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

Xin Geng1,2

, Kate Smith‐Miles1

Automatic Facial Age Estimation

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

Introduction–

Human facial aging

estimation?–

Main challenges

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

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•

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•

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]

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:

= f(b)

[Lanitis

et al. TPAMI’

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

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)

et al., TIP’06]–

Conformal Embedding Analysis (CEA)

[Fu and Huang, TMM’08]–

Locally Adjusted Robust Regression (LARR)

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)

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

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

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–

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•

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)

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•

Outline•

Introduction

Evaluation metrics–

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

Outline•

Introduction

Our approaches•

Comparison of existing techniques•

Summary table [Fu et al., TPAMI’10]

Outline•

Introduction

Our approaches–

Learning from label distributions•

Outline•

Introduction

Our approaches–

AGES Series•

The original

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.

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.

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.

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

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

Personalized

Temporal

Difficulties

Incomplete

Image sequence

class sequence

AGES – Learning•AGES

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

person

AGES – LearningApply PCA iteratively on the incomplete aging patterns

Aging Patterns

Subspace

PCA Reconstruction

Improved

AGES – Learning

Initialization (including the first PCA)

Reconstruction

AGES – Learning

0 2 4 6 8

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–

face images

subjects–

6‐18

images each

subject–

Age: 0‐69

Variations: illumination, pose,

expression, beards, moustaches, spectacles, hats

Experiments•

Data set: MORPH Database–

face images

Around

images each subject–

Age: 16‐68

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

Experiments•

Compared Methods–

AGESlda

WAS, [Lanitis

et al. TPAMI, 2002]–

AAS, [Lanitis

et al. TSMCB, 2004]

Conventional classification methods•

BP network•

C4.5 decision tree

Experiments•

Compared Methods–

Human observers (29 college students)•

HumanA

HumanB

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•

Cumulative Scores

FG-NET (LOPO) MORPH (Test Set)

Outline•

Introduction

Our approaches–

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–

face images

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

HumanB

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

D: Lack of training data–

C: •

Dealing with missing data

et al., MM’06, TPAMI’07]•

Learning from label distributions

et al., AAAI’10]•

Online training

[Ni et al., MM’09]–

D: Personalized aging patterns

Specified data structure

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

D: Temporal aging patterns–

Specified data structure

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•

Voice•

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?

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

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