelings etc., the position of angle point is more accurate, so taking mouth region Important Characteristic Points be two positioning methods of corners of the mouth point.
On the basis of eyes region and nose characteristic of field point is determined, first with the method for area grayscale integral projection It is determined that from first valley point of the following Y-coordinate drop shadow curve in nostril (similarly, it is necessary to eliminated by appropriate peak valley Δ value due to The burr influence that the factors such as beard, mole trace are produced) as the Y-coordinate position of face；Then face region is selected, to area image Processed using Susan operators, after obtaining mouth edge figure；Angle point grid is finally carried out, two corners of the mouths just can be obtained Exact position.
S122：Face colour 3D grids are set up according to characteristic point.
S123：Characteristic value according to face colour 3D grids measures characteristic point simultaneously calculates the annexation between characteristic point. The characteristic point that can be directed to face characteristic by colouring information is measured to associated eigenvalue, and this feature value exists for face characteristic In 2D planes including to one or more the measurement in position, distance, shape, size, angle, radian and curvature, Additionally, also including the measurement to color, brightness, texture etc..Eyes are for example obtained to around extending according to iris central pixel point Whole location of pixels, the shape of eyes, the inclination radian at canthus, eye color etc..Color combining information and depth are believed Breath, then can calculate the annexation between characteristic point, and the annexation can be the topological connection relation between characteristic point With space geometry distance, or can also be the Dynamic link library relation information of various combinations of characteristic point etc..According to face colour 3D The measurement of grid and calculating can obtain the characteristic point on the plane information of each element including face in itself and each element Spatial relation local message, and the spatial relation between each element Global Information.Local message and Global Information is respectively from the local information and structural relation lain in reflection on the whole on face RGBD figures.
S124：Characteristic value and annexation are analyzed to obtain face 3D characteristic informations.By to characteristic value and company The analysis of relation is connect, thus the face shape information of solid can be obtained, so as to obtain face 3D characteristic informations.
For example, in step S124, the topology between characteristic value, characteristic point can be connected using finite element method Connect relation and space geometry distance is analyzed to obtain the 3d space distribution characteristics information of characteristic point.
Specifically, can be to face colour 3D grid march facial disfigurements using finite element analysis.Finite element analysis (FEA, Finite Element Analysis) it is that actual physical system (geometry and load working condition) is entered using the method for mathematical approach Row simulation.Also using element that is simple and interacting, i.e. unit, it is possible to gone to approach infinitely with the unknown quantity of limited quantity The real system of unknown quantity.
For example, after carrying out strain energy of distortion analysis to face colour 3D grids each line units, the list of line unit can be set up First stiffness equations.Then introduce constraint element, such as point, line, cut arrow, method arrow constraint element type.Because curve and surface will expire Foot required to its shape, position, size and with the continuity of adjacent curved surface etc. when checking design, these be all by constrain come Realize.The present embodiment processes these and constrains by penalty function method, the final stiffness matrix and equivalent load for obtaining constraint element Array.
Expand the data structure of Deformable curve and surface so that the data structure of Deformable curve and surface is both comprising such as exponent number, control The geometric parameter part on summit processed and knot vector etc., also including showing some parameters of physical characteristic and external applied load.So that Obtaining Deformable curve and surface can integrally represent that some complex bodies show, enormously simplify the geometrical model of face.And And, physical parameter and constrained parameters in data structure uniquely determine the configuration geometric parameter of face,
Finite element solving Deformable curve and surface is used by programming, for different constraint elements, setting unit enters Mouth program, can calculate the element stiffness matrix and unit load array of any constraint.According to the right of global stiffness matrix Title property, banding and openness, using variable bandwidth one-dimension array storage method to global stiffness matrix computations.During assembling, not only By line unit or face element stiffness matrix, constraint element stiffness matrix is also added to global stiffness square by " sitting in the right seat " mode In battle array, while constraint element equivalent load array is added in General load array, line is finally solved using Gaussian reduction Property Algebraic Equation set.
For example, the formative method of face curve and surface can be described as with Mathematical Modeling：
Required deformation curve
U ∈ Ω=[0,1], or curved surface
(u, v) ∈ Ω=[0,1] × [0,1] is the solution of following extreme-value problem
Wherein,It is the energy functional of curve and surface, it reflects the deformation characteristicses of curve and surface to a certain extent, Assign curve and surface physical characteristic.F1, f2, f3, f4 are the functions on variable in (),It is parameter definition The border in domain, Γ ' is the curve in Surface Parameters domain, (μ0, v0) be certain parameter value in parameter field, condition (1) be interpolating on sides about Beam, condition (2) is boundary continuity constraint, and condition (3) is the constraint of characteristic curve in curved surface, and condition (4) is in curve and surface Point constraint.In the application, energy functionalTake into following form：
Curve：
Curved surface：
Wherein, α, β, γ represent respectively curve stretching, object for appreciation go, coefficient of torsion, α ij and β ij be respectively curved surface (μ, v) Place removes coefficient very much partially along μ, the drawing in v directions with object for appreciation.
It is both full as can be seen that Deformable curve and surface modeling method is same, process all kinds of constraints in phase from Mathematical Modeling Foot Partial controll, in turn ensure that overall wide suitable.Using variation principle, solving above-mentioned extreme-value problem can be converted into solution such as lower section Journey：
Here δ represents first variation.Formula (5) is a differential equation, because the equation is more complicated, it is difficult to obtain essence Really analysis is tied, therefore using numerical value liberation.For example, using finite element method.
Finite element method is regarded as first selecting suitable Interpolation as needed, then solves combination parameter, therefore institute The solution for obtaining is not only conitnuous forms, and the grid of pre-treatment generation is also for finite element analysis is laid a good foundation.
Similarity measurement between cognitive phase, unknown facial image and known face template is given by：
In formula：CiXjThe feature of face, i in the feature and face database of face respectively to be identified1,i2,j1,j2,k1,k2For 3D grid vertex features.Section 1 in formula is that machine selects corresponding local feature X in two vector fieldsjAnd CiSimilarity degree, Binomial then be calculate local location relation and matching order, it can be seen that, when best match i.e. least energy function Match somebody with somebody.
Curved surface deformation has been carried out to face colour 3D grids by above-mentioned finite element method, make face colour 3D grids each Point is constantly close to the characteristic point of real human face, so as to obtain the face shape information of solid, and then obtains human face characteristic point 3d space distribution characteristics information.
Further, it is also possible to using wavelet transformation texture analysis method to the Dynamic link library relation between characteristic value and characteristic point It is analyzed, to obtain the 3d space distribution characteristics information of characteristic point.
Specifically, Dynamic link library relation is the Dynamic link library relation of various features point combination.Wavelet transformation be the time and The local conversion of frequency, it has the feature of multiresolution analysis, and all has sign signal local feature in time-domain and frequency-domain Ability.The present embodiment is by the way that wavelet transformation texture analysis is by the extraction to textural characteristics, classification and analytical procedure and combines Face characteristic value and Dynamic link library relation information, specifically include colouring information and depth information, final to obtain three-dimensional face Shape information, finally analysis extracts the lower people's shape of face with consistency of face slight expression change from face shape information again Shape information, carries out encoding face shape model parameter, and the model parameter can be used as the geometric properties of face, so as to obtain face The 3d space distribution characteristics information of characteristic point.
Face 2D features letter has been also compatible with the acquisition methods of the face 3D characteristic informations that some other embodiment is provided The acquisition of breath, the acquisition methods of face 2D characteristic informations can be the conventional various methods in this area.In those embodiments, While obtaining face 3D characteristic informations, face 2D characteristic informations can also be obtained, to carry out the knowledge of 3D and 2D to face simultaneously Not, so as to further improve the accuracy of recognition of face.
For example, the basis of 3 D wavelet transformation is as follows：
Wherein,
AJ1For function f (x, y, z) arrives SPACE V3 J1Projection operator,
QnIt is Hx,Hy,Hz Gx,Gy,GzCombination；
Order matrix H=(HM, k), G=(GM, k), wherein, Hx,Hy,HzPoint Not Biao Shi H be applied on three dimensional signal x, y, z directions, Gx,Gy,GzRepresent that G is applied on three dimensional signal x, y, z directions respectively.
Cognitive phase, by unknown facial image wavelet transformation after, take its low frequency low resolution subgraph and be mapped to face space, Characteristic coefficient will be obtained, it is possible to use between Euclidean distance characteristic coefficient more to be sorted and everyone characteristic coefficient away from From with reference to PCA algorithms, according to formula：
In formula, K is the people most matched with unknown face, and N is database number, and Y is mapped to by eigenface for unknown face The m dimensional vectors obtained on the subspace of formation, YkFor known face is mapped on the subspace formed by eigenface in database The m dimensional vectors for obtaining.
It is to be appreciated that in another embodiment, it is the 3D recognitions of face based on 2-d wavelet feature that can also use Method is identified, it is necessary first to carry out 2-d wavelet feature extraction, and 2-d wavelet basic function g (x, y) is defined as
gmn(x, y)=a-mnG (x ', y '), a ＞ 1, m, n ∈ Z
Wherein, σ is the size of Gauss window, and a filter function for self similarity can be by function gmn(x, y) to g (x, Y) suitably expansion and rotation is carried out to obtain.Based on superior function, the wavelet character to image I (x, y) can be defined as
Facial image 2-d wavelet extraction algorithm realizes that step is as follows：
(1) obtain the small echo on face by wavelet analysis to characterize, convert the individual features in original image I (x, y) It is wavelet-based attribute vector F (F ∈ Rm)。
(2) Fractional power polynomial models (FPP) model k (x, y)=(xy) is usedd(0 ＜ d ＜ 1) makes m tie up wavelet character space RmProject to n-dimensional space R highernIn.
(3) based on the linear judgment analysis algorithm (KFDA) of core, in RnMatrix S between class is set up in spacebWith matrix S in classw。
Calculate SwNormal orthogonal characteristic vector α1, α2..., αn。
(4) extract facial image and significantly differentiate characteristic vector.Another P1=(α1, α2..., αq), wherein, α1, α2..., αqIt is SwCorresponding q characteristic value is positive characteristic vector, q=rank (Sw).CalculateIt is maximum corresponding to L The characteristic vector β of characteristic value1, β2..., βL, (L≤c-1), wherein,C is face The quantity of classification.Significantly differentiate characteristic vector, fregular=BTP1 TY wherein, y ∈ Rn；B=(β1, β2..., βl)。
(5) the inapparent differentiation characteristic vector of facial image is extracted.CalculateCorresponding to a feature for eigenvalue of maximum to Amount γ1, γ2..., γL, (L≤c-1).Make P2=(αq+1, αq+2..., αm), then inapparent differentiation characteristic vector
It is as follows the step of including in the 3D recognition of face stages：
(1) front face is detected, crucial face characteristic in one front face of positioning and a facial image The contour feature point of point, such as face, left eye and right eye, mouth and nose etc..
(2) three-dimensional people is rebuild by the conventional 3D face databases of the two-dimensional Gabor characteristic vector of said extracted and Face model.In order to rebuild a three-dimensional face model, ORL (Olivetti Research Laboratory) single face three is used Dimension face database, including 100 facial images for detecting.Each faceform has nearly 70000 tops in database Point.Determine a Feature Conversion matrix P, in original three-dimensional face identification method, the matrix is typically by subspace analysis side The subspace analysis projection matrix that method is obtained, the characteristic vector of preceding m eigenvalue of maximum is corresponded to by the covariance matrix of sample Composition.The small echo that will be extracted differentiates that characteristic vector corresponds to the m characteristic vector of eigenvalue of maximum, constitutes main Feature Conversion square Battle array P ', this feature transition matrix has stronger robustness than original eigenmatrix P to factors such as illumination, attitude and expressions, i.e., The feature of representative is more accurate and stable.
(3) newly-generated faceform is processed using template matches and linear discriminant analysis (FLDA) method, is carried Difference and class inherited in the class of modulus type, further optimize last recognition result.
S13：The personal identity information is identified to the personal corresponding face 3D characteristic informations to obtain personal information, And preserve personal information to form face 3D characteristic identity information banks.
Wherein, the personal information of the present embodiment contains the identity information and face 3D characteristic informations of individual, and identity is believed Breath is identified to after face 3D characteristic informations, when the later stage carries out recognition of face, recognizes face 3D characteristic information identical people, i.e., The corresponding identity information of face 3D characteristic informations can be obtained.In certain embodiments, personal information includes identity information and people Face 3D characteristic informations and corresponding RGBD atlas, as shown in Fig. 2 Fig. 2 is a kind of face 3D features provided in an embodiment of the present invention The schematic diagram in identity information storehouse.
It is different from prior art, the present invention obtains face 3D characteristic informations by face RGBD atlas, then by personal identification Message identification to the personal corresponding face 3D characteristic informations and preserve together and formed face 3D characteristic identities information bank with In recognition of face, because face 3D characteristic informations include colouring information and depth information, face skeleton can be created as, because This, the face information in the 3D information picture libraries is more comprehensive, when recognition of face is carried out, can recognize more accurate, and And, because the face information in the 3D information picture libraries is 3D information, therefore the attitude of face, expression, illumination and facial makeup The change of situations such as etc. non-geometric cosmetic variation and fat or thin face will not influence on recognition of face.
In one embodiment, face 3D characteristic identities information bank carries out hierarchical classification management to identity information, wherein, Level includes personal attribute's level and group property level.
Fig. 3 is referred to, Fig. 3 is a kind of identity information layer of face 3D characteristic identity information banks provided in an embodiment of the present invention Levelization sorts out the schematic diagram of management.
For example, personal attribute's level includes the letter such as personal name, sex, age, identification card number with uniqueness The set of breath.Colony's layers such as the group property employee of same company then including nonuniqueness, the staff of same office building Level information.
As shown in figure 3, individual A and individual B goes to work in same company " ", personal E goes to work in company " three ", company " " With company " three " in same office building " 1 ".Personal C and individual D goes to work in same company " two ", and company " two " is in office building In " 2 ".
Fig. 4 is referred to, Fig. 4 is a kind of people of the single people of face 3D characteristic identity information banks provided in an embodiment of the present invention The hierarchical schematic diagram for sorting out management of face 3D characteristic informations.
In some other embodiment, face 3D characteristic informations can also carry out hierarchical classification management, for example, arriving in detail The 3D characteristic informations of the face such as eyes, nose, face are used as a level；The 3D characteristic informations of cheek, chin etc. are used as one Level, the 3D characteristic informations of shape of face or whole head dummy etc. are used as a level.
It is understood that the level of above-mentioned identity information is divided and the level of face 3D characteristic informations divides only this reality A kind of level dividing mode in example is applied, other embodiments there can also be other dividing modes.
Hierarchical classification management can make recognition of face more convenient, fast.
For example, individual A when using payment system, it is necessary to be accurately confirmed whether it is personal A, now, , it is necessary to obtain the face 3D characteristic informations of comprehensive individual A when carrying out recognition of face, for example, it is desired to obtain face shape of face, The face 3D characteristic informations of the face such as cheek, chin and eyes, nose, face, can accurately determine the individual Genus Homo of the user The information of property, so that it is personal A accurately to identify whether.
And for example, a gate control system for office building " 1 ", the gate control system only needs to judge whether identified person is that this writes The group property of the inner staff in building " 1 ", and the personal attribute at the name of the identified person, age etc. need not be judged.Therefore, When by office building gate control system, the face RGBD images that gate control system gets may be not fine enough, for example, only obtaining The RGBD images of the face side of personal A are taken, or when personal B bows by gate control system, has only obtained personal B faces The RGBD images of the first half, or be very fast personal E paces, thus only obtain the fuzzyyer face of the face of personal E RGBD images ... gate control system can now call the identity information of group property level in face 3D characteristic identity information banks Identification, for example, gate control system obtains the face side shape of face of personal A and the 3D of the bridge of the nose by the RGBD images of face side Characteristic information, though cannot judge that whom the personal A is, it can be seen from the information preserved in face 3D characteristic identity information banks, The work people of the 3D characteristic informations with identical side shape of face and the bridge of the nose is there are in staff in the office building " 1 " Member, hence allows to personal A into office building " 1 ".Or, gate control system is obtained by the RGBD images of the first half of the face of individual B The 3D characteristic informations of the top half face of personal B are taken, it can be seen from the information preserved in face 3D characteristic identity information banks, this is write The staff of the 3D characteristic informations with identical top half face is there are in staff in word building " 1 ", though it is not true The fixed staff is exactly personal B, but personal B can be allowed to enter office building " 1 ".Or, gate control system is by individual E's The fuzzyyer RGBD images of face obtain the 3D characteristic informations of the shape of face of the face of individual E, according to face 3D characteristic identity information The information preserved in storehouse is it is recognized that while cannot confirm that whom the personal E is, but can determine the staff in the office building " 1 " In there are the staff of the 3D characteristic informations with identical shape of face, hence allow to the personal E identified persons and enter write Building " 1 ".If gate control system obtains the fuzzyyer face RGBD images of face of personal C, obtained by the RGBD images The 3D characteristic informations of the shape of face of personal C, it can be seen from the information preserved in face 3D characteristic identity information banks, in office building " 1 " The not staff similar to the 3D characteristic informations of the shape of face, thus gate control system wouldn't allow the identified person to enter to write Word building " 1 ", and need further to obtain the RGBD images of the face, sentenced with obtaining more face 3D characteristic informations It is disconnected.
Therefore, after face 3D characteristic identities information bank carries out hierarchical classification management, can be adjusted according to the need for difference Without the identity information of level, and excessive work need not be done, such as it is specific without recognizing in the gate control system of office building Personal attribute identity information, can economize on resources, it is time-consuming so that recognition of face is more convenient, fast.
Fig. 5 is referred to, Fig. 5 is a kind of foundation side of face 3D characteristic identity information banks that another embodiment of the present invention is provided The schematic flow sheet of method.
S21：Personal face RGBD atlas is gathered, wherein, the personal identity information is known.
S22：The personal face 3D characteristic informations are obtained by face RGBD atlas.
S23：The personal identity information is identified to the personal corresponding face 3D characteristic informations to obtain personal information, And preserve personal information to form face 3D characteristic identity information banks.
S24：Recognition of face training is carried out to face 3D characteristic identities information bank.
The present embodiment is that increased step S24 with the difference of above-described embodiment, and face 3D characteristic identity information banks are entered Pedestrian's face recognition training can improve the abundant degree of information resources in face 3D characteristic identity information banks, so as to improve face knowledge Other accuracy.
As shown in fig. 6, Fig. 6 is the schematic flow sheet of step S24 in Fig. 5.Specifically, step S24 includes：
S241：Gather the face RGBD atlas of the tester of known identities information.
Tester includes preserving the personal of personal information in face 3D characteristic identity information banks and not in face 3D The individual that personal information is preserved in characteristic identity information bank.
Wherein, tester identity information, it is known that can refer to tester identity information section it is known or all Know, for example, it may be the identity information of personal attribute's level of tester is all or part of, it is known that and group property level Identity information is unknown, or identity information section of group property level of tester or all, it is known that and individual Genus Homo The identity information of property level is unknown, or the identity information and group property level that can also be personal attribute's level of tester Identity it is new all known.
S242：The face 3D characteristic informations of the tester are obtained from the face RGBD atlas of tester.
The method phase of the step of step S242 obtains the method for the face 3D characteristic informations of tester with above-described embodiment S12 Together, will not be repeated here.
S243：Face 3D in the face 3D characteristic informations and face 3D characteristic identity information banks of the tester that will be obtained is special Reference breath is compared.
For example, the face 3D characteristic information ratios in the face 3D characteristic informations of tester and face 3D characteristic identity information banks It is right, draw the face 3D features letter of the personal X in the face 3D characteristic informations and face 3D characteristic identity information banks of the tester The similarity of breath reaches predetermined threshold value.Then can determine whether to preserve personal in the tester i.e. face 3D characteristic identity information banks X, if not reaching predetermined threshold value, judges the people that the tester is not preserved in face 3D characteristic identity information banks.
For example, when tester be face 3D characteristic identity information banks in preserved personal information it is personal when, such as Fruit comparison result is the tester corresponding with the personal information preserved in face 3D characteristic identity information banks, then show to compare knot Fruit is correct, into step S244；If comparison result represents the personal information of the tester in face 3D characteristic identity information banks In do not preserve, then show comparison result mistake, it is therefore desirable to the tester in face 3D characteristic identity information banks The information of preservation carries out correcting and further enriching the personal information.
For example, when test is artificial preserved not in face 3D characteristic identity information banks personal information it is personal when, if than It is that the information of the tester does not exist in face 3D characteristic identity information banks to result, then shows that comparison result is correct, enters Enter step S244, to collect the personal information of the tester；If comparison result is the artificial face 3D characteristic identities of the test Someone in information bank, then show comparison result mistake, it is necessary to the someone's in face 3D characteristic identity information banks Information is corrected, and further enriches the personal information, meanwhile, the personal information of tester is also saved in face 3D special In levying identity information storehouse, to improve the abundant degree of face 3D characteristic identity information base information resources.
S244：The face RGBD atlas of tester, corresponding face 3D characteristic informations and identity information are saved in face In 3D characteristic identity information banks.
The RGBD atlas of the tester that will be collected in recognition training, face 3D characteristic informations and identity information are saved in people In face 3D characteristic identity information banks in corresponding personal information so that the information resources in face 3D characteristic identity information banks are more It is abundant, so that accuracy when recognition of face is carried out beneficial to the later stage.
For example, in the face 3D characteristic identity information banks tentatively set up, by artificial method by 500 people Part identity message identification in 500 people corresponding face RGBD figures and face 3D characteristic informations, be stored in face In 3D characteristic identity information banks.During recognition training, the RGBD figures for gathering the even more many people of 5000 or 50000 people are carried out Recognition training, at least part of personally identifiable information is also identified on the RGBD figures of tester, and the individual of a large amount of testers is preserved again People's information, if tester is that face 3D characteristic identity information banks are original, can continue to supplement personal RGBD figures, the people Face 3D characteristic informations and identity information.
As shown in fig. 7, Fig. 7 is a kind of face 3D characteristic identity information banks provided in an embodiment of the present invention being identified training When schematic diagram.Face 3D characteristic identity information banks Central Plains preserve personal G RGBD atlas, by the RGBD atlas obtain Face 3D characteristic informations and the identity information on personal attribute, when training is identified, the people of the personal G of collection Face RGBD atlas may includes the RGBD images of more perspective, and more face 3D features can be obtained from the RGBD images Information, also, during recognition training, in face RGBD figure centralised identities building where its work unit, work unit of personal G Deng group property identity information, therefore, during recognition training, in recognizing the tester and face 3D characteristic identity information banks The personal G's that original is preserved is same person, therefore, face RGBD atlas, the face 3D features of individual G are gathered during recognition training The identity information of the group property of information and mark is saved in the personal information of individual G in face 3D characteristic identity information banks Place so that the personal information of individual G is more enriched in face 3D characteristic identity information banks.
Again for example, not preserving any personal information of personal H in face 3D characteristic identity information banks, instruction is identified When white silk, the face RGBD atlas of personal H is collected, and face 3D characteristic informations is obtained from face RGBD atlas, Also, the RGBD figure centralised identities at least part of identity information of personal H, as shown in figure 8, Fig. 8 is the embodiment of the present invention carrying Another face 3D characteristic identity information banks for supplying are identified schematic diagram during training.During recognition training, comparison result For personal H is not preserved in face 3D characteristic identity information banks, so as to the personal information of personal H, including face RGBD be schemed Collection, face 3D characteristic informations and identity information are maintained in face 3D characteristic identity information banks, so as in face 3D feature bodies The archives of personal H are set up in part information bank.
Fig. 9 is referred to, Fig. 9 is a kind of foundation side of face 3D characteristic identity information banks that further embodiment of this invention is provided The schematic flow sheet of method.
S31：Personal face RGBD atlas and RGB atlas is gathered, wherein, the personal identity information is known.
S32：The personal face 3D characteristic informations are obtained by face RGBD atlas, being obtained by face RGB atlas should Personal face 2D characteristic informations.
S33：The personal identity information is identified to the personal corresponding face 3D characteristic informations and face 2D features letter Personal information is preserved to form face 3D characteristic identity information banks by breath to obtain personal information.
The present embodiment is also to gather face RGB while face RGBD atlas is gathered with the difference of above-described embodiment Atlas, so as to face skeleton can not only be set up, must obtain face texture information, Skin Color Information etc., with reference to 3D recognitions of face and 2D face recognition technologies, using weighted average, such as A*g (2D)+B*h (3D)=C*f (RGBD) carries out recognition of face, so as to reach To more accurate recognition effect.
Specifically, the present embodiment can apply to situations below, for example, it is desired to obtain the personal attribute of a certain identified person Identity information and group property identity information when, if obtain face 3D characteristic informations only can recognize that the identified person Group property identity information, and None- identified now needs to combine people to the identity information of the personal attribute of identified person Face 2D characteristic informations, by face 2D characteristic informations and face 3D characteristic informations to face skeleton, face complexion and texture information Etc. being identified, to draw the identity information of the personal attribute of identified person.
Figure 10 is referred to, Figure 10 is a kind of equipment for setting up face 3D characteristic identity information banks provided in an embodiment of the present invention Structural representation.
The equipment for setting up face 3D characteristic identity information banks of the present embodiment is obtained including the first acquisition module 10, first information Modulus block 11 and Knowledge Base Module 12.
Specifically, the first acquisition module 10 is used to gather the face RGBD atlas of individual, wherein, the personal identity information For known.
First information acquisition module 11 is connected with the first acquisition module 10, for obtaining the individual by face RGBD atlas Face 3D characteristic informations.
Knowledge Base Module 12 includes memory module 120, and memory module 120 is obtained with the first acquisition module 10 and the first information Modulus block 11 is connected, for the personal identity information to be identified into the personal corresponding face 3D characteristic informations to obtain individual Information, and personal information is preserved, to form face 3D characteristic identity information banks.
Figure 11 is referred to, Figure 11 is that another kind provided in an embodiment of the present invention sets up setting for face 3D characteristic identity information banks Standby structural representation.
The equipment for setting up face 3D characteristic identity information banks of the present embodiment is obtained including the first acquisition module 20, first information Modulus block 21, Knowledge Base Module 22 and training module 23.
Specifically, the first acquisition module 20 is used to gather the face RGBD atlas of individual, wherein, the personal identity information For known.
First information acquisition module 21 is connected with the first acquisition module 20, for obtaining the individual by face RGBD atlas Face 3D characteristic informations.
The first information acquisition module 21 sets up module 211, the and of computing module 212 including the 3rd acquisition module 210, grid Analysis module 213.
Wherein, the 3rd acquisition module 210 is connected with the first acquisition module 20, for by RGBD man face image acquiring faces Characteristic point.
Grid is set up module 211 and is connected with the 3rd acquisition module 210, for setting up face colour 3D nets according to characteristic point Lattice.
Computing module 212 is set up module 211 and is connected with grid, for the spy according to face colour 3D grids measures characteristic point Value indicative simultaneously calculates the annexation between characteristic point.
Analysis module 213 is connected with computing module 212, for analyzing characteristic value and annexation to obtain face 3D features Information.
Knowledge Base Module 22 includes memory module 220 and management module 221.
Memory module 220 is connected with the first acquisition module 20 and analysis module 213, for by the personal identity information The personal corresponding face 3D characteristic informations are identified to obtain personal information, and personal information is preserved, to form face 3D Characteristic identity information bank.
Management module 221 is connected with memory module 220, and management module 221 is used to carry out hierarchical classification to identity information Management.Wherein, level includes personal attribute's level and group property level.
Training module 23 is connected with the first acquisition module 20, first information acquisition module 21 and Knowledge Base Module 22, uses In carrying out recognition of face training to face 3D characteristic identities information bank.
Specifically, training module 23 includes control module 230 and comparing module 231.
Control module 230 is used for the face RGBD figures of the tester for controlling the collection known identities information of the first acquisition module 20 Collection, control first information acquisition module 21 obtains the face 3D features letter of the tester from the face RGBD atlas of tester Breath.Wherein, tester includes preserving the personal of personal information in face 3D characteristic identity information banks and not in face 3D The individual of personal information is preserved in characteristic identity information bank.
Comparing module 231 is connected with control module 230, the face 3D characteristic informations and face of the tester for that will obtain Face 3D characteristic informations in 3D characteristic identity information banks are compared.
Memory module 220 is additionally operable to when comparison result is correct, by the face RGBD atlas of tester, corresponding face 3D Characteristic information and identity information are saved in face 3D characteristic identity information banks.
Refer to Figure 12, Figure 12 be it is provided in an embodiment of the present invention another set up setting for face 3D characteristic identity information banks Standby structural representation.
The equipment for setting up face 3D characteristic identity information banks of the present embodiment is with the difference of above-described embodiment, this implementation The equipment of example also includes the second acquisition module 24 and the second data obtaining module 25.
Specifically, the second acquisition module 24 is used to gather the personal face RGB atlas.
Second data obtaining module 25 is connected with the second acquisition module 24, for obtaining the individual by face RGB atlas Face 2D characteristic informations.
Memory module 220 is also connected with the second acquisition module 24 and the second data obtaining module 25, for by the individual Face 2D characteristic informations be saved in face 3D characteristic identity information banks with the face 3D characteristic informations for identifying identity information.
Figure 13 is referred to, Figure 13 is a kind of entity for setting up face 3D characteristic identity information banks provided in an embodiment of the present invention The structural representation of device.The device of present embodiment can perform the step in the above method, and related content refers to above-mentioned Detailed description in method, will not be repeated here.
The intelligent electronic device includes the memory 62 that processor 61 is coupled with processor 61.
Memory 62 is used for storage program area, the program of setting and face RGBD atlas, face 3D characteristic informations and body Part information.
Processor 61 is used to gather the face RGBD atlas of individual, wherein, the personal identity information is known；By institute State face RGBD atlas and obtain the personal face 3D characteristic informations；The identity information of the individual is identified to the individual right The personal information is preserved to form face 3D characteristic identities by the face 3D characteristic informations answered to obtain personal information Information bank.
Processor 61 is additionally operable to carry out recognition of face training to the face 3D characteristic identities information bank.
Processor 61 is additionally operable to the face RGBD atlas of the tester for gathering known identities information；From the people of the tester The face 3D characteristic informations of the tester are obtained in face RGBD atlas；Will obtain the tester face 3D characteristic informations with Face 3D characteristic informations in the face 3D characteristic identity information banks are compared；If comparison result is correct, by the survey Face RGBD atlas, the corresponding face 3D characteristic informations and the identity information for trying people are saved in the face 3D features In identity information storehouse.
Processor 61 is additionally operable to gather the personal face RGB atlas；Obtain the individual's by the face RGB atlas Face 2D characteristic informations；The identity information of the individual is identified to the corresponding face 3D characteristic informations of the individual and institute Face 2D characteristic informations are stated to obtain personal information, and the personal information is preserved to form face 3D characteristic identity information Storehouse.
Processor 61 is additionally operable to the characteristic point by the RGBD man face image acquirings face；Set up according to the characteristic point Face colour 3D grids；According to face colour 3D grids measure the characteristic point characteristic value and calculate the characteristic point it Between annexation；The characteristic value and the annexation are analyzed to obtain the face 3D characteristic informations.
In several implementation methods provided by the present invention, it should be understood that disclosed apparatus and method, can pass through Other modes are realized.For example, equipment implementation method described above is only schematical, for example, module or unit Divide, only a kind of division of logic function there can be other dividing mode when actually realizing, for example multiple units or component Can combine or be desirably integrated into another system, or some features can be ignored, or do not perform.It is another, it is shown or The coupling each other for discussing or direct-coupling or communication connection can be the indirect couplings of device or unit by some interfaces Close or communicate to connect, can be electrical, mechanical or other forms.
The unit illustrated as separating component can be or may not be physically separate, be shown as unit Part can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple networks On unit.Some or all of unit therein can be according to the actual needs selected to realize the mesh of present embodiment scheme 's.
In addition, during each functional unit in each implementation method of the invention can be integrated in a processing unit, also may be used Being that unit is individually physically present, it is also possible to which two or more units are integrated in a unit.It is above-mentioned integrated Unit can both be realized in the form of hardware, it would however also be possible to employ the form of SFU software functional unit is realized.
If integrated unit, can to realize in the form of SFU software functional unit and as independent production marketing or when using To store in a computer read/write memory medium.Based on such understanding, technical scheme substantially or Saying all or part of the part or technical scheme contributed to prior art can be embodied in the form of software product Out, computer software product storage is in a storage medium, including some instructions are used to so that a computer equipment (can be personal computer, server, or network equipment etc.) or processor (processor) perform each implementation of the present invention The all or part of step of methods.And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. it is various Can be with the medium of store program codes.
In sum, the present invention obtains face 3D characteristic informations by face RGBD atlas, then by personally identifiable information mark Know the personal corresponding face 3D characteristic informations and preserve together and form face 3D characteristic identity information banks for face Identification, improves the accuracy of recognition of face, and be not readily susceptible to attitude, expression, illumination and facial makeup of face etc. The influence of the change of situations such as non-geometric cosmetic variation and fat or thin face.
Embodiments of the present invention are the foregoing is only, the scope of the claims of the invention is not thereby limited, it is every using this Equivalent structure or equivalent flow conversion that description of the invention and accompanying drawing content are made, or directly or indirectly it is used in other correlations Technical field, is included within the scope of the present invention.

Claims (16)

1. a kind of method for building up of face 3D characteristic identity information banks, it is characterised in that comprise the following steps：
Personal face RGBD atlas is gathered, wherein, the personal identity information is known；
The personal face 3D characteristic informations are obtained by the face RGBD atlas；
The identity information of the individual is identified to the corresponding face 3D characteristic informations of the individual to obtain personal information, And preserve the personal information to form face 3D characteristic identity information banks.
2. method according to claim 1, it is characterised in that the face 3D characteristic identities information bank is believed the identity Breath carries out hierarchical classification management.
3. method according to claim 2, it is characterised in that the level includes personal attribute's level and group property layer Level.
4. method according to claim 1, it is characterised in that described that the identity information of the individual is identified to this The personal information is preserved to form face 3D features by the corresponding face 3D characteristic informations of people to obtain personal information After the step of identity information storehouse, also include：
Recognition of face training is carried out to the face 3D characteristic identities information bank.
5. method according to claim 4, it is characterised in that described that pedestrian is entered to the face 3D characteristic identity information banks The step of face recognition training, includes：
Gather the face RGBD atlas of the tester of known identities information；
The face 3D characteristic informations of the tester are obtained from the face RGBD atlas of the tester；
Face 3D in face 3D characteristic informations and the face 3D characteristic identity information banks of the tester that will be obtained is special Reference breath is compared；
If comparison result is correct, by the face RGBD atlas of the tester, the corresponding face 3D characteristic informations and institute Identity information is stated to be saved in the face 3D characteristic identity information banks.
6. method according to claim 5, it is characterised in that the tester is included in the face 3D characteristic identities The personal of personal information is preserved in information bank and personal information is not preserved in the face 3D characteristic identity information banks It is personal.
7. method according to claim 1, it is characterised in that also wrap the step of the collection personal face RGBD atlas Include：Gather the personal face RGB atlas；
It is described also to include the step of obtain the personal face 3D characteristic informations by the face RGBD atlas：By the face RGB atlas obtains the personal face 2D characteristic informations；
It is described that the identity information of the individual is identified to the corresponding face 3D characteristic informations of the individual to obtain individual Information, and by the personal information preserve to form face 3D characteristic identity information banks the step of also include：By the personal institute State identity information and be identified to the corresponding face 3D characteristic informations of the individual and the face 2D characteristic informations to obtain individual Information, and the personal information is preserved to form face 3D characteristic identity information banks.
8. method according to claim 1, it is characterised in that the personal face 3D is obtained by the face RGBD atlas The step of characteristic information, includes：
By the characteristic point of the RGBD man face image acquirings face；
Face colour 3D grids are set up according to the characteristic point；
Measure the characteristic value of the characteristic point and calculate the connection between the characteristic point according to face colour 3D grids and close System；
The characteristic value and the annexation are analyzed to obtain the face 3D characteristic informations.
9. a kind of equipment for setting up face 3D characteristic identity information banks, it is characterised in that including：
First acquisition module, the face RGBD atlas for gathering individual, wherein, the personal identity information is known；
First information acquisition module, is connected with first acquisition module, for obtaining this by the face RGBD atlas The face 3D characteristic informations of people；
Knowledge Base Module, including memory module, the memory module are obtained with first acquisition module and the first information Modulus block is connected, for the identity information of the individual to be identified into the corresponding face 3D characteristic informations of the individual to obtain Personal information is obtained, and the personal information is preserved, to form face 3D characteristic identity information banks.
10. equipment according to claim 9, it is characterised in that described information library module also includes management module, the pipe Reason module is connected with the memory module, and the management module is used to carry out the identity information hierarchical classification management.
11. equipment according to claim 10, it is characterised in that the level includes personal attribute's level and group property Level.
12. equipment according to claim 11, it is characterised in that the equipment for setting up face 3D characteristic identity information banks Also include training module, the training module and the first acquisition module, first information acquisition module and described information library module Connection, for carrying out recognition of face training to the face 3D characteristic identities information bank.
13. equipment according to claim 12, it is characterised in that the training module includes control module and compares mould Block；
The face RGBD that the control module is used for the tester for controlling the first acquisition module collection known identities information schemes Collection, controls the first information acquisition module that the face 3D spies of the tester are obtained from the face RGBD atlas of the tester Reference ceases；
The comparing module is connected with the control module, for by the face 3D characteristic informations of the tester of the acquisition Compare with the face 3D characteristic informations in the face 3D characteristic identity information banks；
The control module is also connected with the memory module, and the control module is additionally operable to when comparison result is correct, control The memory module preserves the face RGBD atlas of the tester, corresponding face 3D characteristic informations and the identity information To in the face 3D characteristic identity information banks.
14. equipment according to claim 13, it is characterised in that the tester is included in the face 3D feature bodies The personal of personal information is preserved in part information bank and is preserved not in the face 3D characteristic identity information banks personal The individual of information.
15. equipment according to claim 9, it is characterised in that the equipment also includes：
Second acquisition module, for gathering the personal face RGB atlas；
Second data obtaining module, is connected with second acquisition module, for obtaining the individual by the face RGB atlas Face 2D characteristic informations；
The memory module is also connected with second acquisition module and the second data obtaining module, for by the personal people Face 2D characteristic informations are saved in the face 3D characteristic identity information banks with the face 3D characteristic informations for identifying identity information.
16. equipment according to claim 9, it is characterised in that the first information acquisition module is further included：
3rd acquisition module, is connected with first acquisition module, for the feature by the RGB man face image acquirings face Point；
Grid sets up module, is connected with the 3rd acquisition module, for setting up face colour 3D grids according to the characteristic point；
Computing module, sets up module and is connected with the grid, for measuring the characteristic point according to face colour 3D grids Characteristic value and calculate the annexation between the characteristic point；
Analysis module, is connected with the computing module, for analyzing the characteristic value and the annexation to obtain the people Face 3D characteristic informations.
CN201611032737.7A 2016-11-14 2016-11-14 The method for building up and equipment of face 3D characteristic identity information banks Pending CN106778489A (en)

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