Can 3D Shape of the Face Reveal your Age?

Baiqiang Xia

1,3

, Boulbaba Ben Amor

2,3

, Mohamed Daoudi

2,3

and Hassen Drira

2,3

University Lille1, Lille, France

Institut Mines-Telecom/Telecom Lille, Lille, France

LIFL (UMR 8022 Lille 1/CNRS), Lille, France

Keywords:

Age Estimation, 3D Face, Dense Scalar Field, Random Forest Regression.

Abstract:

Age reﬂects the continuous accumulation of durable effects from the past since birth. Human faces deform

with time non-inversely and thus contains their aging information. In addition to its richness with anatomy

information, 3D shape of faces could have the advantage of less dependent on pose and independent of il-

lumination, while it hasn’t been noticed in literature. Thus, in this work we investigate the age estimation

problem from 3D shape of the face. With several descriptions grounding on Riemannian shape analysis of fa-

cial curves, we ﬁrst extracted features from ideas of face Averageness, face Symmetry, its shape variations with

Spatial and Gradient descriptors. Then, using the Random Forest-based Regression, experiments are carried

out following the Leaving-One-Person-Out (LOPO) protocol on the FRGCv2 dataset. The proposed approach

performs with a Mean Absolute Error (MAE) of 3.29 years using a gender-general test protocol. Finally, with

the gender-speciﬁc experiments, which ﬁrst separate the 3D scans into Female and Male subsets, then train

and test on each gender speciﬁc subset in LOPO fashion, we improves the MAE to 3.15 years, which conﬁrms

the idea that the aging effect differs with gender.

1 INTRODUCTION

Face age estimation performs important social roles

in human-to-human communication. Studies in cog-

nitive psychology, presented as a review by (Rhodes,

2009), have discovered that human beings develop the

ability of face age estimation naturally in early life,

and can be fairly accurate in deciding the age or age

group with a given face. These studies, based on sub-

jective age estimation given to face image from hu-

man participants, have also found that multiple cues

contribute to age estimation, including the holistic

face features (like the outline of the face, face shape

and texture, etc.), local face features (like the eyes,

nose, the forehead, etc.) and their conﬁguration (like

the bilateral symmetry of the face (Clinton S. Mor-

rison, 2011)). The aging process is a cumulative,

uncontrollable and personalized slow process, inﬂu-

enced by intrinsic factors like the gene and gender,

and extrinsic factors like lifestyle, expression, envi-

ronment and sociality (Fu et al., 2010; Han et al.,

2013). The appearance and anatomy of human faces

changes remarkably with the progress of aging (Lani-

tis et al., 2002). The general pattern of the aging

process differs in faces of different person (person-

alized or identity-speciﬁc), in faces of different age

(age-speciﬁc), in faces of different gender (gender-

speciﬁc), and in different facial components (Fu et al.,

2010; Rhodes, 2009; Guo et al., 2009; Park et al.,

2010; Guo et al., 2008b). Typically, the craniofacial

growth (bone movement and growth) takes place dur-

ing childhood, and stops around the age of 20, which

leads to the re-sizing and re-distribution of facial re-

gions, such as the forehead, eyes, nose, cheeks, lips,

and the chin. From adulthood to old age, face changes

mainly in the skin, such as the color changes (usually

darker and with more color changes) and the texture

changes (appearance of wrinkles). The shape changes

of faces continues from adulthood to old age. With the

droops and sags of facial muscle and skin, the faces

are tend to be more a shape of trapezoid or rectangle

in old faces, while the typical adult faces are more of

a U-shaped or upside-down-triangle (Rhodes, 2009).

Automatic face age estimation is to label a face

image with the exact age or age group objectively

by machine. With the rapid advances in com-

puter vision and machine learning, recently, au-

tomatic face age estimation have become partic-

ularly prevalent because of its explosive emerg-

ing and promising real-world applications, such as

Xia B., Ben Amor B., Daoudi M. and Drira H..

Can 3D Shape of the Face Reveal your Age?.

DOI: 10.5220/0004652300050013

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 5-13

ISBN: 978-989-758-004-8

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

electronic customer relationship management, age-

speciﬁc human-computer-interaction, age-speciﬁc ac-

cess control and surveillance, law enforcement (e.g.,

detecting child-pornography, forensic), biometrics

(e.g., age-invariant person identiﬁcation (Park et al.,

2010)), entertainment (e.g., cartoon ﬁlm production,

automatic album management), and cosmetology.

Compared with human age estimation, automatic age

estimation yields better performance as demonstrated

in (Han et al., 2013). The performance of age estima-

tion is typically measured by the mean absolute error

(MAE) and the cumulative score (CS). The MAE is

deﬁned as the average of the absolute errors between

the estimated age and the ground truth age, while the

CS, proposed ﬁrstly by (Geng et al., 2007) in age es-

timation, shows the percentage of cases among the

test set where the absolute age estimation error is less

than a threshold. The CS measure is regarded as a

more representative measure in relation with the per-

formance of an age estimator (Lanitis, 2010).

As pointed in (Rhodes, 2009; Ramanathan et al.,

2009), the earliest age estimation works used the

mathematical cardioidal strain model, derived from

face anthropometry that measures directly the sizes

and proportions in human face, to describe the cran-

iofacial growth. These approaches are useful for

young ages, but not appropriate for adults. After this,

abundant works exploiting 2D images have been pub-

lished in the literature with more complex approaches.

Different with the comprehensive surveys given by

(Rhodes, 2009; Ramanathan et al., 2009), which cate-

gorized the literature concerning different aging mod-

eling techniques, we represented the literature with

the different ideas underlying these technical solu-

tions. Based on the previous statements, we describe

the face appearance as a function of multiple factors,

including the age, the intrinsic factors (permanent fac-

tors like gene, gender, ethnicity, identity, etc.), and

the extrinsic factors (temporary factors like lifestyle,

health, sociality, expression, pose, illumination, etc.).

A. General Aging Patterns in Face Appearance.

Essentially, face age estimation is to estimate the

age of a subject by the aging patterns shown visu-

ally in the appearance. To analyze the appearance

given in the face image is the basic ways to esti-

mate the age. In the literature of age estimation,

works were carried out with several different per-

ceptions of the general aging patterns in face ap-

pearance. As aging exhibits similar patterns among

different person, several approaches have been de-

signed to learn the general public-level aging pat-

terns in face appearance for age estimation. The

most representative ones are the Active-Appearance-

Model (AAM) based approaches, the manifold em-

bedding approaches, and the Biologically-Inspired-

Feature (BIF) based approaches. The common idea

underlying these approaches is to project a face (lin-

early or non-linearly) into a subspace, to have a low

dimensional representation. Respectively, (i) (Lanitis

et al., 2002; Lanitis et al., 2004) use an Active Ap-

pearance Model (AAM) based scheme for projecting

face images linearly into a low dimensional space.

The AAM was initially proposed by (Cootes et al.,

1998), in which each face is represented by its shape

and texture deviations to the mean face with a set

of model parameters. Age estimation results with a

quadratic regressor showed that the generic aging pat-

terns work well for age estimation. Moreover,(Lanitis

et al., 2004) illustrated that different face parameters

obtained from training are responsible for different

changes in lighting, pose, expression, and individual

appearance. Considering that these parameters work

well for age estimation, we can conclude that these

face co-variants are inﬂuential in age estimation. (ii)

The goal of manifold embedding approaches is to em-

bed the original high dimensional face data in a lower-

dimensional subspace by linear or non-linear projec-

tion, and take the embedding parameters as face rep-

resentation. In the work of (Guo et al., 2008b; Guo

et al., 2008a), the authors extracted age related fea-

tures from 2D images with a linear manifold embed-

ding method, named Orthogonal Locality Preserving

Projections (OLPP). (Li et al., 2012) learned age man-

ifold with both local preserving requirements and or-

dinal requirements to enhance age estimation perfor-

mance (Wu et al., 2012) projected each face as a point

on the Grassmann Manifold with the standard SVD

method, then the tangent vector on these points of

the manifold were taken as features for age estima-

tion. (iii) Inspired by a feed-forward path theory in

cortex for visual processing, (Guo et al., 2009) intro-

duced the biologically inspired features (BIF) for face

age estimation. After ﬁltering an image with a Gabor

ﬁlter and a standard deviation based ﬁlter consecu-

tively, the obtained features are processed with PCA

to generate lower-dimension BIF features. The results

demonstrated the effectiveness and robustness of bio-

inspired features in encoding the generic aging pat-

terns. Beyond the public-level aging patterns, there

could be some less generic aging patterns when deal-

ing with a subset of faces, such as a group of faces

with high similarity, or a temporal sequence of face

images for the same person. Based on the observation

that similar faces tend to age similarly, (Lanitis et al.,

2004; Lanitis et al., 2002) presented an appearance-

speciﬁc strategy for age estimation. Faces are ﬁrstly

clustered into groups considering their inter similar-

ity, then training is performed on each group sepa-

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

rately to learn a set of appearance-speciﬁc age estima-

tors. Given a previously unseen face, the ﬁrst step is to

assign it to the most appropriate group, then the cor-

responding age estimator makes the age estimation.

Experimental results showed that the group-level ag-

ing patterns are more accurate in age estimation com-

pared with the generic-aging patterns. In case there

is no similar enough face image for a testing face im-

age in the database, (Lanitis et al., 2002) presented

a weighted-appearance-speciﬁc approach which also

yields ﬁne performance. As different individual ages

differently, (Geng et al., 2006; Geng et al., 2007)

proposed the Aging-Pattern-Subspace (AGES), which

studies the individual-level aging patterns from a tem-

poral sequence of images of an individual ordered by

time. For a test face, the aging pattern and the age

is determined by the projection in the subspace that

has the least reconstruction error. Experiments con-

ﬁrm that individual aging patterns contributes to age

estimation. As different face components age differ-

ently, the component-level aging patterns are stud-

ied for age estimation. (Suo et al., 2010) represented

faces with a hierarchical And-Or Graph. Face aging

is then modeled as a Markov process on the graphs

and the learned parameters of the model are used for

age estimation. They found that the forehead and eye

regions are the most informative for age estimation,

which is also supported by discoveries of (Han et al.,

2013) using the BIF features.

B. Considering the Intrinsic/Extrinsic Factors in

Facial Aging. As stated at the beginning of this intro-

duction, the appearance of face is inﬂuenced by intrin-

sic factors like the gene, gender, and extrinsic factors

like lifestyle, expressions, environment and sociality

(Fu et al., 2010; Han et al., 2013). Several studies

have given consideration of the inﬂuences of these

factors in age estimation with enhanced age estima-

tion performance reported. Speciﬁcally, thinking that

faces age differently in different age, age-speciﬁc ap-

proaches are adopted by (Lanitis et al., 2004), where

age estimation is obtained by using a global age clas-

siﬁer ﬁrst, then adjusted the estimated age by a local

classiﬁer which operates within a speciﬁc age range.

Similarly, (Guo et al., 2008b; Guo et al., 2008a) pro-

posed a Locally Adjusted Robust Regressor (LARR)

for age estimation, which begins with a SVR-based

global age regression, then followed by a local SVM-

based classiﬁcation that adjusts the age estimation

in a local age range. All of these age-speciﬁc ap-

proaches have achieved better performance compared

with their corresponding approaches without local ad-

justment. Considering that different gender ages dif-

ferently with age (Ramanathan et al., 2009; Guo et al.,

2008a), (Ueki et al., 2010; Ramanathan et al., 2009;

Guo et al., 2008a; Lakshmiprabha et al., 2011) carried

out age estimation on male and female groups sep-

arately. Considering the individual lifestyle, (Lani-

tis et al., 2002) encoded this information together

with facial appearance in age estimation, and demon-

strated that the importance of lifestyle in determin-

ing the most appropriate aging function of a new in-

dividual. (Ueki et al., 2010) gave weights to dif-

ferent lighting conditions for illumination-robust face

age estimation. (Li et al., 2012) gave consideration

of the feature redundancy and used feature selection

to enhance age estimation. As stated before, in the

childhood, face deformation mainly takes the form of

craniofacial growth with facial features re-sized and

re-distributed. From adulthood to old age, with the

droops and sags of facial muscle and skin, the old

faces usually deform to a trapezoid or rectangle shape

from a typically U-shaped or upside-down-triangle in

adult face (Rhodes, 2009). Another signiﬁcant shape

deformation is the introduction of facial wrinkles with

aging. While, given the fact that face shape deforms

signiﬁcantly with age in three dimensions, and given

the robustness of 3D face scans to illumination and

poses compared with 2D face images, all the previous

works in the literature used 2D face datasets for age

estimation, no work has been done concerning the 3D

face. Thus, in this work, we introduce the investiga-

tion of age estimation with 3D face scans. The rest

of the paper is organized as follows: in section 2, we

present an overview of our methodology and summa-

rize the main contributions; in section 3, we explain

our methodology of features extraction from the 3D

faces based on an Riemann framework; in section 4,

we detail the regression strategy for age estimation

using Random Forest; experimental results and their

discussion are presented in section 5 while section 6

comes to the conclusion of this work.

2 METHODOLOGY AND

CONTRIBUTION

From the analysis above, it emerges that most of the

existing works study age estimation with aging pat-

terns chosen at a speciﬁed level and some aging fac-

tors enrolled for enhancement. As far as we concern,

all these works are based on 2D images, no work con-

cerning 3D face scans has been attached to age es-

timation. Thus, we introduce in the present work a

new study of 3D-based face age estimation to the do-

main. In our approach, we consider the public-level

aging patterns and gender factor for age estimation.

First, we extract four types of Dense Scalar Field

(DSF) features from each pre-processed face, namely

Can3DShapeoftheFaceRevealyourAge?

the Average DSF, the Symmetry DSF, the Spatial DSF

and the Gradient DSF. These DSFs are derived from

different face perception ideas and their computation

is grounding on Riemannian shape analysis of facial

curves. Then we perform age estimation using Ran-

dom Forest Regression on each type of DSFs with

two protocols: one experiment on DSFs of the whole

dataset directly and the other experiments on male

and female DSFs separately. We have also designed a

simple result-level fusion with different type of the

DSFs, to see if the performance improves with all

these face perception ideas combined.

In summary, the main contributions of this work

are as follows. First, as far as we know, this is the

ﬁrst work in 3D-based age estimation. Although 3D

face growth has been notice for a long time (Mark

and Todd, 1983; Bruce et al., 1989), no work has

been reported to 3D face age estimation. Secondly,

in this work, we introduce four different perspectives

of faces perception for face representation. With the

Dense Scalar Field features, we have obtained signiﬁ-

cant accuracy with each of the perspectives, compared

with typical 2D-based age estimation performance.

Last but not the least, we have enhanced the age esti-

mation performance by experimenting on the scans of

each gender separately, which conﬁrms that the sex-

ual dimorphism exists in terms of face aging patterns.

We have also enhanced the performance by a simple

late fusion rule of the four descriptors.

3 FEATURE EXTRACTION

As mentioned earlier, we adopt the Dense Scalar Field

features in our approach. Based on pair-wise shape

comparison of curves, the Dense Scalar Field (DSF)

grounding on Riemannian shape analysis (Drira et al.,

2012) (Drira et al., 2013) is capable for capturing the

local shape deformation between facial curves. For-

mally, for any curve in the space of R

, β: I → R

where I = [0,1], it is ﬁrst represented mathematically

by the square-root velocity function q(t), according

to: q(t) =

β(t)

√

β(t)k

(Srivastava et al., 2011). With the

norm k·k scaled to 1, the space of such functions:

C = {q : I → R

,kqk = 1} ⊂ L

(I,R

) becomes a

Riemannian manifold with the L

metric on its tan-

gent spaces. Since kqk= 1, C is a also a Hypersphere

in the Hilbert space L

(I,R

). Given two curves

and β

, they are ﬁrst represented by the square-

root velocity function, then uniﬁed to q

and q

with

kqk = 1. The geodesic path ψ

∗

between q

on the

manifold C is given by the minor arc of great circle

connecting them on this Hypersphere, ψ

∗

: [0,1] → C

given by (1),

∗

(τ) =

sin(θ)

(sin((1 −τ)θ)q

+ sin(θτ)q

) (1)

where θ = d

) = cos

−1

(

) is the angle

between q

and q

. The tangent vector ﬁeld on this

geodesic

∗

: [0,1] → T

(C ) is then given by (2):

∗

dψ

∗

dτ

−θ

sin(θ)

(cos((1 −τ)θ)q

−cos(θτ)q

)

(2)

Knowing that on a geodesic path, the covariant deriva-

tive of its tangent vector ﬁeld is equal to 0. Thus,

∗

τ=0

is sufﬁcient to represent this vector ﬁeld. Ac-

cordingly, (2) becomes:

∗

τ=0

sin(θ)

−cos(θ)q

) (3)

With the magnitude of

∗

at each all the N indexed

points of the curve, we build a Dense Scalar Field

(DSF) , V = {k

∗

(τ=0)

(k)k, k = 1,2,3,..,N}, which

quantiﬁes the shape difference between two curves.

In our approach, the raw 3D face scans are ﬁrst

pre-processed for hole-ﬁlling, cropping, smoothing

and pose normalization, and then represented by a

set of parameterized radial curves emanating from

the nose tip of the preprocessed face denoted with S.

The radial curve that makes an clockwise angle of α

with the radial curve which passes through the fore-

head (β

) is denoted as β

, and the neighbor curve

of β

that has an angle increase of ∆α is denoted as

α+∆α

. Such representation can be seen as a approx-

imation of the preprocessed face S. To extract the

DSF features, one need to ﬁrst deﬁne the correspon-

dence of curves in pair-wise shape comparison. With

four different perspectives from face perception, we

deﬁne four different types of correspondence in pair-

wise shape comparison, which results into four dif-

ferent types of DSF features with all the radial curves

considered in a face, namely the Symmetry DSF, the

Averageness DSF, the Spatial DSF and the Gradient

DSF. Figure 1 gives an illustration of these DSF fea-

tures. The Symmetry DSF shown in sub-ﬁgure (a)

captures the deformation between a pair of bilateral

symmetrical radial curves (β

and β

2π−α

) in a pre-

processed face S. The Symmetry DSF conveys the

idea that the bilateral facial symmetry loses with age.

The Averageness DSF shown in sub-ﬁgure (b) com-

pares a pair of curves with the same angle index from

a preprocessed face β

and an average face template

. The average face template T (as presented in sub-

ﬁgure (b)) is deﬁned as the middle point of geodesic

deformation path from a representative male scan to

a representative female scan. The Averageness DSF

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

(a)

Template Face

(b)

(c)

(d)

Figure 1: Illustrations of different DSFs on preprocessed face S. (a) Symmetry DSF: the DSF from radial curve β

to its

bilateral symmetrical curve β

2π−α

; (b) Averageness DSF: DSF from radial curve β

in a preprocessed face to radial curve β

in an average face template (with the same angle index α); (c) Spatial DSF: DSF from radial curve β

to the middle radial

curve β

in the forehead; (d) Gradient DSF: DSF from radial curve β

to its neighbor curve β

α+∆α

represents the idea that faces become more person-

alized and thus deviates more from the average face

shape with age. The Spatial DSF shown in sub-ﬁgure

to one ref-

erence radial curve β

in the forehead in a prepro-

cessed face S. As β

is the most rigid curve in the

face, the Spatial DSF can be perceived as the cumula-

tive deformation from the most rigid part of the face.

The Gradient DSF shown in sub-ﬁgure (d) captures

the deformation between a pair of neighbor curves

(β

and β

α+∆α

) in a preprocessed face S. In contrast

with the Spatial DSF, the Gradient DSF can be viewed

as a representation of local deformation on the face.

In each sub-ﬁgure of Figure 1, the left part shows the

extracted radial curves in the face and correspondence

for curve comparison, the right part shows the cor-

responding DSF features as color-map on the face,

where on each face point, the hotter the color, the

lower of the DSF magnitude.

4 RANDOM FOREST

REGRESSION

Age estimation can be considered as a classiﬁcation

problem, when each age is taken as a class label. On

the other hand, age estimation can also be considered

as a regression problem, since the age could be inter-

preted as continuous value. Note that there are only

15 subjects of more than 40 years old in FRGCv2,

the number of faces is too small to train classiﬁers

for those ages. Thus, in our approach, we take the

age estimation as a regression problem. Similar rea-

son has been used by (Guo et al., 2009) for choosing

the regression strategy for age estimation on the FG-

net dataset, where the images from old subjects are

also rare. As summarized by (Criminisi and Shotton,

2013), the regression task is, given a labeled set of

training data, learning a general mapping which asso-

ciates previously unseen, independent test data points

with their dependent continuous output prediction. In

the work of (Montillo and Ling, 2009), Random For-

est regression has demonstrated nice age estimation

performance (3.43 MAE) in LOPO experiments for

the young age subset of the FG-net dataset. As far

as we concern, no studies have investigated the age

estimation performance of Random Forest with the

overall age distribution. Thus, we adopt the Ran-

dom Forest in our regression experiments to demon-

strate its capability in age estimation. Technically,

Random Forest is an ensemble learning method that

grows many classiﬁcation trees t ∈{t

,..,t

}. To esti-

mate age from a new face from an input vector (DSF-

based feature vector v = V

), each tree gives a re-

gression result and the forest takes the average of es-

timated ages as the ﬁnal result. In the growing of

each tree, two types of randomness are introduced

consecutively. Firstly, a number of N instances are

sampled randomly with replacement from the original

data, to make the training set. Then, if each instance

comprises of M input variables, a constant number m

(m<<M) is speciﬁed. At each node of the tree, m

variables are randomly selected out of the M and the

best split on these m variables is used to split the node.

The process goes on until the tree grows to the largest

possible extent without pruning, where the resulted

subsets of the node are totally puriﬁed in label.

5 EXPERIMENTS

Our experiments are carried out with Random For-

est Regression on FRGCv2 dataset. The FRGCv2

dataset was collected by researchers from the Uni-

versity of Notre Dame and contains 4007 3D near-

frontal face scans of 466 subjects, where 203 are fe-

male and 263 are male (Phillips et al., 2005). The

age of subjects ranges from 18 to 70, with 92.5%

Can3DShapeoftheFaceRevealyourAge?

in the 18-30 age group. Our experiments are per-

formed with the 466 earliest scans of each subject

in FRGCv2. With the 466 earliest scans, we design

two experiment protocols. The ﬁrst protocol, named

Gender-General-Protocol (GGP), experiments on the

466 scans directly with Random Forest Regression.

While the second protocol, named Gender-Speciﬁc-

Protocol (GSP), separates the 466 scans into male

group and female group ﬁrst, and then performs ex-

periments on each group separately with Random

Forest Regression. For all the two protocols, exper-

imental results are generated using the Leave-One-

Person-Out (LOPO) cross-validation strategy, where

each time one scan of the concerning data (all 466

scans or scans of each gender) is used as testing face

once, with the rest the scans used in training. Thus,

there are altogether 466 experiments in the cross-

validation in each protocol, and each scan is tested

equally only once.

5.1 Gender-general Experiment

As described above, with the Gender-General-

Protocol (GGP), we perform Leave-One-Person-Out

cross-validation experiments directly with the 466

earliest scans of FRGCv2 dataset for each descriptor.

Each time one scan is picked out for testing and the

rest 465 scans are used for training. Table 1 shows

the experimental results as the mean of the absolute

error between the truth and the estimated age for each

tested scan in corresponding age group. By taking

the minimum value of the estimated ages given by the

four descriptors as the age estimation result, we have

also obtained the fusion results, as shown also in Ta-

ble 1. From this table, we observe that we achieve a

minimum overall mean absolute error (MAE) about

3.7 years by the Averageness and Spatial DSFs. For

the other three descriptors, the overall mean absolute

errors are a little higher, while all of them are under 4

years. Thus, from the perception of the overall mean

absolute errors, we ﬁnd that our approaches with all

of the four descriptors are effective in age estimation.

Moreover, when we go inside of the details of these

results for each age group, we ﬁnd that the age es-

timation performance declines signiﬁcantly with ag-

ing. We assume that the big decrease of the number of

scans in aged groups (from about 200 to about 20) ac-

counts largely for this performance decline. From the

same table, we also observe that the fusion method,

which takes the minimum of the estimated ages con-

cerning each of the four descriptors, yields a better

overall mean absolute error of 3.29 years. It means

that the age related cues in these descriptors are dif-

ferent and complimentary in age estimation. When

going inside of the detail of the fusion result for each

age group, we ﬁnd the enhancement of overall perfor-

mance is mainly coming from young age groups. It

is probably due to the fact that for young age groups,

more scans are available in training for each descrip-

tor. Thus the estimation results from each descriptor

for young age groups are less biased for making the

fusion decision.

Table 1: Age estimation results for different age groups with

the Gender-General-Protocol. (MAE:Mean Absolute Error;

AVR: Averageness; SYM: symmetry; GRA: gradient; SPA:

spatial; MIN: minimum rule for fusion).

Age

group

MAE

AVR

MAE

SYM

MAE

GRA

MAE

SPA

Fuse

MIN

] of

scans

≤ 20 3.48 3.43 3.77 3.30 2.20 185

(20,30] 2.18 2.58 2.32 2.38 1.98 246

(30,40] 9.99 7.60 10.05 8.92 9.18 20

> 40 24.82 23.66 24.56 25.36 25.75 15

Overall 3.76 3.79 3.94 3.76 3.29 466

Figure 2 shows the experimental results of

Gender-General-Protocol by cumulative scores for

the four descriptors. The x-axis is the level of Mean

Absolute Error, which represents the mean of the ab-

solute age error (between the truth and estimated age

of scan) over the 466 scans. The y-axis show the cu-

mulative score of accuracy by percentage of accep-

tance. Thus, a point (a,b) on the curve shows, with a

Mean Absolute Error tolerance of a years, it achieves

an acceptance of b percent. We have also captured the

fusion result in the same ﬁgure by cumulative scores.

From Figure 2, we observe that with a Error Level

of 5 years, we achieve an acceptance of more than

75% over the 466 scans; when the Error Level is 10

years, the cumulative score of acceptance increases to

more than 90%. We also observe that the fusion re-

sult is signiﬁcantly higher compared with the result of

each individual descriptor. From these observations,

we claim again that our approach concerning all these

descriptors are comparably effective in age estima-

tion, and the result-level fusion of these descriptors

enhances the age estimation performance.

5.2 Gender-speciﬁc Experiment

Gender and age are natural co-variates in human face.

In (Samal et al., 2007), Ashok Samal et al. statisti-

cally conﬁrm that sexual dimorphism is strong and

widespread among face features, and ﬁnd out the de-

gree of dimorphism changes as a function age (e.g.,

the average age at which the sexual dimorphism be-

comes more signiﬁcant is around 13). Thus, the face

aging effect is considerably different with different

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

1 2 3 4 5 6 7 8 9 10

100

Error Level (years)

Cumulative Score−ACCURACY (%)

Averageness + RF regression

Symmetry + RF regression

Gradient + RF regression

Spatial + RF regression

Fusion−Min

Figure 2: Age regression results in Leave-One-Person-Out

experiment with the Gender-General-Protocol.

gender. In the experiments considering the Gender-

Speciﬁc-Protocol (GSP), we ﬁrst separate the 466

earliest scans of FRGCv2 into male group and fe-

male group, then we perform Leave-One-Person-Out

cross-validation experiments on male scans and fe-

male scans separately for each descriptor. As in the

GGP experiments, each time we take one scan in test-

ing and the rest scans in training. The ﬁnal results for

each descriptor are generated by statistically merging

the results from each gender.

Table 2: Results for different age groups with the Gender-

Speciﬁc-Protocol. (MAE:Mean Absolute Error; AVR: Av-

erageness; SYM: symmetry; GRA: gradient; SPA: spatial).

Age

group

MAE

AVR

MAE

SYM

MAE

GRA

MAE

SPA

Fuse

MIN

] of

scans

≤ 20 3.25 3.38 3.46 3.19 2.14 185

(20,30] 2.03 2.16 2.14 2.18 2.04 246

(30,40] 8.97 8.52 9.18 8.81 10.43 20

> 40 20.81 22.59 21.32 22.22 24.05 15

Overall 3.42 3.57 3.58 3.51 3.15 466

Table 2 shows the experimental results as the

mean of the absolute error between the truth and the

estimated age for each tested scan in corresponding

age group. From Table 2, we observe that for all

the four descriptors, we always achieve better over-

all performance with GSP. We also achieve better re-

sults in each age group with all these descriptors,

except for the symmetry descriptor in the (30,40]

age group. With these observations, which indicate

that the Gender-Speciﬁc-Protocol outperforms the

Gender-General-Protocol in age estimation, we con-

ﬁrm the claims in (Samal et al., 2007), that faces of

different gender convey considerably different mor-

phology of aging. Moreover, the overall fusion result

outperforms again the result of each descriptor in the

GSP experiments, and also the overall fusion result in

the GGP experiments. It shows again that the result-

level fusion of these descriptors can enhance the age

0 1 2 3 4 5 6 7 8 9 10

100

Error Level with Average Description (year)

Cumulative Score−ACCURACY (%)

0 1 2 3 4 5 6 7 8 9 10

100

Error Level with Symmetry Description (year)

Cumulative Score−ACCURACY (%)

0 1 2 3 4 5 6 7 8 9 10

100

Error Level with Gradient Description (year)

Cumulative Score−ACCURACY (%)

0 1 2 3 4 5 6 7 8 9 10

100

Error Level with Spatial Description (year)

Cumulative Score−ACCURACY (%)

M466

F466

M263

F203

M466

F466

M263

F203

M466

F466

M263

F203

M466

F466

M263

F203

Figure 3: Comparison of results from the Gender-General-

Protocol (GGP) and the Gender-Speciﬁc-Protocol (GSP)

for each gender. (M-gender-general: male group in GGP

experiments; F-gender-general: female group in GGP ex-

periments; M-gender-speciﬁc: male group in GSP exper-

iments; F-gender-speciﬁc: female group in GSP experi-

ments.)

estimation performance.

Figure 3 makes a further comparison between

the GGP and GSP experiments, with the cumulative

scores for each gender and for each descriptor in these

two type of experiments. From Figure 3, we observe

that, only except for the beginning part of result with

the female group and symmetry descriptor, the experi-

mental results are always signiﬁcantly higher for both

male and female groups in the GSP experiments for

all the descriptors. That is to say, although trained

with less data, the GSP experiments have the advan-

tage of giving better age regression results. One prob-

able explanation for this observation is that, in the

GSP experiments, the regression results do not suf-

fer the inﬂuence from the scans in the other gender,

which conveys a signiﬁcantly different aging mor-

phology. With Figure 3, we further conﬁrm that the

aging effect differs with gender.

Can3DShapeoftheFaceRevealyourAge?

6 CONCLUSIONS

In this paper, we presented the ﬁrst work in age es-

timation based on 3D facial scans. Our approach

uses the DSF features extracted from 3D face from

four different perspectives of face perception. Fol-

lowing the Leave-One-Person-Out experimental set-

ting when using the Random Forest Regression strat-

egy, we have achieved comparable age estimation re-

sults with all the four descriptions. And with the age

estimation results improved in their fusion, we have

conﬁrmed that the four perspectives produce compli-

mentary information for age estimation. By investi-

gating the age estimation separately on Female and

Male subsets, we have achieved better age estimation

results, which justiﬁes that the general aging effect of

face differs considerably with gender.

ACKNOWLEDGMENTS

This work was supported by the ANR through the 3D

Face Analyzer project under the contract ANR 2010

INTB 0301 01 and by the Chinese Scholarship Coun-

cil (CSC) to Baiqiang Xia.

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Can3DShapeoftheFaceRevealyourAge?