EP2923302A1

EP2923302A1 - Method for generating a three-dimensional facial model

Info

Publication number: EP2923302A1
Application number: EP13792400.7A
Authority: EP
Inventors: Sami Romdhani
Original assignee: Morpho SA
Current assignee: Idemia Identity and Security France SAS
Priority date: 2012-11-20
Filing date: 2013-11-20
Publication date: 2015-09-30
Also published as: FR2998402B1; US10235814B2; US20150310673A1; JP2016501396A; FR2998402A1; WO2014079897A1; JP6318162B2

Abstract

The invention relates to a method for generating a three-dimensional facial model the shape of which can changed on the basis of a plurality of images of faces of persons, including the steps that involve: generating a facial template; acquiring shapes from examples of faces of persons; repeatedly changing the shape of the template for each example of a face of a person, so that the shape of the changed template corresponds to the shape of the face example, and determining the change in shape between the initial template and the changed template; and generating the facial model as a linear combination of the shape of the template and the changes in shape between the initial template and the changed template, for each example of a face of a person. The invention also relates to a method for processing an image of a face of a person such as to generate a three-dimensional image of the face of the person from of said deformable model.

Description

METHOD FOR GENERATING A FACE MODEL INTO THREE

DIMENSIONS FIELD OF THE INVENTION

The field of the invention is that of the image processing of faces of individuals, to generate a front view of an individual from a non-frontal image thereof.

The invention is particularly applicable to the identification of individuals by face recognition.

STATE OF THE ART

The identification of individuals by face recognition is implemented by comparing two images of faces, and deducing from this comparison a score evaluating the similarity between the faces on the images.

When the compared faces do not appear on the image with the same pose, the resemblance score can be strongly degraded, even if the faces on the images come from the same person. This results in a significant loss of efficiency of identification processes implemented, since the pose of the face on the images is not the same.

The optimal recognition efficiency is therefore achieved not only when two faces have the same pose on the compared images, but also when the faces are seen from the front, because this view provides the most information on the shape of the face.

However, it is impossible to systematically obtain a face image of a face for identification. Indeed, in most situations, a previously recorded front image of an individual, such as for example in an identity document, is compared to an image of the individual acquired "on the fly" by a computer system. acquisition such as a surveillance camera. The image thus acquired is hardly ever a frontal image because the individual does not look through the acquisition system.

In this case, image processing methods have been developed for generating, from an image of a face, an image of the same face, seen from the front. To do this, the acquired image is processed to determine the three-dimensional shape of the face of the individual on the image, its pose, that is to say its position relative to a front view, as well as a representation of the texture of the face, that is, the physical appearance of the surface of the face superimposed on the three-dimensional structure of the shape of the face.

The determination of the three-dimensional shape of the face of the individual is carried out by deforming a deformable three-dimensional model of the human face, to minimize the difference between the characteristic points of the model (position of the eyes, nostrils, tip of the nose, commissures of the lips, etc.) and the corresponding points of the face on the image.

Different types of three-dimensional face models have already been proposed. For example, according to the publication A Morphable Model For the Synthesis of 3D Faces, V. Blanz, T. Vetter, Mac-Planck-Institut fur biologische Kybernetik, a three-dimensional face model generated from examples of faces of individuals, whose characteristic points have been mapped, and on which a statistical analysis called "principal components analysis" has been implemented. This analysis is based on a particularly constraining assumption that human face shapes have a Gaussian probability density.

However, this hypothesis is not proven, so that the face model thus generated can not possibly be used to generate any human face.

PRESENTATION OF THE INVENTION

The purpose of the invention is to propose a method of treating a face image of an individual not having the aforementioned drawback, and in particular making it possible to determine the shape of any human face appearing on a face. picture. In this respect, the subject of the invention is a method for generating a deformable three-dimensional face model from a plurality of images of faces of individuals, the method being characterized in that it comprises the steps of:

generate a face template, acquire example shapes of individuals' faces, for each individual face example, iteratively deform the template so that the shape of the deformed template matches the shape of the face example, and determine the deformation between the initial template and the deformed template, said iterative deformation of the template including minimizing the derivative of the gap between the original template and the deformed template, to constrain the deformed template to retain a human face shape.

- and

the generation of the face model as a linear combination of the shape of the template and deformations between the initial template and the deformed template for each example of an individual's face.

Advantageously, but optionally, the method according to the invention also has at least one of the following characteristics:

the acquisition of example shapes of faces of individuals comprises the detection of characteristic points of each example of faces of individuals, and the matching of the corresponding characteristic points between the examples of faces.

the iterative deformation of the template comprises, for each example of an individual's face, the modification of the positions of the characteristic points of the template in order to minimize a difference of position between said characteristic points and the corresponding points of the example of an individual's face.

the iterative deformation of the template further comprises minimizing a difference in position between the points of the template and the surface of the face example.

the step of iterative deformation of the template comprises the iterative minimization of a linear combination of:

o the difference in position between the characteristic points of the face example and the corresponding points of the template,

o the derivative of the gap between the initial template and the deformed template, and a difference in position between the points of the template and the surface of the facial example, and the coefficients of the linear combination vary from one iteration to the next.

The invention also proposes a method for processing at least one face image of an individual, comprising the steps of generating, from the image, a three-dimensional representation of the individual's face, said representation comprising the steps of:

determining a sketch of the pose of the face of the individual on the image by comparison between positions of characteristic points of the face of the individual and the corresponding point positions of a reference human face shape,

determining the shape and pose of the face of the individual on the image by iteratively deforming a three-dimensional model obtained by implementing the method of generating a face model according to the invention, so that the shape of the deformed model corresponds to the shape of the face of the individual in the image,

the deformation of the three-dimensional model being carried out by modifying the coefficients of the linear combination of the model,

the method being characterized in that the changes of the coefficients of the linear combination are constrained to ensure that the deformed pattern corresponds to a human face.

Advantageously, but optionally, the method of processing a face image further comprises at least one of the following features: the modifications of the coefficients of the linear combination are constrained by minimizing the norm of the derivative of the difference between the initial model and the distorted model.

the pose and the shape of the face of the individual on the image are estimated simultaneously, by iterative modification of the pose and the shape of the three-dimensional model to minimize the difference between the characteristic points of the face of the individual on the image and corresponding points of the model. the modification of the pose of the model comprises at least one transformation among the preceding group: translation, rotation, change of scale, the modification of the shape of the three-dimensional model comprises the determination of the coefficients of the linear combination between the face template and the deformations applied to the template to obtain each face example.

the method further comprises the steps of:

o from the estimation of the pose and the shape of the face of the individual on the image, to generate a representation of the texture of the face of the individual, and

o generate a front view of the individual's face.

The method is implemented on a plurality of face images of individuals, and:

the step of determining a blank of the pose of the face of the individual is implemented on each face image of the individual, and

o the step of determining the shape and the pose of the face of the individual is implemented on all the face images by iteratively deforming the three-dimensional model so that the shape of the deformed model corresponds to the shape of the individual's face on the images.

The invention finally proposes a system for identifying individuals comprising at least one control server of an individual to be identified, and at least one management server of a database of N reference images of listed individuals, the server control system comprising acquisition means adapted to proceed to the acquisition of an image of the face of the individual,

the system for identifying individuals being characterized in that one of the control server and the management server comprises processing means adapted to implement the processing method according to the invention, and, from a front view of the face of an individual obtained, implement a face recognition treatment by comparison with the reference images of the base, in order to identify the individual. DESCRIPTION OF THE FIGURES

Other features, objects and advantages of the present invention will appear on reading the detailed description which follows, with reference to the appended figures, given by way of non-limiting examples and in which:

FIG. 1 represents an exemplary identification system adapted to implement an image processing method.

FIG. 2a represents the main steps of the method for generating a three-dimensional model of faces,

FIG. 2b represents the main steps of the image processing method according to the invention.

Figure 3 illustrates the characteristic points of a face.

Figure 4 illustrates notations used for calculating a differentiation matrix.

FIG. 5a is an image of a face to be treated in order to identify the individual on the image,

FIGS. 5b and 5c are respectively the restitution of the shape of the face of the individual and a representation of the texture of said face,

Figure 5d is a front view of the face of the individual reconstructed from the image of Figure 5a.

Figures 6a and 6b are respectively input images of the same face and a face image of the face obtained from these input images. DETAILED DESCRIPTION OF AT LEAST ONE MODE OF IMPLEMENTING THE INVENTION

Image processing system

Referring to Figure 1, there is shown an identification system 1 adapted to implement an image processing method. During the identification of an individual I, a control server SC, provided with means 1 1 of acquisition of appropriate images, proceeds to the acquisition of an image of the face of the individual. This image may be non-frontal. In order to identify the individual I, the control server SC can also acquire a face image of the individual, this time frontal, which is stored in an identity document.

The control server then advantageously comprises processing means adapted to implement, on the first image of the face of the individual, a treatment aimed at "frontalising" this image: that is, to generate, from of this image, a frontal image. Following this front-end processing, the control server can advantageously compare the two front images it has, to determine if the faces on the images correspond to the same person.

Alternatively, the second face image can be stored, among other images, in a database of a management server SG. Then, the control server transmits the first image that it has acquired to the management server, and the latter implements the method of processing the first image and comparison to identify the individual I. In this case, the comparison can take place between the "frontalised" image of the individual and each face image recorded in the database.

Face image processing method

Referring to Figure 2, there will now be described a method of processing face images, to obtain a frontal image of a face of an individual.

In computer science, every three-dimensional object such as a human face can be described using the following three elements:

- The shape of the object, which is composed of a set of 3D vertices, each vertex being a point of the object defined by coordinates along three orthogonal directions. We denote by N the number of vertices vi of an object, each object being described by a matrix of dimension 3xN S = (v ₁ , ..., v _N ) in which the vertices are arranged in columns.

- The surface of the object: it is materialized by connecting vertex between them to form triangles. A list of triangles is thus defined for each object, each triangle being indicated by the three indexes of the corresponding columns of the matrix S. A representation of the texture of the object: it is an image used to color the object in three dimensions obtained from its shape and its surface. The surface of the object defined by the triangle list is used to match the vertices of the object to a particular texture.

Generation of a deformable 3D model

The method includes a first step 100 of generating a three-dimensional model of a human face shape, which can be deformed to obtain any type of human face shape.

This model is mathematically formulated as a linear combination of examples of individuals' faces, noted

Where S ⁰ is a template of human face shape, constituting the basis of the model, and S ° + S ^j represents the shape of the face of a particular example of a real individual. Therefore, S ^j represents the gap between one of the face examples and the template.

The coefficients α _; are later determined to distort the model S to match the face of an individual that we want to identify.

We will now describe the steps to obtain this model.

During a step 1 10, the template S ⁰ of human face is generated: it may be a face shape of a particular individual, or an average of faces of a plurality of faces. 'people. In all cases, the face shape or shapes are defined by a series of vertex corresponding to points of the face. These points comprise, inter alia, a number N _s of characteristic points of a face, represented in FIG. 3, typically 22, and which are the corners of the eyes, the ends of the mouth, the nostrils, the tip of the nose, ears, etc.

These feature points can be manually marked by an operator from a front face image, or they can be automatically spotted by a server.

The human face template also includes the order of a few thousand other vertex acquired by a 3D scanner. During a step 120, we proceed to the acquisition of forms of examples of faces of real individuals. This acquisition is implemented in the same way as before, by identifying the characteristic points of the faces of individuals to generate a list of vertexes.

The shapes of faces thus acquired each correspond to a S ° + S ^j .

To construct the three-dimensional model, the deflection S ^j between the face and the template is determined from the vertex lists of each face shape.

However, all the forms generated by the three-dimensional model S must be possible face shapes, and not mathematical aberrations. To ensure this result, we put all the examples of face shapes in correspondence, that is to say by associating each vertex of a face with a defined number. For example, a given number is attributed to the tip of the nose and another number to the left commissure of the lips. These numbers correspond to the vertex indices.

The peculiarity of the template is that it is a form of face for which vertex indexing is already done. Therefore, the vertex indexing of each example of a face shape is performed by mapping in step 130 the vertices of each example shape to the vertex of the template.

To do this, it deforms in a step 131 the template iteratively to minimize the gap between the template shape and that of the face example, the deformed template must always correspond to a human face shape.

The mathematical function to be minimized includes three terms.

The first term serves to minimize the distance between the characteristic points of an example of a face and the corresponding points of the template. It is written:

Where i is the index of a characteristic point, is a vertex of a point of an individual example face corresponding to the characteristic point i, v _ki is a vertex of a point of the template after deformation corresponding to the same point characteristic i, and N _s is the number of characteristic points in a face, for example 22.

It is therefore sought to modify the positions of the characteristic points of the template iteratively to correspond to the positions of the same characteristic points on the face example.

The second term is used to match the surface of the face shape of the template with the surface of the shape of the face example. The function to be minimized represents the difference between the points of the template and the surface of the face example that is closest to the feature points. It is noted:

Where Pvi is a point on the surface of the face example, that is to say a point corresponding to the projection on the surface of the face of the vertex ν ,. It is possible that the surface of the face example is incomplete, if for example it is obtained from a non-frontal image, and that points of the template do not correspond to any point of the face example. In this case, these points of the template are not taken into account.

The third term constrains the deformed template to remain a real human face, even if the face example used for deformation of the template is incomplete or contains noise. This term makes the jig deformed as "smooth" as possible, that is to say as continuous as possible, by minimizing the standard of the derivative of the transformation of the jig, at each iteration. This standard is expressed as follows:

\\ A - vec (S °)) \\ ²

Where v is the concatenation of the 3D vertices of the deformed template, and vec (S °) the same term for the template before transformation, v and vec (S °) are vectors of size 3N X 1.

The derivation of a function being a linear operation, its computation can be realized by the multiplication of the function by a matrix. In this case, A is a vector differentiation matrix v-vec (S °), of dimension 3T x 3N, where T is the number of triangles of the surface of the template.

The derivative is calculated for each triangle t of the surface of the template, the derivative of the deformation of a triangle t being calculated with respect to the triangles q neighbors of the triangle t, by approximation of the finite difference of the triangle t with the neighboring triangles q as follows:

Where N _t is the set of triangles q adjacent to the triangle t, w _qt is a weighting factor that depends on the surfaces of the triangles t and q, d _t is the deformation of the triangle t at its centroid, and b _t is the position of the centroid of the triangle t. The distance between the centers of gravity and the weighting factor are calculated on the undeformed template S ⁰ .

With reference to FIG. 4, the weighting factor w _qt is the sum of the surfaces of two triangles whose base is the edge connecting the triangles t and q, and the opposite vertex to this base is respectively the barycenters b _t of the triangle t and that b _q of the triangle q.

To obtain the deformation _{t t} of the triangle t at its barycentre (that is to say the displacement of the barycentre between the undeformed template and the deformed template), the deformation of the shape is multiplied (v-vec (S ⁰ )) by a matrix B _t of dimension 3 3N which is zero everywhere except the elements associated with the vertex of the triangle t. These elements are then equal 1/3.

The barycenter b _t of the triangle t being the average of its three vertexes, the multiplication of this matrix B with the deformation (v-vec (S ⁰ )) makes it possible to obtain the displacement of the barycentre of the triangle.

The matrix A, of dimension 3T x 3N, is obtained by vertically concatenating the set of matrices B _t associated with each triangle t, whose coefficients corresponding to the vertex of a triangle t are multiplied by weighting factors w _qt and divided by the distances between the centers of gravity \\ b _q - b _t \\

It can be seen that the differentiation matrix A depends solely on the surface of the undistorted template (list of triangles of S ⁰ ), and not on the shape of the deformed template v. It is therefore constant.

The three terms described in detail above are minimized simultaneously, so we determine: Where κ, and γ are weighting coefficients of each term. This minimization can be solved linearly by decomposition into singular values.

Since this minimization is iterative, at the beginning of the matching step, the template may be arbitrarily removed from the individual's face example, and thus the points p _v , of the face surface closest to the points V, of the template are not well defined. We then set a low value for κ compared to the other weights. Moreover, a significant value is fixed for γ to ensure that the transformation is almost rigid, that is to say that the shape of the face of the template is the least distorted possible.

At each iteration of the minimization, the value of κ is increased. At each iteration, the points p _v are searched on the surface of the example of the individual face, as being the closest to the points v, of the template deformed at this iteration. As the minimization is iterated, these points ρ _ν , are more and more reliable and the value of the coefficient γ is decreased to make the comparison more flexible.

This iterative mapping step is performed for each individual face example. It results in a deformed template, which corresponds to an example of a face, from which we can deduce the value of S ^j , the deviation between the template and the example of the face.

Thus, at the end of this step, a deformable three-dimensional face model is obtained, comprising the template S ⁰ and the deviations S ^j , which can be made linear combinations to obtain any individual face. Once this model is obtained, it can be used to generate, from a face image of an individual, a three-dimensional shape of his face.

Returning to FIG. 2, during a step 200, an image of the face of an individual that one wishes to identify is acquired, for example by means of a control server of FIG. 1. An example of such an image is appended in FIG. 5a.

Then, it implements a so-called "rigid" estimation step 300 of the pose, or position, of the face on the image. The estimate is called rigid because it does not include deformation of the face. The pose is defined relative to a reference using six parameters: three rotation angles, two translation parameters and a scale factor and is defined as follows:

p = s. R. v + t

Where p is a two-dimensional vector, comprising the X and Y coordinates of the projection of each vertex v in three dimensions, s is the scale parameter, R is a 2 x 3 matrix whose two lines are the first two rows of a rotation matrix, and t is a translation vector in X and Y.

The rotation matrix is written according to the angles of Euler a _x , a _y , and a _z as follows:

R

To estimate the pose, the positions of the characteristic points of the face of the individual on the image are acquired in the same way as previously, for example by the pointing of an operator or by an automatic detection. In this regard, reference may be made to the following publications:

Yow et al. Feature-based human face detection. Image and Vision

Computing, 15 (9): 713-735, 1997.

Nikolaidis and Pitas, Facial feature extraction and determination of pose, Proc. Of the NOBLESSE Workshop on Nonlinear Model Based Image Analysis, page 257-262, 1998.

Lee et al. Realtime facial feature detection for person identification system,

IAPR Workshop on Machine Vision Applications, 2000.

Then, we compare the positions of these points with the vertex projections of the corresponding points of a typical example of a face, which can be in this case the S0 template used to generate the three-dimensional model. This comparison is made by iteratively modifying the pose of the face mask, by varying the parameters mentioned above, to minimize the difference between the projections of the vertex of the face of the individual and the template in the following way:

Where Pi is the position of a characteristic point i on the image and v, is a vertex of the corresponding point i of the template. Depending on the image of the individual available, which is non-frontal, certain characteristic points may be invisible on the image or their position may be uncertain.

Each characteristic point i is therefore assigned a weighting coefficient c, representative of the "confidence" in the position of the point. If a point is invisible on the image, then its confidence coefficient is zero.

The determination of the pose of the individual on the image is then written as follows:

22 The pose obtained for the template at the end of the minimization constitutes the pose of the face of the individual on the image.

This optimization problem is solved with a two-step procedure, the first step 310 being the linear search of a solution, and the second step being the nonlinear minimization 320 to refine the estimate of the pose obtained with the first one. step.

Linear estimation step 310 will now be described.

This estimation assumes that the distance between the positions of the characteristic points and the modeling of their projection, called the "retroprojection" error, is Gaussian with a zero mean and a standard deviation equal to -L, and that if the error is independent for all points, then we can show that the solution of the previous equation is also the solution of the following linear system:

Ax = b

With

χ ^τ - (sr _{1: L} sr ₁₂ sr ₁₃ t _x sr ₂ sr ₂ 2 sr ₂₃ £ _y )

b ^{T =} ^(c LPX, LPY the ^{^c-c} 2lPx, 22 ^c 2lPy 22

The resolution of this overdetermined linear system is standard linear algebra and is performed using the pseudo-inverse given by the singular value decomposition described in Golub et al. Matrix computations volume 3, Johns Hopkins Univ Pr, 1996.

This first step of linear resolution 310 provides a good initial estimate of the pose, but the assumption previously adopted for the linear resolution not being however based in practice, the estimation needs to be refined by the step of non-linear estimate 320.

The result of the linear step is refined by implementing a non-linear iterative step 320, for which a preferred method is the minimization of

Levenberg-Marquardt. Reference may be made to Gill et al. Practical

Optimization. Academy Press, London and New York, 1981.

This step finally makes it possible to obtain a first estimate of the pose of the face of the individual on the image, this pose then being refined during the step 400 of "flexible" estimation of the pose and the shape of the face . It is therefore considered that at this stage a "draft" of the pose of the face has been determined.

We will now describe step 400 of flexible estimation of the pose and the shape. This estimate is implemented using the three-dimensional face model obtained in step 100. As indicated above, this model is written in the form of a linear combination of the template S0 and deviations of this template compared to examples of individuals:

The shape of any face can be obtained by choosing the coefficients a, of the linear combination. The flexible estimation of the shape and pose of the face of the individual on the image is thus achieved by minimizing the difference between the projections of the characteristic points p, the face of the individual on the image, and the same projections of the model. To do this, it is iteratively modified the shape of the face obtained by the model (thanks to the coefficients α _; ) and the parameters of pose of the face. Mathematically, we seek to obtain the following minimum:

22 M

min Cj _Pi - (s R. (S ° + ajSi) + t) ²

a, s, a _x , CLya _z , t ' ¹

7 = 1

However the resolution of this equation could lead to a form of the distorted face model that no longer corresponds to a human face. Indeed, the characteristic points p, of the face of the individual can be noisy or inaccessible, and the system would not then be determined enough.

The aj coefficients are then constrained to guarantee a realistic human face. For this, the norm of the derivative of the deformation of the three-dimensional model is minimized, the deformed vertexes of the model being here defined as a function of the vector a comprising the aj for j between 1 and M. The derivative of the deformation of the Three-dimensional model is obtained by multiplying the deformed model by a matrix A 'constructed in the same way as the matrix A of previous differentiation.

This minimization step corresponds to a hypothesis of continuity of the face, which is verified regardless of the individual and therefore allows the process to be as general as possible, that is to say applicable for any individual.

We thus obtain the following equation:

22 M

Min Cj _Pi -.? (s A. (S + AJS () + t) + Y \\ A'Sa \ a, s, a _x, _v a _z, t ^'1

i = l 7 = 1

This equation is solved, analogously to the nonlinear minimization step, using the Levenberg-Marquardt minimization algorithm. The initialization of the pose is provided by the pose obtained at the end of the rigid estimation step. The initial form used for the minimization is that of the template S ⁰ of origin, ie that the values of the coefficients α _; initials are zero.

Once this estimate is implemented, the deformed three-dimensional model thus corresponds to the three-dimensional shape of the individual's face in the image, represented in FIG. 5b. This three-dimensional shape can be manipulated simply to obtain a frontal representation.

In addition, from the three-dimensional shape and the original image, during a step 500, a representation of the texture of the face of the face is generated. the individual, represented in FIG. 5c. To do this, we sample the original image at the positions of the points of the three-dimensional shape.

Finally, during a step 600, from the face shape, positioned from the front, and from the representation of the texture of the face, a two-dimensional face image of the individual is generated. This image is illustrated in Figure 5d. It can serve as a basis for a conventional identification method by face recognition.

Finally, it can be noted that this method can be implemented for a plurality of input images of the same individual, to obtain a unique three-dimensional shape of the individual's face and a unique representation of the texture of the face of the person. the individual. However, a set of installation parameters must be estimated for each input image.

The steps 310 and 320 of linear estimation and nonlinear of the pose are implemented for each input image. Then the step 400 of flexible estimation of the pose and the shape is carried out on all the K images by searching for the following minimum:

+ y \\ ASa

Then a representation of the texture of the face is extracted from each image, and they are melted together, according to the visibility of each element of the face on each input image, to obtain a unique representation of the texture of the face.

The realization of a new synthesis image is performed in the same manner as previously for a single image.

FIG. 6a shows two input images of the same individual, and in FIG. 6b a front image of the individual generated with this method.

Claims

1. A method of generating a deformable three-dimensional face model from a plurality of face images of individuals,

the method being characterized in that it comprises the steps of:

generate a face mask (S ⁰ ),

acquire forms of examples of faces of individuals (S ° + S ^j ),

for each example of an individual face, iteratively deforms the template so that the shape of the deformed template corresponds to the shape of the face example, and determines the deformation (S ^j ) between the initial template and the deformed template, said iterative deformation including minimizing the derivative of the gap between the initial template and the deformed template, to constrain the deformed template to retain a human face shape, and generating the face template as a linear combination of the shape of the template. template (S ⁰ ) and deformations (S ^j ) between the initial template and the deformed template for each example of an individual's face.

The method of claim 1, wherein acquiring face example shapes of individuals comprises detecting feature points of each example of faces of individuals, and matching the corresponding feature points between the examples. faces.

3. Method according to claim 2, wherein the iterative deformation of the template comprises, for each example of a person's face, the modification of the positions of the characteristic points of the template to minimize a difference in position between said characteristic points and the corresponding points. of the individual face example.

The method of claim 3, wherein the iterative deformation of the template further comprises minimizing a difference in position between the template points and the surface of the face example.

5. Method according to one of the preceding claims, wherein the step of iterative deformation of the template comprises the iterative minimization of a linear combination of:

the difference in position between the characteristic points of the face example and the corresponding points of the template,

the derivative of the gap between the initial template and the deformed template, and a difference in position between the points of the template and the surface of the example of a face,

wherein the coefficients of the linear combination vary from one iteration to the next.

A method of processing at least one face image of an individual, comprising the steps of generating, from the image, a three-dimensional representation of the individual's face, said representation comprising the steps of at :

determining the shape and pose of the face of the individual on the image by iteratively deforming a three-dimensional model obtained by implementing the method according to one of the preceding claims, so that the shape of the deformed model corresponds to the shape of the individual's face in the image,

7. The processing method according to claim 6, wherein the changes of the coefficients of the linear combination are constrained by minimizing the standard of the derivative of the difference between the initial model and the deformed model.

8. Treatment method according to one of claims 6 or 7, wherein the pose and the shape of the face of the individual in the image are estimated simultaneously, by iterative modification of the pose and the shape of the model in three dimensions to minimize the difference between the characteristic points of the individual's face on the image and the corresponding points of the model.

9. Processing method according to the preceding claim, wherein the modification of the pose of the model comprises at least one transformation from the previous group: translation, rotation, scaling.

10. Processing method according to the preceding claim, wherein the modification of the shape of the three-dimensional model comprises determining the coefficients of the linear combination between the face mask and the deformations applied to the template to obtain each face example.

1 1. The treatment method according to one of claims 6 to 10, further comprising the steps of:

from the estimation of the pose and the shape of the face of the individual on the image, generate a representation of the texture of the face of the individual, and - generate a front view of the face of the individual .

12. Treatment method according to one of claims 6 to 1 1, implemented on a plurality of face images of individuals, and wherein:

the step of determining the shape and the pose of the face of the individual is implemented on all the face images by iteratively deforming the three-dimensional model so that the shape of the deformed model corresponds to the shape of the individual's face on the images.

13. An identification system for individuals comprising at least one control server (SC) of an individual (I) to be identified, and at least one management server (SG) of a database (DB) of N frames of references of listed individuals, the control server (SC) comprising acquisition means adapted to acquire an image of the face of the individual (I),

the system for identifying individuals being characterized in that one of the control server (SC) and the management server (SG) comprises processing means adapted to implement the processing method according to one of the claim 12 or 13, and, from a front view of the face of an individual obtained, implement a face recognition treatment in comparison with the reference images of the base, in order to identify the individual .