KINSHIP CLASSIFICATION

BY MODELING FACIAL

FEATURE HEREDITY

Ruogu Fang1, Andrew C. Gallagher1

Tsuhan Chen1, Alexander Loui2

1 Cornell University2 Eastman Kodak Company

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IEEE International Conference on Image Processing 2013

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KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY Problem Definition:Recognize the family that a query person belongs to from a set of families. Solution: Reconstruct the query face from a mixture of parts from a set of family members for the recognition.Motivation:Genetic model of reproduction using the mathematical tool of sparsity.

A GENETIC PERSPECTIVE

• Why do we look like the way that we do?– DNA

• How are our appearances affected by ancestors?– Inheritance and mutation

• Facial features are part of the appearance.

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DNA

The facial feature heredity also follow the model of genetics.

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MENDEL’S LAWS I• Law of Random Segregation: For every particular

trait, one randomly selected allele from each parent is passed down to the offspring.

B

b

b b

Bb

B b

B

b

BB Bb

bb

B: Brown eyes (dominant) b: blue eyes (recessive)

bb

bb

Bb

Bb

Each facial feature of an individual can be represented by a sparse

combination of the relatives with this feature.

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FAMILY SPARSITY

?

Few families are selected.

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ENERGY FUNCTION• For one family, given sufficient training samples

of a family (m = feature length, n = number of training samples)

• A new sample from the same family• Approximately lies in the linear span of the family

member samples associated with this family • For all unrelated families,

L1 norm: Individual sparsity term

(illumination, pose and expression)

L2,1 norm: Family sparsity term

N= # families

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MENDEL’S LAWS II

• Law of Independent Assortment: Genes of separate traits are passed down independently from parents to offspring.

Credit: Northeast Medical School

The facial features should be analyzed independently.

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INDEPENDENCE OF FACIAL PARTS

For each part, a part-based dictionary is built.

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CLASSIFICATION

1. Choose three representative parts with smallest possible residues R.

2. Rank the normalized residues for all families on these three parts.

3. Sum the ranks and use the highest rank.

…# families

…# families

…# families

Error

Error

Error

…Reconstruction error for part p from family j

Remove outliers due to

recessive genes

Byproduct: Find the three

most distinguishabl

e features

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POTENTIAL APPLICATIONS

Family Photo Album DistillationFamily Image Retrieval

Social Websites: Auto Family Tagging

Tag Your Family Members

From Sara Lee’s family?

From Kelly Ng’s family?

Find Lost Relatives

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FAMILY 101 DATABASE

• 101 Different Families • 607 Individuals• 14,816 Images

Kennedy

27 (410)

# people

# images

Download: http://tinyurl.com/kinshipclassification

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FAMILY DATABASE COLLECTIONKennedy Family

27 Individuals

48 Images of Caroline Kennedy

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Database # of Family

# of People

# of Images

Highlights References

Labeled Faces in the Wild (LFW)

0 5749 13,233 Unconstrained, “natural” variability in pose, lighting, etc.

[Hung et al. 2007]

PubFig 0 200 58,797 Real world, deep and large, celebrities and politicians

[Kumar et al. 2009]

Cornell Kinship Verification

150 300 300 Controlled parent-child pairs [Fang et al. 2010]

UB KinFace 90 180 270 Child with young parent and old parent faces

[Xia et al. 2011]

Family101 101(206 Nuclear)

607 14,816 Real world, family structure of 2-3 genenrations, variations of age, pose, illumination, expression, ethnicity, etc. Political, royal, wealthy and celebrity families.

This Work

RELATED DATABASES

• Facts about Family101 Database– Multiple generations– Every nuclear family has 6 family members on average– Every individual has 24 images on average

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EXPERIMENT SETUP• Feature: Dense SIFT 16x16• Baseline

– K nearest neighbors (KNN)– Support vector machine (SVM)– Sparse representation based recognition (SRC)

• Unless specified in each scenario: – 3 family members for training, 2 for testing. – 20 families randomly selected for evaluation. – 30 images/person for both training and testing.

• Evaluation metric: Mean per-family accuracy

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EXP 1: NO. OF FAMILIES 3 family members for training2 family members for testing30 images/person for training/testing

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EXP 2: NO. OF PEOPLE FOR TRAINING

20 families randomly selected30 images/person for training/testing

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FACIAL FEATURE MATCHING

• Task: Find the people with similar facial features to the query person.

Training Images

Test Images

Martin SheenHigh

Low

Hair

Eyes

Nose

Mouth

Martin Sheencoefficients

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CONCLUSION

• Motivation: biological process of inheritance– Mendel’s laws of random segregation

and independent assortment

• A new challenge: kinship classification• A new framework: reconstruct the query

face from a mixture of parts from a set of families

• A new dataset: Family 101

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FUTURE WORK

• Use family tree structure

• Hallucination– Hallucinate what the appearance of the father

might be, just by looking at the differences between a child and her mother.

Q & A

Thank you!

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KINSHIP CLASSIFICATION

BY MODELING FACIAL

FEATURE HEREDITY Ruogu Fang Andrew C. Gallagher

Tsuhan Chen Alexander Loui

Project Page and Dataset Download:http://tinyurl.com/kinshipclassification

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FACE DETECTION & ALIGNMENTActive Shape Model: 82 Facial PointsFace DetectionFace Alignment: 6 Fiducial Points