Age progression of subject facial image

Age progression of a test facial image is facilitated by compiling training data, including a training set(s) having selected initial images of subjects by gender and age-group. In addition, the age progression includes manipulating the training data, including: for a given age-group of a training set, substantially aligning respective face shapes; determining a common frame based on the aligned shapes; substantially aligning respective face appearances to generate a shape-free form corresponding to the face appearance of each subject, using the substantially aligned shapes to generate an age-specific shape-dictionary for each age-group, and a common shape-dictionary for the age-groups of the training set, and using the aligned appearances to generate at least an age-specific appearance-dictionary for each age-group, and a common appearance-dictionary for the age-groups of the training set. The age specific appearance dictionary for each age group and the common appearance dictionary facilitate age progression of the facial image.

BACKGROUND

The face of a subject includes identification features that may provide information specific to that subject. Such information may, for example, include the identity, gender, age, ethnicity, facial expression, and so forth, of the subject.

Recently, further efforts have been made to develop methods that may facilitate automated recognition and/or verification of the identity of a subject with robustness to different variations, such as, for example, poses, illumination conditions and/or occlusions, and aspiring to human performance capability.

A problem that may be associated with some of these methods is that they are developed on a basis of a training image gallery including various images collected for a subject in a given age. Naturally, the performance capability of such previously-proposed methods is reduced when applied to the facial progression of subjects in ages differing from that used in the compilation of the training image gallery. Such reduced performance capability may be attributed to the fact that age progression is a relatively complex process that may affect at least one of a shape and appearance of the face of a subject. It may be possible to improve the performance capability of such previously-proposed methods by updating the training image gallery with images of the given subject in different ages, but this is relatively time and resource consuming.

Regarding the age progression of the face of a given subject in different age-groups, some previously-proposed methods in this regard may be limited by: the requisite of a minimum age of the subject in an input image that is to be processed and/or for such an input image to be in a frontal pose with a neutral expression; sensitivity to any kind of occlusions, such as, for example, hair, sunglasses and so forth, and such age progression is not performed with consideration to the progression of both appearance and face shape of the subject.

Accordingly, the ability to provide enhanced age progression methods that alleviates the drawbacks associated with previously-proposed methods is an area of interest and continued research for, for instances, establishing commercial advantage in the industry. For instance, it is desirable to be able to age progress the face of a given subject, as provided in an input image that has been collected in situ rather than placing the given subject in a considered and/or specialized context, over a broad age spectrum.

SUMMARY

The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a computer-implemented method of facilitating age progression of a test subject facial image. The computer-implemented method includes: compiling training data and manipulating the training data. The compiled training data includes at least one training set including selected facial images of subjects in a corresponding gender and classified into age groups. Manipulating the training data includes, for a given age-group of a given training set, substantially aligning respective face shapes of subjects; computing a common frame based on the aligned shapes; substantially aligning respective face appearances of subjects, thereby generating a shape-free form corresponding to the face appearance of each subject, using the substantially aligned shapes to generate, in respect of a shape aspect, an age specific shape dictionary for each given age-group, and a common shape dictionary for all the given age groups, of the given training set, in using the substantially aligned appearances to generate, in respect of an appearance aspect, at least an age specific appearance dictionary for each given age group, and a common appearance dictionary for all the given age groups, of the given training set. The age specific appearance dictionary for each given age group, and the common appearance dictionary facilitate age progression of the test subject facial image.

Systems and computer program products related to one or more aspects are also described and claims herein.

Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in details herein and may be considered a part of the claimed invention.

DETAILED DESCRIPTION

Within the description, the same reference numerals or signs are used to denote the same parts or the like.

An embodiment of the present invention may be applied for age progression of a test subject via respective stages including: compiling training data that is to form a basis of performing age progression; manipulating the training data to at least generate further information to be applied in, and further refine, performance of the age progression, and testing the training data, thereby applying the training data for age progression of the test subject in any given age-group.

Compiling Training Data

Reference is now made toFIG. 1, which schematically illustrates a stage1of compiling training data in a computer-implemented embodiment according to an aspect of the present invention.

Compilation of the training data is begun at block10by accessing a database D including images of different subjects in different ages, genders and/or ethnicities. Each of the images is taken in situ, that is, without arranging any subject in a given pose and/or taking any image in a specific context and/or condition, for example.

Because it is desirable that an embodiment of the present invention be applicable over a relatively wide-spectrum of ages, an age-range of subjects whose images are accessed from the database D at block10may be chosen to be relatively wide too and may, by way of example, be chosen to range from one month to over ninety years.

At block11, a respective detected face of subjects in each of the accessed images is mapped. This may be done by mapping each detected subject face according to defined landmark coordinates L. By way of example, L may include 68 landmark coordinates in an embodiment of the present invention. Such mapping according to defined landmark coordinates may be performed by passing a bounding box as input into a face localization method. In this way, a corresponding shape s of each detected face may be obtained and may be defined for x, y landmark coordinates as:
s=[x1,y1,x2,y2, . . . , xL,yL]∈R2L×1.

In an embodiment of the present invention, a selection may be performed of those accessed images that are to form a basis of the training data. In this regard, and according to block12, a subject pose in respective faces detected from the accessed images is determined. For this purpose, a three-dimensional face shape model is used in conjunction with the predefined landmark coordinates L that are used for face mapping as above-described. From a subject pose, it may be automatically ascertained whether there is a non-frontal aspect in the detected face of the subject. At block13, removal of those accessed images including subject poses with at least a non-frontal aspect is performed so that such images are not used for compilation of the training data. At block14, removal of those accessed images with occlusions on the subject face is performed. Non-frontal aspects and occlusions are criteria according to which the accessed images are selected for compilation of the training data in an embodiment of the present invention. However, an embodiment of the present invention is not limited thereto and, indeed other criteria may be introduced for further refinement of such a selection of those accessed images that are to form the basis of the training data.

At block15, respective training sets are compiled whereby, in respect of a given gender, a training set having selected images of subjects in a corresponding gender, and classified into given age-groups K, is compiled. By way of example, the selected images of subjects that are to form the basis of the training data are classified into K different age-groups, where respective training sets may include N images of males and N images of females for each age-group i of the K different age-groups. The training data in an embodiment of the present invention may include such compiled training sets.

Manipulating the Training Data

The training data compiled in stage1of an embodiment of the present invention is then manipulated in a further stage2. So, reference is now made toFIG. 2, which schematically illustrates steps according to the stage2of manipulating the training data in an embodiment of the present invention.

In an embodiment of the present invention, each age-group of a given training set is allocated an integer i, which extends from 1 to K integers. At block20, a given training set comprising selected images of subjects in a given gender and classified into K given age-groups is selected as an input. At block21, the index allocated to a given age-group i of the given training set is set to i=1.

Generally, the shape of a human face has rigid variations that may be attributed to scale, rotation and translation of the face, for example, and non-rigid variations that may be attributed to the facial expressions pertaining to the face of the subject. So as to improve the accuracy with which age progression of a subject face is performed in different age-groups in an embodiment of the present invention, rigid variations are substantially removed from images in any given training set.

For the condition i≤K at block22, a step of substantially aligning the respective face shapes of the subjects in the given age-group i is performed at block23. In an embodiment of the present invention, such an alignment may be performed with Generalised Procrustes Analysis as described in Cootes et. al in the document, “Statistical models of appearance for computer vision”, an online technical report of which may be generated using http://www.isbe.man.ac.uk/˜bim/refs.html (2001). An embodiment of the present invention is, of course, not limited to the use of such a method to perform such an alignment and, indeed, any other method that may be suitable for performing this task within the context of an embodiment of the present invention may be used. Performance of such alignment in respect of a given age-group i results in rigid variations being substantially removed from respective faces of subjects in that given age-group i; it also facilitates a corresponding mean face shape, hereinafter also referred to as a common frame, to be generated for that given age-group i, this being generally denoted at block23′ inFIG. 2.

Following on to block24, respective face appearances of subjects in the given age-group i are substantially aligned. This may be done by putting respective detected faces of the subjects substantially into correspondence with respect to each other, by warping them into the common frame correspondingly generated for the given age-group i. In an embodiment of the present invention, such warping may be performed using the Piecewise Affine Warp based on the Delaunay triangulation that is described by Dryden et. al in “Statistical shape analysis”, published by Wiley (1998). An embodiment of the present invention is, of course, not limited to the use of such a method to perform such a task and, indeed, any other method that may be suitable within the context of an embodiment of the present invention may be used. In this way, shape-free images are created for respective faces of the subjects in the given age-group i. The shape-free images each have the same dimension di=mi×ni, where miand nirespectively denote a height and a width of each of the warped images.

Denoted at block25is that the aligned respective face shapes of block23and shape-free forms generated at block24by the alignment of face appearances of subjects in a given age-group i are stored as columns in two given matrices, XiS∈R2L×Nand XiA∈Rdi×N, where: XiSis generated in respect of a shape aspect and XiAis generated in respect of an appearance aspect; S includes shape information; A includes appearance information; i is an integer allocated to a given age-group in respect of which a given matrix is formed, and extends from 1 to K integers for a given training set; L is the number of defined landmark coordinates for mapping a detected face, thereby to obtain a corresponding face shape; N is a number of images in each age-group, and diincludes the dimension of a shape-free form corresponding to the face appearance of each subject.

In an embodiment of the present invention, the face of a subject in a given age-group i of a given training set may be expressed as a superposition of two components: an age component including age-related information pertaining to the given age-group i and a common component generally including information on given facial appearance variations pertaining to all the given age-groups i spanning across the given training set. Regarding facial appearance variations, and by way of example, they may be at least one of expression, shape and identity in an embodiment of the present invention.

For an appearance aspect pertaining to a given training set: an age-component is generated in respect of each given age-group i, and a common component is generated in respect of, and is shared by, all given age-groups i of the given training set. For the appearance aspect: the correspondingly generated age-component for a given age-group i is stored in a given age-specific appearance-dictionary, DiA, and the correspondingly generated common component is stored in a given common appearance-dictionary, D0,iA. Matrix XiAgenerated at block25is used for the generation of these age-specific and common appearance-dictionaries, DiA, D0,iA.

For a shape aspect pertaining to a given training set: an age-component is generated in respect of each given age-group i, and a common component is generated in respect of, and is shared by, all given age-groups i of the given training set. For the shape aspect: the correspondingly generated age-component for a given age-group i is stored in a given age-specific shape-dictionary, DiS, and the correspondingly generated common component is stored in a given common shape-dictionary, D0,iS. Matrix XiSgenerated at block25is used for the generation of these age-specific and common shape-dictionaries, DiS, D0,iS.

Given that {XiS, XiA}i=1K, K age-specific dictionaries, {DiS, DiA}i=1Kand one common shape-dictionary {D0,iS, D0,iA} are generated for respective shape and appearance aspects for a given training set. This is generally denoted by reference numerals26′ and26″ onFIG. 2and is done by decomposing the matrices XiSand XiAinto three components according to equation (1) as shown below:
XiA=D0,iA+DiA+EiAi=1, . . .K(1)
XiS=D0,iS+DiS+EiSi=1, . . .K
where: Ei, EiAinclude respective error matrices that are applied when generating the respective shape-dictionaries and appearance dictionaries of equation (1). Such error matrices are applied in order to make an embodiment of the present invention relatively robust to different types of errors that may serve to reduce the accuracy with which progression of a test subject image may be performed in different age-groups i. By way of example, such errors may be, but are not limited to, relatively poor localization of the landmark coordinates L during face mapping, the presence of occlusions and/or illuminations in respect of any image of any given training set.

At block26, an embodiment of the present invention is looped and the steps from block23to25are performed for a next age-group i+=1 of the given training set, thereby to generate corresponding age-specific and common shape-dictionaries DiS, D0,iS, and age-specific and common appearance-dictionaries, DiA, D0,iA, for that next age-group i+=1. Such a loop and perform function may be done until corresponding age-specific and common shape-dictionaries DiS, D0,iS, and age-specific and common appearance-dictionaries, DiA, D0,iA, are generated for all the given age-groups i, of the given training set, where i=1, . . . K.

Testing the Training Data

Reference is now made toFIG. 3, which schematically illustrates a stage3of testing training data in an embodiment according to a method aspect of the present invention.

At block40, for the input of an image Y of a test subject that is to form the basis of age progression in different age-groups i, respective age-specific and common shape-dictionaries, DiS, D0,iS, and respective age-specific and common appearance-dictionaries, DiA, D0,iA, generated for given training sets are called up.

At block41, the face of the test subject is detected from the image Y and mapped according to the defined landmark coordinates L, thereby to yield a corresponding face shape of the test subject. Such mapping is performed as above-described with reference to block11inFIG. 1.

At block42, at least a given training set compiled in respect of a gender corresponding to the test subject gender is accessed. Respective age-specific and common shape-dictionaries, DiS, D0,iS, and respective age-specific and common appearance-dictionaries, DiA, D0,iA, generated for given age-groups i of the accessed training set are also called up.

At block43, the index allocated to a given age-group i of the accessed training set is set to i=1.

For a condition i≤K being registered at block44, a step of the test subject face shape being substantially aligned with the common frame computed for such a given age-group i of the given training set is performed at block45, where i=1, . . . K. Such an alignment may be performed with the Generalised Procrustes Analysis as above-mentioned in the description of manipulation of the training data as performed at block23of stage2in an embodiment of the present invention. Of course, an embodiment of the present invention is not limited to the use of such a method to perform such an alignment and, indeed, any other method that may be suitable for performing this task within the context of an embodiment of the present invention may be used.

Following on to block46, the face appearance of the test subject is aligned with the common frame computed for the given age-group i of the given training set as hereinbefore mentioned with respect to block44. This is done by putting the detected face of the test subject substantially in correspondence with the common frame generated in respect of the given age-group i, by warping it into the common frame. Such warping may be performed using the Piecewise Affine Warp based on the Delaunay triangulation as above-mentioned in the description of manipulation of the training data as performed at block24of stage2in an embodiment of the present invention. Of course, an embodiment of the present invention is not limited to the use of such a method to perform such a task and, indeed, any other method that may be suitable within the context of an embodiment of the present invention may be used.

At block47, respective progression of the aligned test subject face shape of block45and aligned test subject face appearance of block46is performed in the given age-group i of the accessed training set.

At block47, progression of the aligned test subject face shape in the given age-group i of the accessed training set is performed by generating respective coding parameters ci,2S, ci,1S, for the age-specific shape-dictionary DiSand common shape-dictionary D0,iS, which have been generated in respect of that given age-group i, by solving the following equation (2) below:

At block47, progression of the aligned test subject face appearance in the given age-group i of the accessed training set is performed by generating respective coding parameters ci,2A, ci,1A, for the age-specific appearance-dictionary DiAand common appearance-dictionary D0,iA, which have been generated in respect of that given age-group i, by solving the following equation (3) below:

minci,1A,ci,2A⁢λc⁡(ci,1A22+ci,2A22)+λe⁡(ei1)(3)
subject to the constraint that:
yiA=D0,iAci,1A+DiAci,2A+ei
where: A is an appearance aspect; I is an integer allocated to a given age-group of the accessed training set and extends from 1 to K integers for the accessed training set; λcand λeare respective weighting parameters; ∥·∥1, ∥·∥2respectively comprise are l1and l2norms and are respectively defined for a vector x as ∥x∥1=Σi|xi| and ∥x∥2=√{square root over (Σi|xi|2)}, eiis a sparse vector accounting for non-Gaussian sparse errors; ci,1A, ci,2Aare coding parameters for respective allocation to the common appearance-dictionary D0,iAand given age-specific appearance-dictionaries DiA, and yiAis the aligned face appearance of the test subject into a given common frame computed in respect of a given age-group i of the accessed training set.

It can be seen from equations (2) and (3) that the progressed version of a test subject face includes the summation of: a linear combination of columns of a given age-specific dictionary, DiS, DiA, a linear combination of columns of the corresponding common dictionary, D0,iS, D0,iAand a sparse vector eiaccounting for non-Gaussian sparse errors.

The progressed appearance and the progressed shape of the test subject face in the given age-group i of the accessed training set may be respectively generated in accordance with equation (4) below:
{progressed appearance}i=D0,iAci,1A+DiAci,2A
{progressed shape}i=D0,iSci,1S+DiSci,2S(4)
where: ci,1S, ci,2Sare coding parameters for respective allocation to the common shape-dictionary D0,iSand given age-specific shape-dictionaries DiS, and ci,1A, ci,2Aare coding parameters for respective allocation to the common appearance-dictionary D0,iAand given age-specific appearance-dictionaries DiA.

At block48, the progressed appearance and the progressed shape of the test subject as defined in equation (4), which are generated in respect of the given age-group i of the accessed training set, are substantially combined. In an embodiment of the present invention, this may be performed by substantially warping the progressed appearance into the progressed face shape. Such warping may be performed, as above-described, with the Piecewise Affine Warp.

At block49, an embodiment of the present invention is looped and the steps from block45to48are performed for a next age-group i+=1 of the given training set, thereby returning progressed images of the test subject for that next age-group i+=1. Such a loop and perform function may be done until progressed images are collected for all of the K age-groups of the given training set and they are returned/stored, this being denoted at block48′.

Note from the above description that provided herein, in one or aspects, is a computer-implemented method of facilitated age progression of a test subject facial image. The computer-implemented method includes: compiling training data, the training data including at least one training set including selected images of subjects in a corresponding gender in classified into given age groups; and manipulating the training data. Manipulating the training data includes: for a given age group of a given training set, substantially aligning respective face shapes of the subjects; computing a common frame based on the aligned shapes; substantially aligning respective face appearances of subjects, thereby generating a shape-free form corresponding to the face appearance of each subject, using the substantially aligned shapes to generate, in respect of a shape aspect, in age specific shape dictionary for each given age group, and a common shape dictionary for all the given age groups, of the given training set, and using the substantially aligned appearances to generate, in respect of an appearance aspect, at least an age specific appearance dictionary for each given age group, and a common appearance dictionary for all the given age groups, of the given training set. Together the age specific appearance dictionary for each given age group, and the common appearance dictionary facilitate age progression of the test subject facial image.

In one or more implementations, compiling the training data may include: accessing images of different subjects; mapping a respective detected face in each of the accessed images according to defined landmark coordinates and obtaining a face shape corresponding to each detected face; selecting accessed images according to at least a given criterion, and compiling, in respect of a given gender, the at least one training set.

In or more embodiments, the computer-implemented method may further include testing the training data. The testing may include: accessing at least a training set substantially corresponding in gender to a detected gender of the test subject; accessing given age specific dictionaries and common dictionaries, which have been generated for respective shape and appearance aspects, in respect of the given age groups of the accessed training set; mapping a detected face of the test subject according to the defined landmark coordinates to obtain a corresponding test subject face shape; substantially aligning the test subject face shape with a common frame respectively computed for each given age group of the accessed training set; substantially aligning at least a face appearance of the test subject with the respective common frame computed in respect to each given age group of the accessed training set; progressing the aligned test subject face shape in at least a selectable age group of the accessed training set using a corresponding age specific shape dictionary and common shape dictionary; progressing the aligned test subject face appearance in at least the selected age group of the accessed training set using a corresponding age specific appearance dictionary and common appearance dictionary, in substantially combing at least a progressed face shape and progressed face appearance of the test subject in the selective age group of the accessed training set.

Aligning an appearance of a given subject into a given common frame may be performed by substantially warping the detected face of the subject into the given common frame. In this way, alignment of an appearance of a given subject into a given common frame may be performed without the need for use and/or development of relatively complex processing resources. This feature may extend the advantages of ease of use and relatively efficient allocation of processing resources to an embodiment of the present invention.

Manipulating the training data may further include generating for a given training set, at least an age-specific dictionary for a given age-group and common dictionary for all given age-groups, in respect of each of a shape aspect and an appearance aspect, by applying a corresponding error aspect. In an embodiment of the present invention, error aspects are applied in the generation of the given age-specific dictionaries and common dictionaries. This may serve to make an embodiment of the present invention relatively robust to different types of errors that may serve to reduce the accuracy with which progression of a test subject image may be performed in different age-groups. By way of example, such errors may be, but are not limited to, relatively poor localization of the landmark coordinates during face mapping, the presence of occlusions and/or illuminations in respect of any image of any given training set.

More particularly, manipulating the training data may include generating at least two matrices, XiS∈ R2L×Nand XiA∈ Rdi×Nfor respective shape and appearance aspects by using the aligned respective face shapes and aligned face appearances of subjects, where: S includes shape information; A includes appearance information; i is an integer allocated to a given age-group in respect of which a given matrix is formed, and extends from 1 to K integers for a given training set; L is the number of defined landmark coordinates for mapping a detected face, thereby to obtain a corresponding face shape; N is a number of images in each age-group, and diis a dimension of a shape-free form corresponding to the face appearance of each subject, and performing a decomposition of the matrices XiSand XiA, respective to a shape aspect and appearance aspect, into at least three components so as to correspondingly generate, in respect of each aspect, at least an age-specific dictionary for each given age-group, and a common dictionary for all the given age-groups, of a given training set:
XiA=D0,iA+DiA+EiAi=1, . . . K
XiS=D0,iS+DiS+EiSi=1, . . . K
where: D0,iA, DiA, respectively are a common appearance-dictionary and a given age-specific appearance-dictionary that are generated in respect of an appearance aspect A for the given training set, D0,iS, DiS, respectively are a common shape-dictionary and a given age-specific shape-dictionary that are generated in respect of a shape aspect S for the given training set, and EiS, EiAare error matrices generated respectively for the shape aspect S and appearance aspect A. In an embodiment of the present invention, given age-specific dictionaries and common dictionaries are generated in respect of one of a shape aspect or an appearance aspect by performing a decomposition of corresponding matrices generated in respect of any one of these aspects. This feature extends the advantage of relative ease of generation of the training data forming a basis of age progression to an embodiment of the present invention.

Testing the training data may include: progressing the test subject face and face appearance in a given age-group of the accessed training set by solving a respective coding problem for each of a shape aspect and an appearance aspect in respect of the given age-group. A given optimization problem may be solved in order to respectively progress the face shape and face appearance of a given test subject in an embodiment of the present invention. This may extend the advantages of increased efficiency and reduced complexity with which age progression may be performed in an embodiment of the present invention.

More particularly, testing the training data may include progressing the aligned test subject face shape in a given age-group of the accessed training set by generating respective coding parameters for the age-specific shape-dictionary and common shape-dictionary, which have been generated in respect of the given age-group, by solving:

In one or more implementations, testing the training data may include progressing the aligned test subject face appearance in a given age-group of the accessed training set by generating respective coding parameters for the age-specific appearance-dictionary and common appearance-dictionary, which have been generated in respect of the given age-group, by solving:

minci,1A,ci,2A⁢λc⁡(ci,1A22+ci,2A22)+λe⁡(ei1)
subject to the constraint that:
yiA=D0,iAci,1A+DiAci,2A+ei
where: A is an appearance aspect; i is an integer allocated to a given age-group of the accessed training set and extends from 1 to K integers for the accessed training set; λcand λeare respective weighting parameters; ∥·∥1, ∥·∥2respectively are the l1and l2norms and are respectively defined for a vector x as ∥x∥1=Σi|xi| and ∥x∥2=√{square root over (Σi|xi|2)}, eiare a sparse vector accounting for non-Gaussian sparse errors; ci,1A, ci,2Aare coding parameters for respective allocation to the common appearance-dictionary D0,iAand given age-specific appearance-dictionaries DiA, and yiAare the aligned face appearance of the test subject into a given common frame computed in respect of a given age-group i of the accessed training set. The progressed face appearance of the test subject is generated on a basis of the summation of: a linear combination of columns of a given age-specific appearance dictionary, DiA, a linear combination of columns of the corresponding common appearance dictionary, D0,iA, and a sparse vector eiaccounting for non-Gaussian sparse errors. This feature may extend the advantages of ease of age progression of a given test subject in a given age-group since such summation may be performed with relative ease and by using relatively fewer technical and/or processing resources.

In one or more embodiments, the age-specific dictionary generated for an age-group of a given training set may include age-related information of subjects in that age-group. An age-specific dictionary may be compiled for each given age-group in each given training set in an embodiment of the present invention. Because such age-specific dictionaries are compiled using age-related information on multiple, different subjects possessing a variety of age-related characteristics, the advantages of improved accuracy and versatility may be extended to an embodiment of the present invention since age progression of a test subject is performed using such age-specific dictionaries.

In one or more embodiments, the common dictionary generated in respect of a given training set may include information on at least a given facial appearance variation of subjects in all the age-groups of that given training set. In an embodiment of the present invention, a common dictionary is compiled in respect of each training set, for all given age-groups thereof. The common dictionaries are compiled using facial appearance variations recorded for multiple, different subjects. Such common dictionaries form a basis of age progression being performed in respect of a given test subject in an embodiment of the present invention. Thus, this feature may further reinforce the advantages of improved accuracy and versatility with which age progression is performed in an embodiment of the present invention.

In one or more implementations, testing the training data includes: respectively generating the progressed face appearance and the progressed face shape of the test subject in a given age-group of the accessed training set as:
{progressed appearance}i=D0,iAci,1A+DiAci,2A
{progressed shape}i=D0,iSci,1S+DiSci,2S
where: A and S are respective appearance and shape aspects, i is an integer allocated to a given age-group of the accessed training set and extends from 1 to K integers, D0,iSis the common shape-dictionary generated in respect of any given age-group i of the accessed training set; DiSis a given age-specific shape-dictionary generated in respect of a given age-group i of the accessed training set; ci,1S, ci,2Sare coding parameters for respective allocation to the common shape-dictionary D0,iSand given age-specific shape-dictionaries DiS; D0,iAare the common appearance-dictionary generated in respect of any given age-group i of the accessed training set; DiAis a given age-specific appearance-dictionary generated in respect of a given age-group i of the accessed training set, and ci,1A, ci,2Aare coding parameters for respective allocation to the common appearance-dictionary D0,iAand given age-specific appearance-dictionaries DiA. The progressed face appearance and the progressed face shape of the test subject are respectively generated in an embodiment of the present invention. This presents the opportunity for individual analysis of the respective progressions, which may be desirable in certain scenarios. Such individual analysis may include, for example, further processing of either or both of the respective progressions and/or viewing the corresponding visual depictions of the respective progressions. This feature may serve to extend the advantages of versatility and ease of use to an embodiment of the present invention.

Testing the training data may also include: substantially combining the progressed face shape and the progressed face appearance of the test subject in a selected age-group of the accessed training set by substantially warping the progressed face appearance into the progressed face shape. Information on the facial ageing of a subject pertains to a combination which includes the face shape and face appearance of the subject rather than individual consideration of these features. In an embodiment of the present invention, a combination of the progressed face shape and the progressed face appearance as obtained for a test subject in a selected age-group is performed. An advantage associated with this feature is that relatively accurate and realistic information may be obtained on an expected age progression of the test subject. Also, the corresponding visual depiction of the combined progressed face shape and the progressed face appearance provides ease of view of the expected age progression of the subject.

In one or more embodiments, compiling the training data may include determining a pose of a given subject from a corresponding mapped face of the subject. Age progression according to an embodiment of the present invention may be performed on the basis of training data which includes a collection of multiple images of subjects of different ages, genders, ethnicities, and so forth, which are accessed from a database. So that age progression may be performed with increased accuracy and/or efficiency, selected images from the images accessed from the database are used. Such a selection is performed according to given criteria of such accessed images. Whether an accessed image fulfils such given criteria may be determined from the pose of the subject of that image, which is accordingly determined in an embodiment of the present invention.

Further, in one or more implementations, compiling the training data includes performing a selection of the accessed images by removing those accessed images, including at least a non-frontal aspect and at least an occlusion. Age progression according to an embodiment of the present invention may be performed on the basis of training data comprising a collection of multiple images of subjects of different ages, genders, ethnicities, and so forth, which are accessed from a database. So that age progression may be performed with increased accuracy and/or efficiency, selected images fulfilling given criteria are used from the images accessed from the database. Such given criteria include a lack of a non-frontal face aspect and an occlusion. In this way, the accuracy and efficiency with which age progression is performed in an embodiment of the present invention is further improved.

The present invention has been described purely by way of example and modifications of detail may be made within the scope of the invention.

Exemplary embodiments of computing environments to implement one or more aspects of the present invention are described below with reference toFIGS. 4-6.

By way of further example,FIG. 4depicts one embodiment of a computing environment400, which includes a computing system412. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system412include, but are not limited to, a personal computer, a workstation, a handheld or laptop computer or device, a mobile phone, a programmable consumer electronic device, a tablet, a personal digital assistant (PDA), and the like.

Computing system412may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types.

As depicted inFIG. 4, computing system412, is shown in the form of a general-purpose computing device. The components of computing system412may include, but are not limited to, one or more processors or processing units416, a system memory423, and a bus418that couples various system components including system memory423to processor416.

In one embodiment, processor416may be based on the z/Architecture® offered by International Business Machines Corporation, or other architectures offered by International Business Machines Corporation or other companies. z/Architecture® is a registered trademark of International Business Machines Corporation, Armonk, N.Y., USA. One embodiment of the z/Architecture® is described in “z/Architecture® Principles of Operation,” IBM Publication No. SA22-7832-10, March 2015, which is hereby incorporated herein by reference in its entirety.

In other examples, it may be based on other architectures, such as the Power Architecture offered by International Business Machines Corporation. One embodiment of the Power Architecture is described in “Power ISA™ Version 2.07B,” International Business Machines Corporation, Apr. 9, 2015, which is hereby incorporated herein by reference in its entirety. POWER ARCHITECTURE is a registered trademark of International Business Machines Corporation, Armonk, N.Y., USA. Other names used herein may be registered trademarks, trademarks, or product names of International Business Machines Corporation or other companies.

Computing system412may include a variety of computer system readable media. Such media may be any available media that is accessible by computing system412, and it includes both volatile and non-volatile media, removable and non-removable media.

Program/utility440, having a set (at least one) of program modules442, may be stored in memory432by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules442generally carry out the functions and/or methodologies of embodiments of the invention as described herein. Alternatively, a separate, processing unit reassignment system, module, logic, etc.,401may be provided within computing environment412.

Computing system412may also communicate with one or more external devices414such as a keyboard, a pointing device, a display424, etc.; one or more devices that enable a user to interact with computing system412; and/or any devices (e.g., network card, modem, etc.) that enable computing system412to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces422. Still yet, computing system412can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter420. As depicted, network adapter420communicates with the other components of computing system,412, via bus418. It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computing system412. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

One or more aspects may relate to or use cloud computing.

Characteristics are as Follows:

Service Models are as Follows:

Deployment Models are as Follows:

A cloud computing node may include a computer system/server, such as the one depicted inFIG. 4. Computer system/server412ofFIG. 4may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. Computer system/server412is capable of being implemented and/or performing any of the functionality set forth hereinabove.

Referring now toFIG. 5, illustrative cloud computing environment210is depicted. As shown, cloud computing environment210comprises one or more cloud computing nodes200with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone220A, desktop computer220B, laptop computer220C, and/or automobile computer system220N may communicate. Nodes200may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment210to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices220A-N shown inFIG. 5are intended to be illustrative only and that computing nodes200and cloud computing environment210can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring toFIG. 6, a set of functional abstraction layers provided by cloud computing environment210is shown. It should be understood in advance that the components, layers, and functions shown inFIG. 6are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: