automatic facial age estimationyunfu/papers/pricai10_t4.pdf · model age progression in young faces...

Xin Geng1,2

, Yun

Fu3

, Kate Smith‐Miles1

Automatic Facial Age Estimation

1

Monash

University, Australia2

Southeast University, China3

University at Buffalo (SUNY), USA

Tutorial at PRICAI 2010

2:00pm-5:00pm, Monday, August 30, 2010

•

Introduction•

Overview of existing techniques

•

Evaluation of existing techniques•

Our approaches

•

Conclusions and discussions

Outline

•

Introduction–

Human facial aging

–

What is facial age estimation?–

Why we need the techniques for automatic facial age

estimation?–

Main challenges

•

Overview of existing techniques•

Evaluation of existing techniques

•

Our approaches•


Outline

Who is this man?

Human Facial Aging

Facial aging of Albert EinsteinFacial aging of Albert Einstein

With the progress of age, the appearance of human faces exhibits remarkable changes

•

Craniofacial growth–

Face contour

–

Facial feature (eyes, nose, mouth etc.) shape–

Facial feature distribution on face

•

Skin aging–

Skin color

–

Wrinkles•

Reduction of muscle strength/ elasticity–

Facial lines

–

Wrinkles–

Facial feature shape

–

Facial feature distribution on face

Why?

And why?•

2009 Nobel Prize in Physiology or Medicine

Elizabeth H. Blackburn Carol W. Greider Jack W. Szostak

Discover the relationship of telomeres and the aging of cells

•

Slow•

Irreversible

•

Personalized–

Internal influential factors•

Gene

•

Gender–

External influential factors•

Health

•

Living style•

Working environment

•

Sociality•

Weather conditions

•

Smoking•

…

Characteristics of the Aging Progress

•

Early age (from birth to adulthood)–

Mainly shape changes caused by craniofacial growth•

Forehead Slopes back, shrinks and releases spaces on the surface of the

cranium •

Facial featuresExpand their areas and tend to cover the interstitial spaces

•

CheeksExtend to larger areas

•

ChinBecomes more protrusive

Facial Aging Stages

•


Mainly shape changes caused by craniofacial growth

Facial Aging Stages

[Ramanathan and Chellappa, CVPR’06]

[Todd et al., Scientific American, 1980]

•


Minor skin changes•

Facial hairs Become dense, change color

•

Skin colorSlightly changes

Facial Aging Stages

Facial Aging Stages•

During the adulthood (from adulthood to old age)–

Mainly skin changes (texture changes)•

Thinner

•

Darker•

Less elastic

•

More leathery

•

Adynamic/ Dynamic wrinkles•

Blemishes

•

Double chin•

Dropping cheek

•

Eyelid bags …

[Gonzalez-Ulloa and Flores, Plast. Reconstr. Surg. 1965]

Facial Aging Stages•

During the adulthood (from adulthood to old age)–

Minor craniofacial growth•

Face shape–

U‐shaped

–

Upside‐down triangle–

Trapezoid

–

Rectangle

Outline•

Introduction–

Human facial aging

–



estimation?–

Main challenges

•



•

Our approaches•


Facial Age Estimation•

Definition

Label a face image automatically with the exact age (year) or the age group (year range) of the individual face

Facial Age Estimation Age = 3 (years)


Reversely…–

Facial Age Synthesis

Re‐render a face image aesthetically with natural aging or rejuvenating effects on the individual face.

Facial Age SynthesisInput Image

Synthesized Image at the Target Age

Target Age (Older)


About the Age–

Actual Age (Chronological Age)

The number of years an individual has lived, i.e., “the real age”.

–

Appearance Age The age information revealed by the visual appearance.

–

Perceived Age The individual age gauged by human subjects from the

visual appearance.–

Estimated Age

The indiviaul age recognized by machine from the visual appearance.

Ground truth

Usually consistent with the actual age, but variation is often inevitable

Defined on visual appearance. The closer to the appearance age, the better.

Which is better?

Outline•

Introduction–

Human facial aging

–



estimation?–

Main challenges

•



•

Our approaches•


Motivation•

Age estimation is a basic ability required for smooth

communication between humans. –

People at different ages have different requirements and

preferences in various aspects, such as •

Linguistics

•

Aesthetics•

Consumption habit

•

…–

A good understanding of age leads to successful

communication

between humans•

What about human‐computer interaction?

Motivation•

Human ability in age estimation–

Developed early in life

–

Can be fairly accurate–

But can be affected by various factors•

More accurate in the estimator’s own kind–

Age group

–

Race–

Gender

•

Facial attachment–

Glasses

–

Beard•

Attractiveness

•

Kindness

Motivation•

Is it easy?

–

Difficult for humans!–

Even more difficult for machines!

19 49 5

Motivation•

But it is very useful!–

Age‐specific HCISatisfy preferences of all ages

–

Age‐specific access control Children protection

–

Law enforcement Security and Surveillance

–

Multi‐cue identification face/fingerprint/iris + age

Applications•

e.g., Internet safety for minors

Applications•

e.g., Cigarette vending machine

Applications•

e.g., Age‐specific shopping HCI

Outline•

Introduction–

Human facial aging

–



estimation?–

Main challenges

•



•

Our approaches•


Challenges –

Incomplete aging patterns•

The aging progress is uncontrollable–

No one can age at will

–

The procedure of aging is slow and irreversible–

The collection of sufficient training data for age

estimation is extremely laborious•

Consequently, –

The available dataset typically just contain a very

limited number of aging images for each person–

The images at the higher ages are especially rare

Challenges ‐

personalized aging patterns•

Different persons age in different ways–

Internal influential factors•

Gene

•

Gender–

External influential factors•

Health

•

Living style•

Working environment

•

Sociality•

Weather conditions

•

Smoking•

…

•

Consequently, –

The mapping from features (face images) to class labels (ages) is

not unique

Challenges ‐

temporal aging patterns•

The aging progress must obey the order of time. –

The face status at a particular age will affect all older faces,

but will not affect those younger ones.•

Consequently, –

The set of class labels (ages) is a totally ordered set

–

Each age has a unique rank in the time sequence

Outline•

Introduction

•

Overview of existing techniques–

Aging face models

–

Age estimation algorithms•


•

Our approaches•


Flow chart of age estimation systems

Aging face model

Age estimation algorithms

Face image Age

Outline•

Introduction

•


Aging face models

–



•

Our approaches•


Aging face models•

Anthropometric models–

Based on the measurements and proportions of the human faces

•

Active appearance models–

Based on the statistical face model AAM proposed by T. Cootes

et al.

•

Aging pattern subspace–

Based on the AGES method proposed by Geng

et al.

•

Age manifold–

Based on the manifold embedding techniques to learn the low‐

dimensional aging trend•

Appearance feature models–

Based on aging‐related features extracted from face images

Anthropometric models•

The earliest work on age estimation based on face

images –

[Kwon and Lobo, CVPR’94] [Kwon and Lobo, CVIU’99]

–

Six

ratios of distances on frontal face images to separate babies from adults


Face recognition across age progression –

[Ramanathan

and Chellappa, CVPR 2006] –

Model age progression in young faces with eight

ratios of

distance measures to predict an individual’s appearance across different ages

Eight ratiosEight ratios

(1) Facial index

(2) Mandibular index

(3) Intercanthal index

(4) Orbital width index

(5) Eye fissure index

(6) Nasal index

(7) Vermilion height index

(8) Mouth-Face width index


Might be useful to distinguish minors from adults,

but not appropriate for classifying different adult ages

–

E.g., Kwon and Lobo [ CVPR’94, CVIU’99]

further analyze the wrinkles to separate young adults from senior adults


Sensitive to head pose–

Usually only frontal faces are used to measure the facial

geometries–

All tested on small data sets

•

Do not involve texture information

Active appearance models•

The appearance model [Edwards et al., IVC’98]–

Combine a model of shape

variation with a model of

texture

variation–

Require a training set of annotated images where the

‘landmark points’

have been marked on each example



Shape•

The face shape in each image is represented as a vector x

•

Procrustes

analysis is used to align the shape vectors over the training set

•

Apply PCA to the shape vectors x

to get the shape model–

Texture•

Warp each training image so the points match those of the mean

shape, obtaining a “shape‐free patch”•

A texture vector g

is created via raster scan, and normalized

•

Apply PCA to the texture vectors g

to get the texture model



Combine shape and texture models

•

: the mean shape•

: the mean texture

•

: matrices describing the modes of variation derived from the training set

•

: the appearance model parameters•

How? –

concatenate the shape model parameters and

texture model parameters and apply PCA again


The active appearance model [Cootes, et al., TPAMI’01]–

The appearance model relies on the ‘landmark points’

–

In the training set, the landmark points are manually labeled

–

Given a new image, an active search process

is required to find the parameters that make the appearance model matches the image as closely as possible


Age estimation based on AAM–

Aging function:

age

= f(b)

[Lanitis

et al. TPAMI’

02]

•

b: the vector of the AAM parameters•

f: a quadratic function.

•

Training–

f() is fitted by Genetic Algorithm (GA) for each individual in the

training set as his/her aging function•

Age estimation

–

Substitute the AAM parameters of a new face image into the aging

function. The output of the function is estimated age–

Other variations based on aging function

[Lanitis

et al. TSMCB’04]

–

AGES also uses AAM [Geng

et al. MM’06], [Geng

et al. TPAMI’07]

Active appearance models

•

AAM vs.

Anthropometric model–

AAM considers both the shape

and texture, while

the anthropometric model only involve facial geometry

–

AAM based approaches can deal with any age, while the anthropometric model can be only used

to distinguish minors from adults–

AAM is robust against head poses, while the

anthropometric model is quite sensitive to poses

Aging pattern subspace•

Regard the sequence of an individual’s aging face

images as a whole

rather than separately•

Based on a data structure called aging pattern

•

Two stages: learning stage & age estimation stage•

Learning stage –

Model the aging patterns by constructing a representative

subspace•

Age estimation stage–

Find the most suitable age for the single test face image

based on the subspace of image sequences•

Refs: the AGES algorithm series

[Geng

et al., MM’06], [Geng

et al., TPAMI’07], [Geng


et al., ICASSP’09]

Aging Pattern

An aging pattern is a sequence of personal face images sorted in time order

Appearance Model[Edwards et al. IVC’98]

Incomplete aging pattern causes many missing values in the aging pattern vector

Aging Pattern•

Advantages

of using aging pattern as basic data sample

–

Each aging pattern involves only one person –

personalized–

All the images in one aging pattern are sorted in the time order

–

temporal

•

New challenges–

The aging patterns are always incomplete•

There are many missing values

in the aging pattern vector

–

An aging pattern is an image sequence, which corresponds to a class (age) sequence

•

But what we need finally is to give a single age estimation

to a single face image


AGESAGing

pattErn

Subspace

•

The goalLearn a representative subspace for the aging patterns

•

How to deal with the massive missing values?–

Apply PCA iteratively

•

How to predict the age of a single face image based on the subspace of image sequences?

–

Put the face image into different positions in the aging pattern

–

Find the position with minimum reconstruction error


Well match the characteristics of the age estimation

problem•

High request for the quality of training samples, but

with certain ability to handle missing face images in the aging sequence

•

Extensibility–

Feature extraction methods (AAM or any other?)

–

Linear PCA Nonlinear subspace analysis Multilinear

(tensor) subspace analysis

…

Age manifold•

Assume that the aging face images are distributed on an

intrinsic low‐dimensional manifold (faces with close ages locate closely on the manifold)

•

Regard the age estimation problem as a special case of supervised manifold embedding

problem

•

Rely on the power of manifold learning to find out the mapping from the face images to the ages

Age manifold•

Problem formulation

(for any supervised manifold

learning, not specified for age)–

Given•

Samples:

•

Class labels:–

The goal•

Find the low‐dimensional embeddings

•

The mapping from the original space to the manifold space

Age manifold•

Typical approaches–

Orthogonal Locality Preserving Projections (OLPP)

[Cai

et al., TIP’06]–

Conformal Embedding Analysis (CEA)

[Fu and Huang, TMM’08]–

Locally Adjusted Robust Regression (LARR)

[Guo

et al., TIP’08], [Guo

et al., WACV’08]–

Synchronized Submanifold

Embedding (SSE)

[Yan et al., TIP’09]

Age manifold•

Could find the intrinsic low‐dimensional manifold for

the aging face images•

Do not require many images at different ages from

the same person•

But require many images labeled with age to learn

the embedded manifold with statistical sufficiency•

Usually not tailored to the characteristics of the age

estimation problem •

Can be applied to other problems as well

Appearance feature models•

Extract age‐related features based on the face

appearance–

Texture features vs. Shape features

–

Global features vs. Local features–

Graphics / image / signal processing features

–

Biologically inspired features



[Hayashi et al., ICVSM’01, SICE’02]•

Consider both texture (wrinkle) and shape (geometry)

features•

A semantic‐level indexing of the face was also used

•

Extended by [Fujiwara et al., KES’03]–

[Fukai

et al., SICE’07]

•

Use Fast Fourier Transform (FFT) to extract feature spectrum

•

Use Genetic Algorithm (GA) for feature selection



[Günay

and Nabiyev, ISCI’08]

•

Rely on the texture descriptor Local Binary Patterns (LBP)

–

[Gao

and Ai, ICAB’09]•

Rely on the Gabor features

–

[Yan et al., CVPR’08, ICASSP’08]•

Use Spatially Flexible Patch (SFP) as local feature

descriptor



[Suo

et al., FGR’08]

•

Use four types of features: topology, geometry, photometry and configuration

•

Hierachical

face model ‐

a face image is decomposed into detailed parts from coarse to fine

–

[Guo

et al., CVPR’09]•

Use Biologically Inspired Features (BIF)

–

Based on the ‘HMAX’

model – a feed‐forward model of the primate

visual object recognition pathway–

[Guo

et al., ICCV’09]

•

BIF + age manifold features

Outline•

Introduction

•


Aging face models

–



•

Our approaches•



Closely related to the aging face models used–

Strong facial feature + simple age estimation algorithm

–

Simple facial feature + strong age estimation algorithm–

Integrated aging face model and age estimation

algorithm•

Age estimation is a special PR problem–

Each age is a class label –

classification

–

Each age is a number –

regression–

Combine classification and regression?

–

Beyond classical classification and regression?

Classification•

[Lanitis

et al., TSMC‐B’04]

–

General purpose classification methods applied to age estimation

•

Nearest neighbor•

Multilayer perceptron

•

Self‐organizing map (SOM)•

[Ueki et al., FGR’06]–

Age group classification

–

Two‐phase feature extraction: 2DLDA+LDA–

One Gaussian model is trained for each age group

–

Classification is based on likelihoods of the Gaussian models

Classification•

[Guo

et al., WACV’08, TIP’08]

–

Apply SVM to age estimation•

[Kanno

et al., IEICE TIS’01]

–

Artificial neural network–

4‐class age‐group classification

•

AGES [Geng


et al., TPAMI’07], [Geng


et al., ICASSP’09]

–

Age classification based on aging pattern subspace–

Compare AGES with general purpose classification

methods: kNN, BP, C4.5, SVM

Regression•

Aging function

[Lanitis

et al. TPAMI’

02, TSMCB’04]–

Three formulations for AF: linear, quadratic, and cubic

–

Output of the AF is the estimated age•

Multiple linear regression

[Fu et al., ICME’07, TMM’08]–

Fit the CEA age manifold

•

Support Vector Regression (SVR) [Guo

et al., WACV’08, TIP’08]

–

Fit the OLPP age manifold

Regression•

[Yan et al ., ICCV’07]–

Perform age estimation via a regressor

learned from

uncertain nonnegative labels–

Use Semi‐Definite Programming (SDP) to solve the

regression problem•

Image Based Regression (IBR)

[Zhou et al., ICCV’05]–

A boosting scheme is used to select features from

redundant Haar‐like feature set•

Robust Multi‐instance Regression (RMIR)

[Ni et al., MM’09]–

Automatic online training

Hybrid Approach•

Locally Adjusted Robust Regression (LARR)

[Guo

et al., WACV’08, TIP’08]–

Global regression by SVR

–

Locally adjust the estimated age via classification (SVM)–

Local search range is determined heuristically

•

Further improvement of LARR [Guo

et al., SVPR‐SLAM’08]

–

Determine the combination parameters automatically •

Transform both the regression (SVR) and classification

(SVM) results into probabilities using a uniform distribution

•

Combine the results via Bayesian theory

Beyond classical classification and regression?•

Classification–

What if the class labels are closely related to each other?

–

What if the training samples for different classes are unbalanced?

•

Regression–

How to constrain the output within a desired range?

–

How to choose a suitable model for regression?•

An approach beyond all these –

Learning from Label Distributions (LLD) [Geng

et al., AAAI’10]

–

Particularly useful when•

The classes are correlated to each other

•

The training data for some classes are insufficient

Matching the problem

•

Neighboring ages are highly correlated–

Aging is a slow and gradual progress

–

The faces at close ages look quite similar•

The ‘lack of training samples’

problem

–

The available dataset typically just contain a very limited number of aging images for each person

–

The images at the higher ages are especially rare•

LLD matches the problem of age estimation well–

Use the neighboring ages to relieve the ‘lack of

training samples’

problem

Label distribution

•

A real number is assigned to each class label•

Those numbers for all the labels sum to 1

•

Similar to but NOT probability

LLD for age estimation•

Each image is assigned with a label distribution–

Highest at the real age

–

Decrease with the distance from the real age–

E.g., Gaussian distribution, triangle distribution

•

When learning a particular age, the neighboring ages are also utilized

–

The contribution is reversely proportional to the distance from the neighboring age to the real age

LLD for age estimation•

Learning from label distributions–

Learn a conditional p.d.f. as similar as possible to the label

distribution –

Similarity is measured by the Kullback‐Leibler

divergence

•

Age estimation–

Given a face image x

–

Calculate its label distribution–

Explicit label distribution of x provides many possibilities in classifier

design•

Single label

•

Multiple labels•

Prediction with confidence measure

Outline•

Introduction

•


Evaluation of existing techniques–

Aging face databases

–

Evaluation metrics–

Comparison of existing techniques

•

Our approaches•


Aging face databases•

FG‐NET Aging Database–

1002 face images

–

82 subjects–

Age: 0‐69

–

Publicly available•

YGA Database–

8000 face images

–

1600 subjects–

Age: 0‐93


MORPH Database–

Album 1•

1724 face images

•

515 subjects•

Age: 15‐68

–

Album 2•

> 20,000 face images

•

>4000 subjects•

Age: 21‐99

–

Publicly available


WIT‐DB Database–

26,222 face images

–

5500 subjects–

Age: 3‐85

•

AI&R (V2.0) Asian Face Database–

34 face images

–

17 subjects–

Age: 22‐61

•

Burt’s Caucasian Face Database–

147 face images

–

147 subjects–

Age: 20‐62


LHI Face Database–

8,000 face images

–

8,000 subjects–

Age: 9‐89

•

HOIP Face Database–

306,000 face images

–

300 subjects–

Age: 15‐64

•

Iranian Face Database–

3,600 face images

–

616 subjects–

Age: 2‐85


Gallagher’s Web Collected Database–

28,231 faces

–

5,080 images–

Seven age ranges: 0‐2, 3‐7, 8‐12, 13‐19, 20‐36, 37‐65, 66+

•

Ni’s Web Collected Database–

219,892 faces

–

77,021 images–

Age: 1‐80

•

3D Morphable

Database–

200 adults

–

238 teenager


Summary–


FG‐NET Aging Database

•

MORPH Database•

Gallagher’s Web Collected Database

–

Large size•

MORPH Database

•

YGA Database•

LHI Face Database

•

NI’s

Web Collected Database•


Outline•

Introduction

•




–

Evaluation protocols–


•

Our approaches•


Evaluation protocols•

Two measurements–

Mean Absolute Error (MAE)

•

First used in [Lanitis

et al., TPAMI’02]•

An indicator of the average performance of the

age estimators–

Cumulative Score (CS) at error level l

•

First used in [Geng

et al., MM’06]•

An indicator of accuracy of the age estimators


An example of CS [Geng

et al., MM’06]


Training Set vs. Test Set–

Half‐half

–

Cross validation–

Leave One Person Out (LOPO)

•

Suggested evaluation protocol–

MAE + Cumulative Score

–

LOPO

Outline•

Introduction

•




–



•

Our approaches•


Comparison of existing techniques•

Summary table [Fu et al., TPAMI’10]

C

Comparison of existing techniques•

Summary table [Fu et al., TPAMI’10]

Outline•

Introduction

•



•

Our approaches–

AGES

–

Learning from label distributions•


AGES Series•

The original

1.

Xin

Geng, Zhi‐Hua

Zhou, Yu Zhang, Gang Li, and Honghua

Dai. Learning from

facial aging patterns for automatic age estimation. ACM MM'06, Santa Barbara,

CA, 2006, pp. 307‐316. 2.

Xin

Geng, Zhi‐Hua

Zhou, and Kate Smith‐Miles. Automatic age estimation

based on facial aging patterns. IEEE TPAMI, 2007, 29(12): 2234‐2240.

•

Variants–

Nonlinear3.

Xin

Geng, Kate Smith‐Miles, and Z.‐H. Zhou. Facial Age Estimation by

Nonlinear Aging Pattern Subspace. ACM MM'08, Vancouver, Canada, 2008, pp.

721‐724.

–

Multilinear4.

Xin

Geng

and Kate Smith‐Miles. Facial Age Estimation by Multilinear

Subspace

Analysis. ICASSP’09, Taipei, Taiwan, 2009, pp. 865‐868.

Aging patternFace image

Iterative learning algorithm

AGES

Incomplete temporal aging patterns which are determined by personalized factors

Difficulties

& Countermeasures

Aging Pattern

An aging pattern is a sequence of personal face images sorted in time order

Appearance Model[Edwards et al. IVC’98]

Aging Pattern•

Advantages &

New Challenges

Aging Pattern

Identity

Time

Personalized

Temporal

Difficulties

Incomplete

Image sequence

class sequence

AGES – Learning•AGES

AGing

pattErn

Subspace•The goal

Learn a representative subspace for the aging patternsPCA (Principal Component Analysis)?

•Basic idea

Different person Same age

Same person Different ages+

Missing faces

?

mm

mm

m m

m mm

age

person

AGES – LearningApply PCA iteratively on the incomplete aging patterns

Aging Patterns

Subspace

PCA Reconstruction

Improved

Improved

AGES – Learning

Initialization (including the first PCA)

Reconstruction

PCA

AGES – Learning

0 2 4 6 8

10

12 14 16 18

Full-fill the aging patterns

AGES – Age Estimation

LDA+AGES•

There are usually variations other than aging in the

data set•

LDA can be applied to the feature vectors with age

labels•

The resulting discriminant

parameters are expected

to be more related to the aging variation•

The AGES based on such discriminant

parameters is

denoted by AGESlda

Experiments•

Data set: FG‐NET Aging Database–

1002

face images

–

82

subjects–

6‐18

images each

subject–

Age: 0‐69

–

Variations: illumination, pose,

expression, beards, moustaches, spectacles, hats

Experiments•

Data set: MORPH Database–

433

face images

–

Around

3

images each subject–

Age: 16‐68

–

Used to test the algorithms trained on the FG‐NET database

Experiments•

Compared Methods–

AGES

–

AGESlda

–

WAS, [Lanitis

et al. TPAMI, 2002]–

AAS, [Lanitis

et al. TSMCB, 2004]

–

Conventional classification methods•

kNN

•

BP network•

C4.5 decision tree

•

SVM

Experiments•

Compared Methods–

Human observers (29 college students)•

HumanA

test

•

HumanB

test

Experiments•

Compare Mode–

For AGES, AGESlda

,

WAS, AAS, kNN, BP, C4.5, SVM•

FG‐NET:

Leave‐One‐Person‐Out (LOPO)

•

Train on FG‐NET and test on MORPH–

For human tests5% face images randomly selected from the FG‐NET

Aging Database ( )

•

Evaluation measure–

MAE + Cumulative score

1002 5% 51× ≈

Result – Age Estimation•

MAE

Result – Age Estimation•

Cumulative Scores

FG-NET (LOPO) MORPH (Test Set)

Outline•

Introduction

•



•

Our approaches–

AGES

–




Xin

Geng, Kate Smith‐Miles, Zhi‐Hua

Zhou. Facial

Age Estimation by Learning from Label Distributions. AAAI’10, Atlanta, GA, 2010.

The ‘lack of training samples’

problem•

Ideal training set should–

Cover a wide range of age

–

Include many different subjects–

Contain at least one image for each age of each subject

•

Unfortunately…–

The aging progress cannot be artificially controlled

–

Great efforts in searching for the images taken years ago–

Can do nothing to acquire future images

•

Consequently, –

The available dataset typically just contain a very limited number

of aging images for each person–

The images at the higher ages are especially rare

The neighboring ages

•

Aging is a slow and gradual progress•

The faces at close ages look quite similar

•

Can we use the neighboring ages to relieve the ‘lack of training samples’

problem?

Label distribution

•

A real number is assigned to each class label y, representing the degree that the label

describes the instance•

Those numbers for all the labels sum to 1

•

Similar to but NOT probability

Special cases of label distribution

•

Case 1: Single label•

Case 2: Multiple labels

•

Case 3: General case of label distribution


Problem definition–

Training set

–

Goal: learn a conditional p.d.f. from S

•

The best parameter vector

The Kullback‐Leibler divergence from to


The maximum entropy model (Berger, et al. 1996)

•

Target function of for the optimization:

,exp ( )y k kyk

Z g xθ⎛ ⎞= ⎜ ⎟⎝ ⎠

∑ ∑Where

is the normalization factor

is the k-th feature of x

The algorithm IIS‐LLD

Experiments•

Data set: FG‐NET Aging Database–

1002

face images

–

82

subjects–

6‐18

images each

subject–

Age: 0‐69

–

Variations: illumination, pose,

expression, beards, moustaches, spectacles, hats

Experiments•

How to generate LDs from the real age?–

Gaussian

–

Triangle

–

Single

Experiments•

Compared Methods–

AGES, [Geng

et al. TPAMI, 2007]

–

WAS, [Lanitis

et al. TPAMI, 2002]–

AAS, [Lanitis

et al. TSMCB, 2004]

–

General‐purpose classification methods kNN, BP, C4.5, SVM

–

29 college students (human observers)•

HumanA

test

•

HumanB

test

Results•

Mean Absolute Error (MAE)

•

Distribution width

Results•

MAE in different age ranges

Outline•

Introduction

•



•

Our approaches•


Conclusions•

Human age estimation via face is a promising

technique for vast potential applications•

Yet it is quite challenging a task–

Lack of training samples

–

Personalized–

Temporal

•

Comprehensive efforts from both academia and industry have been devoted to algorithm design, modeling, data collecting, system performance test,

valid evaluation protocols, etc.

Conclusions•

Difficulties

(D) vs. Countermeasures

(C)

–

D: Lack of training data–

C: •

Dealing with missing data

[Geng

et al., MM’06, TPAMI’07]•

Learning from label distributions

[Geng

et al., AAAI’10]•

Online training

[Ni et al., MM’09]–

D: Personalized aging patterns

–

C:•

Specified data structure

[Geng


Hierarchical models

[Lanitis

et al., TSMC‐B’04]•

Manifold learning

[Fu et al., TMM’08] [Guo

et al., TIP’08, CVPR’09]

Conclusions•

Difficulties

(D) vs. Countermeasures

(C)

–

D: Temporal aging patterns–

C:•

Specified data structure

[Geng


Regression

[Lanitis

et al. TPAMI’

02, TSMCB’04], [Fu et al., ICME’07, TMM’08], [Yan et al ., ICCV’07], [Zhou et

al., ICCV’05]

Conclusions•

Key components–

Aging face models•

Anthropometric models

•


Aging pattern subspace

•

Age manifold•

Appearance feature models

–


Classification

•

Regression•

Hybrid methods

•

Learning from label distributions

Conclusions•

Databases–


FG‐NET Aging Database

•

MORPH Database•


–

Large size•

MORPH Database

•

YGA Database•

LHI Face Database

•

NI’s

Web Collected Database•


Conclusions•


MAE + Cumulative Scores

–

Leave One Person Out (LOPO)

Future directions•

Data collection–

Extremely laborious, extremely important

–

Ideal data set should •

Cover a wide range of age

•

Include many different subjects•

Contain at least one image for each age of each subject

•

Facial attributes decomposition–

A face image shows multiple facial attributes: identity,

expression, gender, age, race, pose, etc.–

Decomposition of these facial attributes is essential to

extract age‐related features


Multi‐cue age estimation–

People rely on multiple cues to estimate other people’s

age–

Possible indicative cues for age•

Face

•

Voice•

Gait

•

Hair–

Combine face with one or more other cues for age

estimation might remarkably improve the current performance


Pose and illumination variations–

Pose and illumination variations are always troublesome

in real applications–

Pose‐invariant and illumination‐invariant techniques

(intensively investigated in face recognition) might be introduced into age estimation methods

•

Go fuzzy?–

Considering the numerous influential factors, age

estimation can hardly be certain–

Fuzzy classification•

E.g., ‘I am 85% sure that you are 18 years old’


Application oriented age estimation techniques–

Most age‐specific access control systems only need to

determine whether the user is older than a certain age (say 18), which is a binary classification problem

–

‘How old are you?’

‘Are you over 18?’•

Influential factors–

Human aging patterns can be affected by many internal

and external factors, such as genetics, race, gender, health, lifestyle, and even weather conditions

–

Incorporate the influential factors to improve performance

Thank you for attention!

Questions?

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