April 3, 2006 Face Recognition Grand Challenge 

Face Recognition Age Progression

Dr. Rama Chella

Narayanan Raman
April 3, 2006 Face Recognition Grand Challenge 

Age Progression in Human Fa

– Changes in the shape of the cranium 

from infancy to teenage

7 yrs 13 yrs 14 yrs 17 yrs 20 yrs

Facial aging effects are manifested in differin different ages :

– Changes in the skin texture during 

adulthood
April 3, 2006 Face Recognition Grand Challenge 

Outline• Previous work on facial aging• Craniofacial growth model (Modeli

progression in young faces)

• Bayesian Age-Difference classifier 

verification across age progression)

• Experimental results• Conclusions & Future work
April 3, 2006 Face Recognition Grand Challenge 

Previous Work• D’Arcy Thompson pioneered the use of ge

transformations in the study of morphogen

D’Arcy Thompson proposed that “ Geome

associated with morphogenetic changes 

physical forces acting on the animal’s en
April 3, 2006 Face Recognition Grand Challenge 

Previous Work (contd.)• J. B. Pittenger & Robert E. Shaw apply diff

transformations to the profile views of humWhich transformation models facial gro

Stress forces

Strain forces

Mark et al. (1981) observed that cardio

transformation was effective in modeling 
April 3, 2006 Face Recognition Grand Challenge 

• Ho Kwon et al. (1994) :• Alice O’Toole (1997) :• Tidderman et al. (2001) :

Builds aging 

PCA coeffic

textur

• Lanitis et al. (2002) :

Age classifica

images – you

face anth

Age perception 

caricature : e

wrinkles increas

Prototyping a

facial texture 

Previous Work (contd)
April 3, 2006 Face Recognition Grand Challenge 

Craniofacial Growth Model (co

Wolf’s law : Stress is a direct stimulant to growRemodeling of Human head with growth is conto the remodeling a fluid-filled spherical tank wet al 1980)

k : analogous to the 
April 3, 2006 Face Recognition Grand Challenge 

Craniofacial Growth Model (co

Applying the revised-cardioidal strain transformation profile views of human heads : the resulting trais perceived as that of facial growth

Resulting 
April 3, 2006 Face Recognition Grand Challenge 

Craniofacial Growth Model (coApplying the transformation on real face images : we observe that the prediction isgood for small age transformations and poor for large age transformations Growth parameters in differentfacial regions change with age

Hence, age-basmeasurements aestimate facial g
April 3, 2006 Face Recognition Grand Challenge 

Face Anthropometry

Leslie Faanthropomeasurefrom facages (0 – 

52 such facial measin our study. 
April 3, 2006 Face Recognition Grand Challenge 

Face Anthropometry (contd.)

The ‘Origin of reference’ for the revised-cardioidaestimated using the growth patterns observed acroFacial prototypes for different ages are created usinanthropometric measurements.
April 3, 2006 Face Recognition Grand Challenge 

Computing Growth parameters

Linear and Non-linear constraints on growth parameters

Glossary 

Age-based growth parameters landmarks

Ratios of age-based facial meas
April 3, 2006 Face Recognition Grand Challenge 

Computing Growth parameters 

The comgrowth pformulata non-lin

We use Levenberg Marquartd optimization to solvbased growth parameters defined over facial landm

Using thformulatgrowth pother fac
April 3, 2006 Face Recognition Grand Challenge 

Results Prediction oacross age pThe proposemodel can bperform facacross age pimages of ch
April 3, 2006 Face Recognition Grand Challenge 

Face Verification across Age progr

Problem Statement :

• Given a pair of age separated face images of 

is the confidence measure in verifying the ide

• How does age progression affect facial simila

Passport Image database  465 pairs 

 Age range  Pair-wise 

9 1 - 2 9 3 - 4 9 5 - 7 9 8 - 9 
April 3, 2006 Face Recognition Grand Challenge 

Bayesian Age difference classi

• Given a pair of age separated face images

• Establish identity : intrapersonal / extraperson• Classify intrapersonal age separated samples b

difference : 1-2 yrs , 3-4 yrs, 5-7 yrs, 8-9 yrs.

Textural variations due to aging observed in 

images are captured in the difference imag
April 3, 2006 Face Recognition Grand Challenge 

Age difference classifier : Ove

First stage of classification

• Create an intra-personal and extra-personal space u

PointFive faces

• Given a pair of face images, compute their differen

its likelihood from each class & classify the image por extra-personal (MAP)

Second stage of classification

• Create age-difference based intra-personal spaces fo

differences (1-2 yrs, 3-4 yrs, 5-7 yrs, 8-9 yrs).

• Estimate likelihood of intra-personal difference ima

classes & classify each pair using a MAP rule.
April 3, 2006 Face Recognition Grand Challenge 

Age Difference Classifier (con

Subspace Density Estimation : Intra PersAssume Gaussian distribution of Intra – personal image 

Marginal density 

in F space

Mcs

Principal subspace and

Its orthogonal complement

for Gaussian density

(Courtesy : Moghaddam 1997)
April 3, 2006 Face Recognition Grand Challenge 

Age Difference Classifier (con

Subspace Density Estimation : Extra per

Assume feature space F to be estimated by a pa

model (mixture of Gaussian – use EM approacparameters)

Assume components of complementary space 

(Courtesy : Moghaddam 1997)
April 3, 2006 Face Recognition Grand Challenge 

Age Difference Classifier (con

Age-difference intra-personal 
April 3, 2006 Face Recognition Grand Challenge 

Age based classification : Resu

• Using 200 pairs of PointFive faces we created an in

and an extra-personal space

• Intra-personal difference images (465 pairs) and ex

difference images were computed from the passpo

First Stage

• 99 % of the intra-personal difference imaof the extra-personal difference images wecorrectly as intrapersonal and extrapersona• The misclassified intrapersonal pairs diffsignificantly due to glasses or due to facial combination of both.
April 3, 2006 Face Recognition Grand Challenge 

Age based classification : Resu(contd) Second Stage

1-2 yrs

3-4 yrs

5-7yrs

8-9 yrs

• Intra-personal image pairs with little variationfacial expressions / glasses / facial hair were mclassified correctly to their age difference categ• Image pairs with significant variations in the were incorrectly classified under 8-9 yrs catego
April 3, 2006 Face Recognition Grand Challenge 

Similarity Measure

Similarity scorepersonal imageage-difference 

Similarity measas the correlatiocomponents co% of the varian
April 3, 2006 Face Recognition Grand Challenge 

Conclusions & Future Work

• The Craniofacial growth model is effective in 

appearances across age (for young adults)

• The Bayesian age-difference classifier can be u

face images across age progression. The lesser to facial hair, facial expressions and glasses on image, the better the success of the age-differ

• A study of facial similarity across time shows 

between age separated face images decreases w

• In future, we wish to develop a model for tex

adult faces across progression. 
April 3, 2006 Face Recognition Grand Challenge 

References :

•Narayanan Ramanathan and Rama Chellappa, "Modeling Age Young Faces" (accepted in CVPR 2006, New York) •Narayanan Ramanathan and Rama Chellappa, "Face VerificatiProgression" (accepted in IEEE Transactions on Image Proce•Narayanan Ramanathan and Rama Chellappa, "Face VerificatiAge Progression", IEEE Computer Vision and Pattern Recogn

vol. 2, pp: 462-469, June 2005, San Diego.Narayanan Ramanathan, Amit K. Roy Chowdhury and Rama Ch"Facial Similarity across Age, Disguise, Illumination and PIEEE International Conference in Image Processing, vol. 3, pp: October 2004, Singapore.