Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

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Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa
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Transcript of Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

Page 1: Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

Face Verification across Age Progression

Narayanan RamanathanDr. Rama Chellappa

Page 2: Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

24th June 2005 Face Verification across Age Progression - CVPR 2005

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Age Progression in Human faces

• Facial aging effects are manifested in different forms in different age groups – shape & texture.

• Both biological and non-biological factors contribute towards facial aging effects.

• Face recognition systems : sensitive to illumination, pose variations, expressions etc.

How does age progression affect the performance of face recognition systems ?

Page 3: Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

24th June 2005 Face Verification across Age Progression - CVPR 2005

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Previous work on Age Progression

• Pittenger & Shaw (1975) :

• Ho Kwon et al. (1994) :

• Alice O’Toole (1997) :

• Tidderman et al. (2001) :

• Lanitis et al. (2002) :

Aging faces as ‘viscal – elastic’

eventsAge classification

from face images – young / old

Age perception using 3D head caricature

Prototyping and transforming facial

texture : Age perceptionToward

automatic age simulation on

faces

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Problem Statement :• Given a pair of age separated face images of an

individual, what is the confidence measure in verifying the identity ?

• How does age progression affect facial similarity ?

Passport Image database 465 pairs of passport images Age range : 20 yrs to 70 yrs Pair-wise age difference

1 - 2 yrs : 165 3 - 4 yrs : 104 5 - 7 yrs : 81 8 - 9 yrs : 115

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PointFive faces

• PointFive face : Better illuminated half of a frontal face (assuming bilateral symmetry of faces)

• We represent frontal faces using PointFive faces (circumvents non-uniform illumination on faces) Mean Intensity Curve

Optimal Mean Intensity Curve

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PointFive faces : Illustration

MIC of left-half

MIC of right-half

Optimal MIC

A face recognition experiment (round-robin fashion) on the PIE dataset gave a 27 % rise in performance while usingPointFive faces

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PointFive faces : Evaluation

Experiments on PIE dataset

Page 8: Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

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Bayesian Age difference classifier : • Given a pair of age separated face

images• Establish identity : intrapersonal /

extrapersonal• Classify intrapersonal age separated

samples based on age difference : 1-2 yrs , 3-4 yrs, 5-7 yrs, 8-9 yrs.We develop a Bayesian age-

difference classifier based on a Probabilistic eigenspaces framework

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Age difference classifier : Overview

First stage of classification• Create an intra-personal and extra-personal space using

differences of PointFive faces• Given a pair of face images, compute their difference

image and estimate its likelihood from each class & classify the image pairs as intra-personal or extra-personal (MAP)

Second stage of classification• Create age-difference based intra-personal spaces for

each of four age-differences (1-2 yrs, 3-4 yrs, 5-7 yrs, 8-9 yrs).

• Estimate likelihood of intra-personal difference images from each of four classes & classify each pair using a MAP rule.

Page 10: Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

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Age Difference Classifier (contd)

Subspace Density Estimation : Intra Personal class

Assume Gaussian distribution of Intra – personal image differences

Marginal density in F space

Marginal density in complementary space

Principal subspace andIts orthogonal complement

for Gaussian density

(Courtesy : Moghaddam 1997)

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Age Difference Classifier (contd)

Subspace Density Estimation : Extra personal class Assume feature space F to be estimated by a

parametric mixture model (mixture of Gaussian – use EM approach to estimate the parameters)

Assume components of complementary space to be Gaussian

(Courtesy : Moghaddam 1997)

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Age Difference Classifier (contd)

if intra-personal

Age-difference basedintra-personal class

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Age based classification : Results

• Using 200 pairs of PointFive faces we created an intra-personal space and an extra-personal space

• Intra-personal difference images (465 pairs) and extra-personal difference images were computed from the passport images.First Stage

• 99 % of the intra-personal difference images and 83 % of the extra-personal difference images were classified correctly as intrapersonal and extrapersonal respectively• The misclassified intrapersonal pairs differed significantly due to glasses or due to facial hair or a combination of both.

Page 14: Face Verification across Age Progression Narayanan Ramanathan Dr. Rama Chellappa.

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Age based classification : Results (contd)Second Stage

• Intra-personal image pairs with little variations due to facial expressions / glasses / facial hair were more often classified correctly to their age difference category.

• Image pairs with significant variations in the above factors were incorrectly classified under 8-9 yrs category.

8-9 yrs

5-7yrs3-4 yrs

1-2 yrs

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Similarity Measure

Similarity scores between intra-personalimages dropped as

age-difference increased

Similarity measure was computed as the correlation of principal components corresponding to 95 % of the variance

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Summary

• A study of facial similarity across time shows that similarity between age separated face images decreases with age.

• The lesser the variations due to facial hair, facial expressions and glasses on age separated face image, the better the success of the age-difference classifier.

• With more training data, the proposed approach can be used in applications such as automated renewal of passport images.

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Thank You