# KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY

## Presentation on theme: "KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY"— Presentation transcript:

KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY
IEEE International Conference on Image Processing 2013 KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY Definition of the new challenge Kinship classification: recognizing the family that a query person belongs to from a set of families. Ruogu Fang1, Andrew C. Gallagher1 Tsuhan Chen1, Alexander Loui2 1 Cornell University 2 Eastman Kodak Company

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. Every face image in in collage represents a family.

The facial feature heredity also follow the model of genetics.
A genetic perspective DNA 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. The story: -computer vision has used many representations for faces. - but there is one ”natural” representation, DNA, -DNA affects every aspect of how our bodies appear (along with environment) -In fact, you can go from DNA to appearance... -Therefore, we would like to model the process of genetics in our kinship work. We do this with sparsity. Facial features are part of the appearance The facial feature heredity also follow the model of genetics.

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: Brown eyes (dominant) b: blue eyes (recessive) b b Each facial feature of an individual can be represented by a sparse combination of the relatives with this feature. B b Bb Bb BB Bb B b B b Sparse selection: For each gene, a child receives a pair of alleles, one allele from each parent. The visible appearance (phenotype) of the individual depends on which alleles are dominant and which are recessive. This process is a sparse process: for various genes, the displayed phenotype for each gene is a sparse selection from among the parents. Explain what are dominant and recessive genes Every trait comes from a relative in the family bb bb Bb bb

? family sparsity Few families are selected.
Few people from few families are selected. ?

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, L2,1 norm: Family sparsity term N= # families L21 norm: few families involved in the reconstruction L1 norm: selecting only a few images with similar illumination, pose and expression. Few people in few families are selected. Latex: D_j=[d_{j,1},d_{j,2},\ldots,d_{j,n}]\in R^{m\times n} y\approx D_j\alpha L1 norm: Individual sparsity term (illumination, pose and expression)

The facial features should be analyzed independently.
Mendel’s Laws II Law of Independent Assortment: Genes of separate traits are passed down independently from parents to offspring. The facial features should be analyzed independently. Credit: Northeast Medical School

Independence of facial parts
For each part, a part-based dictionary is built. We segment the facial image into 12 parts. The location and the size of the 12 facial parts are determined from the annotation of one researcher on a template face and applied to all the other facial images which have been aligned to the same template.

classification Reconstruction error for part p from family j # families Error Remove outliers due to recessive genes Choose three representative parts with smallest possible residues R. Rank the normalized residues for all families on these three parts. Sum the ranks and use the highest rank. Error Byproduct: Find the three most distinguishable features # families It is a heuristic method for the classification. Latex R_j^{(p)}=\|y^{(p)}-D_{G_j}^{(p)}\alpha_j^{(p)}\|_2^2 Error # families

Potential applications
Family Image Retrieval Family Photo Album Distillation Tag Your Family Members From Sara Lee’s family? From Kelly Ng’s family? Social Websites: Auto Family Tagging Find Lost Relatives

Family 101 database 607 Individuals 14,816 Images
101 Different Families 607 Individuals 14,816 Images Download: Kennedy 27 (410) political, royal, wealthy and celebrity families # people # images

family database Collection
Kennedy Family 27 Individuals Caroline Kennedy 48 Images of Caroline Kennedy

Related databases Facts about Family101 Database Multiple generations
# of Family # of People # of Images Highlights References Labeled Faces in the Wild (LFW) 5749 13,233 Unconstrained, “natural” variability in pose, lighting, etc. [Hung et al. 2007] PubFig 200 58,797 Real world, deep and large, celebrities and politicians [Kumar et al. 2009] Cornell Kinship Verification 150 300 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 Facts about Family101 Database Multiple generations Every nuclear family has 6 family members on average Every individual has 24 images on average

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

Exp 1: No. of families 3 family members for training
2 family members for testing 30 images/person for training/testing

Exp 2: No. of people for training
20 families randomly selected 30 images/person for training/testing

Facial Feature Matching
Task: Find the people with similar facial features to the query person. Martin Sheen High Low Hair Eyes Nose Mouth coefficients Martin Sheen Decide the family. Pick up the top three images in the part-based dictionary D^(p) with the largest coefficients. These images are regarded as the images of family relatives who have similar facial traits. Test Images Training Images

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

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. Hallucination: consider a child with her mother. Facial parts that are in common are explained by the mother’s passing of her alleles for those traits. For facial parts that are different, we expect that it is likely that those traits were inherited from the father.

Q & A KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY
Ruogu Fang Andrew C. Gallagher Q & A Thank you! Project Page and Dataset Download: Tsuhan Chen Alexander Loui

Face detection & alignment
Face Alignment: 6 Fiducial Points Active Shape Model: 82 Facial Points Face Detection

Download ppt "KINSHIP CLASSIFICATION BY MODELING FACIAL FEATURE HEREDITY"

Similar presentations